├── download.sh
├── Uground
    ├── scripts
    │   ├── preprocess
    │   │   ├── __pycache__
    │   │   │   ├── params.cpython-310.pyc
    │   │   │   ├── __init__.cpython-310.pyc
    │   │   │   ├── constants.cpython-310.pyc
    │   │   │   ├── data_process.cpython-310.pyc
    │   │   │   ├── patch_image.cpython-310.pyc
    │   │   │   └── point_extract.cpython-310.pyc
    │   │   ├── constants.py
    │   │   ├── point_extract.py
    │   │   ├── patch_image.py
    │   │   ├── params.py
    │   │   └── data_process.py
    │   ├── untarget.py
    │   ├── target.py
    │   ├── infer_screenspot.py
    │   └── check_attack.py
    ├── infer.sh
    ├── untarget.sh
    ├── check.sh
    └── target.sh
├── OS-Atlas-7B
    ├── scripts
    │   ├── preprocess
    │   │   ├── __pycache__
    │   │   │   ├── params.cpython-310.pyc
    │   │   │   ├── constants.cpython-310.pyc
    │   │   │   ├── data_process.cpython-310.pyc
    │   │   │   ├── patch_image.cpython-310.pyc
    │   │   │   └── point_extract.cpython-310.pyc
    │   │   ├── constants.py
    │   │   ├── point_extract.py
    │   │   ├── patch_image.py
    │   │   ├── params.py
    │   │   └── data_process.py
    │   ├── target.py
    │   ├── untarget.py
    │   ├── infer_screenspot.py
    │   └── check_attack.py
    ├── target.sh
    ├── untarget.sh
    ├── infer.sh
    └── check.sh
├── requirements.txt
├── generation_config.json
├── .gitignore
├── preprocessor_config.json
├── create_noisy.py
├── README.md
└── datas
    └── screenspot_desktop_v2.json


/download.sh:
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1 | cd datas
2 | wget https://huggingface.co/datasets/OS-Copilot/ScreenSpot-v2/resolve/main/screenspotv2_image.zip
3 | unzip screenspotv2_image.zip


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/Uground/scripts/preprocess/__pycache__/params.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/Uground/scripts/preprocess/__pycache__/params.cpython-310.pyc


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/Uground/scripts/preprocess/__pycache__/__init__.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/Uground/scripts/preprocess/__pycache__/__init__.cpython-310.pyc


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/Uground/scripts/preprocess/__pycache__/constants.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/Uground/scripts/preprocess/__pycache__/constants.cpython-310.pyc


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/OS-Atlas-7B/scripts/preprocess/__pycache__/params.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/OS-Atlas-7B/scripts/preprocess/__pycache__/params.cpython-310.pyc


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/Uground/scripts/preprocess/__pycache__/data_process.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/Uground/scripts/preprocess/__pycache__/data_process.cpython-310.pyc


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/Uground/scripts/preprocess/__pycache__/patch_image.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/Uground/scripts/preprocess/__pycache__/patch_image.cpython-310.pyc


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/OS-Atlas-7B/scripts/preprocess/__pycache__/constants.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/OS-Atlas-7B/scripts/preprocess/__pycache__/constants.cpython-310.pyc


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/Uground/scripts/preprocess/__pycache__/point_extract.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/Uground/scripts/preprocess/__pycache__/point_extract.cpython-310.pyc


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/OS-Atlas-7B/scripts/preprocess/__pycache__/data_process.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/OS-Atlas-7B/scripts/preprocess/__pycache__/data_process.cpython-310.pyc


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/OS-Atlas-7B/scripts/preprocess/__pycache__/patch_image.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/OS-Atlas-7B/scripts/preprocess/__pycache__/patch_image.cpython-310.pyc


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/OS-Atlas-7B/scripts/preprocess/__pycache__/point_extract.cpython-310.pyc:
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https://raw.githubusercontent.com/ZZZhr-1/Robust_GUI_Grounding/HEAD/OS-Atlas-7B/scripts/preprocess/__pycache__/point_extract.cpython-310.pyc


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/requirements.txt:
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 1 | transformers==4.45.1
 2 | accelerate
 3 | tiktoken
 4 | einops
 5 | transformers_stream_generator
 6 | scipy
 7 | torchvision
 8 | pillow
 9 | tensorboard
10 | matplotlib
11 | qwen-vl-utils


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/generation_config.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "bos_token_id": 151643,
 3 |   "do_sample": true,
 4 |   "eos_token_id": [
 5 |     151645,
 6 |     151643
 7 |   ],
 8 |   "pad_token_id": 151643,
 9 |   "temperature": 0.000001,
10 |   "top_k": 1,
11 |   "top_p": 0.001,
12 |   "transformers_version": "4.45.0.dev0"
13 | }


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/.gitignore:
--------------------------------------------------------------------------------
 1 | datas/screenspotv2_image.zip
 2 | datas/screenspotv2_image
 3 | datas/noisy_images
 4 | datas/noisy_images_s
 5 | outputs
 6 | OS-Atlas-7B/os_check_result.txt
 7 | OS-Atlas-7B/OS_infer_result.txt
 8 | OS-Atlas-7B/one_step.sh
 9 | 
10 | Uground/Uground_check_result.txt
11 | Uground/Uground_infer_result.txt
12 | Uground/one_step.sh
13 | Uground/patch.sh
14 | Uground/scripts/patch_attack.py
15 | 
16 | Uground-2B/


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/Uground/scripts/preprocess/constants.py:
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 1 | IGNORE_INDEX = -100
 2 | 
 3 | DEFAULT_IM_START_TOKEN = "<|im_start|>"
 4 | DEFAULT_IM_END_TOKEN = "<|im_end|>"
 5 | DEFAULT_IMAGE_TOKEN = "<|image_pad|>"
 6 | DEFAULT_VIDEO_TOKEN = "<|video_pad|>"
 7 | LLAVA_IMAGE_TOKEN = "<image>"
 8 | LLAVA_VIDEO_TOKEN = "<video>"
 9 | VISION_START_TOKEN = "<|vision_start|>"
10 | VISION_END_TOKEN = "<|vision_end|>"
11 | 
12 | SYSTEM_MESSAGE = "You are a helpful assistant."


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/OS-Atlas-7B/scripts/preprocess/constants.py:
--------------------------------------------------------------------------------
 1 | IGNORE_INDEX = -100
 2 | 
 3 | DEFAULT_IM_START_TOKEN = "<|im_start|>"
 4 | DEFAULT_IM_END_TOKEN = "<|im_end|>"
 5 | DEFAULT_IMAGE_TOKEN = "<|image_pad|>"
 6 | DEFAULT_VIDEO_TOKEN = "<|video_pad|>"
 7 | LLAVA_IMAGE_TOKEN = "<image>"
 8 | LLAVA_VIDEO_TOKEN = "<video>"
 9 | VISION_START_TOKEN = "<|vision_start|>"
10 | VISION_END_TOKEN = "<|vision_end|>"
11 | 
12 | SYSTEM_MESSAGE = "You are a helpful assistant."


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/Uground/infer.sh:
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 1 | # python scripts/infer_screenspot.py \
 2 | #     --model_path osunlp/UGround-V1-7B \
 3 | #     --screenspot_imgs ../datas/screenspotv2_image \
 4 | #     --screenspot_test ../datas \
 5 | #     --task all \
 6 | #     --max_pixels 1024
 7 | 
 8 | # CUDA_VISIBLE_DEVICES=2 python scripts/infer_screenspot.py \
 9 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
10 | #     --screenspot_imgs ../datas/screenspotv2_image \
11 | #     --screenspot_test ../datas \
12 | #     --task all \
13 | #     --max_pixels 256
14 | 


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/preprocessor_config.json:
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 1 | {
 2 |   "do_convert_rgb": true,
 3 |   "do_normalize": true,
 4 |   "do_rescale": true,
 5 |   "do_resize": true,
 6 |   "image_mean": [
 7 |     0.48145466,
 8 |     0.4578275,
 9 |     0.40821073
10 |   ],
11 |   "image_processor_type": "Qwen2VLImageProcessor",
12 |   "image_std": [
13 |     0.26862954,
14 |     0.26130258,
15 |     0.27577711
16 |   ],
17 |   "max_pixels": 1048576,
18 |   "merge_size": 2,
19 |   "min_pixels": 3136,
20 |   "patch_size": 14,
21 |   "processor_class": "Qwen2VLProcessor",
22 |   "resample": 3,
23 |   "rescale_factor": 0.00392156862745098,
24 |   "size": {
25 |     "max_pixels": 1048576,
26 |     "min_pixels": 3136
27 |   },
28 |   "temporal_patch_size": 2
29 | }
30 | 


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/Uground/scripts/preprocess/point_extract.py:
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 1 | import re
 2 | 
 3 | def pred_2_point(s):
 4 |     floats = re.findall(r'-?\d+\.?\d*', s)
 5 |     floats = [float(num) for num in floats]
 6 |     if len(floats) == 2:
 7 |         click_point = floats
 8 |     elif len(floats) == 4:
 9 |         click_point = [(floats[0]+floats[2])/2, (floats[1]+floats[3])/2]
10 |     return click_point
11 | 
12 | # bbox (qwen str) -> bbox
13 | def extract_bbox(s):
14 |     # Regular expression to find the content inside <box> and </box>
15 |     pattern = r"\((\d+,\d+)\),\((\d+,\d+)\)"
16 |     matches = re.findall(pattern, s)
17 |     # Convert the tuples of strings into tuples of integers
18 |     return [(int(x.split(',')[0]), int(x.split(',')[1])) for x in sum(matches, ())]


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/OS-Atlas-7B/scripts/preprocess/point_extract.py:
--------------------------------------------------------------------------------
 1 | import re
 2 | 
 3 | def pred_2_point(s):
 4 |     floats = re.findall(r'-?\d+\.?\d*', s)
 5 |     floats = [float(num) for num in floats]
 6 |     if len(floats) == 2:
 7 |         click_point = floats
 8 |     elif len(floats) == 4:
 9 |         click_point = [(floats[0]+floats[2])/2, (floats[1]+floats[3])/2]
10 |     return click_point
11 | 
12 | # bbox (qwen str) -> bbox
13 | def extract_bbox(s):
14 |     # Regular expression to find the content inside <box> and </box>
15 |     pattern = r"\((\d+,\d+)\),\((\d+,\d+)\)"
16 |     matches = re.findall(pattern, s)
17 |     # Convert the tuples of strings into tuples of integers
18 |     return [(int(x.split(',')[0]), int(x.split(',')[1])) for x in sum(matches, ())]


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/Uground/untarget.sh:
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 1 | # python scripts/untarget.py \
 2 | #     --model_path osunlp/UGround-V1-7B \
 3 | #     --screenspot_imgs ../datas/screenspotv2_image \
 4 | #     --screenspot_test ../datas/screenspotv2_web_ug_target.json \
 5 | #     --output_path ../outputs/ug/target\
 6 | #     --max_pixels 1024
 7 | 
 8 | CUDA_VISIBLE_DEVICES=3 python scripts/untarget.py \
 9 |     --model_path /data/home/zhr/models/UGround-V1-7B \
10 |     --screenspot_imgs ../datas/screenspotv2_image \
11 |     --screenspot_test ../datas/screenspotv2_mobile_ug_target.json \
12 |     --output_path ../outputs/ug/untarget/256\
13 |     --max_pixels 256
14 | 
15 | CUDA_VISIBLE_DEVICES=3 python scripts/untarget.py \
16 |     --model_path /data/home/zhr/models/UGround-V1-7B \
17 |     --screenspot_imgs ../datas/screenspotv2_image \
18 |     --screenspot_test ../datas/screenspotv2_desktop_ug_target.json \
19 |     --output_path ../outputs/ug/untarget/256\
20 |     --max_pixels 256
21 | 
22 | CUDA_VISIBLE_DEVICES=3 python scripts/untarget.py \
23 |     --model_path /data/home/zhr/models/UGround-V1-7B \
24 |     --screenspot_imgs ../datas/screenspotv2_image \
25 |     --screenspot_test ../datas/screenspotv2_web_ug_target.json \
26 |     --output_path ../outputs/ug/untarget/256\
27 |     --max_pixels 256


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/OS-Atlas-7B/target.sh:
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 1 | # python scripts/target.py \
 2 | #     --model_path OS-Copilot/OS-Atlas-Base-7B \
 3 | #     --screenspot_imgs ../datas/screenspotv2_image \
 4 | #     --screenspot_test ../datas/screenspot_web_os_target.json \
 5 | #     --output_path ../outputs/os/target\
 6 | #     --max_pixels 1024
 7 | 
 8 | # CUDA_VISIBLE_DEVICES=2 python scripts/target.py \
 9 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
10 | #     --screenspot_imgs ../datas/screenspotv2_image \
11 | #     --screenspot_test ../datas/screenspot_web_os_target.json \
12 | #     --output_path ../outputs/os/target/256\
13 | #     --max_pixels 256
14 | 
15 | 
16 | # CUDA_VISIBLE_DEVICES=2 python scripts/target.py \
17 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
18 | #     --screenspot_imgs ../datas/screenspotv2_image \
19 | #     --screenspot_test ../datas/screenspot_mobile_os_target.json \
20 | #     --output_path ../outputs/os/target/256\
21 | #     --max_pixels 256
22 | 
23 | # CUDA_VISIBLE_DEVICES=2 python scripts/target.py \
24 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
25 | #     --screenspot_imgs ../datas/screenspotv2_image \
26 | #     --screenspot_test ../datas/screenspot_desktop_os_target.json \
27 | #     --output_path ../outputs/os/target/256\
28 | #     --max_pixels 256
29 | 


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/OS-Atlas-7B/untarget.sh:
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 1 | # python scripts/untarget.py \
 2 | #     --model_path OS-Copilot/OS-Atlas-Base-7B \
 3 | #     --screenspot_imgs ../datas/screenspotv2_image \
 4 | #     --screenspot_test ../datas/screenspot_web_os_target.json \
 5 | #     --output_path ../outputs/os/untarget\
 6 | #     --max_pixels 1024
 7 | 
 8 | # CUDA_VISIBLE_DEVICES=2 python scripts/untarget.py \
 9 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
10 | #     --screenspot_imgs ../datas/screenspotv2_image \
11 | #     --screenspot_test ../datas/screenspot_mobile_os_target.json \
12 | #     --output_path ../outputs/os/untarget/256\
13 | #     --max_pixels 256
14 | 
15 | # CUDA_VISIBLE_DEVICES=2 python scripts/untarget.py \
16 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
17 | #     --screenspot_imgs ../datas/screenspotv2_image \
18 | #     --screenspot_test ../datas/screenspot_desktop_os_target.json \
19 | #     --output_path ../outputs/os/untarget/256\
20 | #     --max_pixels 256
21 | 
22 | # CUDA_VISIBLE_DEVICES=2 python scripts/untarget.py \
23 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
24 | #     --screenspot_imgs ../datas/screenspotv2_image \
25 | #     --screenspot_test ../datas/screenspot_web_os_target.json \
26 | #     --output_path ../outputs/os/untarget/256\
27 | #     --max_pixels 256


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/OS-Atlas-7B/infer.sh:
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 1 | # python scripts/infer_screenspot.py \
 2 | #     --model_path OS-Copilot/OS-Atlas-Base-7B \
 3 | #     --screenspot_imgs ../datas/screenspotv2_image \
 4 | #     --screenspot_test ../datas \
 5 | #     --task all \
 6 | #     --max_pixels 1024
 7 | 
 8 | # export CUDA_VISIBLE_DEVICES=2
 9 | # python scripts/infer_screenspot.py \
10 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
11 | #     --screenspot_imgs /data/home/zhr/Robust_GUI_Grounding/datas/noisy_images/color_jitter \
12 | #     --screenspot_test ../datas \
13 | #     --task all \
14 | #     --max_pixels 1024
15 | 
16 | # python scripts/infer_screenspot.py \
17 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
18 | #     --screenspot_imgs /data/home/zhr/Robust_GUI_Grounding/datas/noisy_images_s/contrast_adjusted \
19 | #     --screenspot_test ../datas \
20 | #     --task all \
21 | #     --max_pixels 1024
22 | 
23 | # python scripts/infer_screenspot.py \
24 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
25 | #     --screenspot_imgs /data/home/zhr/Robust_GUI_Grounding/datas/noisy_images_s/gaussian_blur \
26 | #     --screenspot_test ../datas \
27 | #     --task all \
28 | #     --max_pixels 1024
29 | 
30 | # python scripts/infer_screenspot.py \
31 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
32 | #     --screenspot_imgs /data/home/zhr/Robust_GUI_Grounding/datas/noisy_images_s/gaussian_noise \
33 | #     --screenspot_test ../datas \
34 | #     --task all \
35 | #     --max_pixels 1024
36 | 


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/OS-Atlas-7B/check.sh:
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 1 | # python scripts/check_attack.py \
 2 | #     --model_path OS-Copilot/OS-Atlas-Base-7B \
 3 | #     --screenspot_imgs ../output/ \
 4 | #     --screenspot_test ../datas \
 5 | #     --task all \
 6 | #     --max_pixels 1024
 7 | 
 8 | 
 9 | # CUDA_VISIBLE_DEVICES=2 python scripts/check_attack.py \
10 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
11 | #     --screenspot_imgs ../outputs/os/target/256 \
12 | #     --screenspot_test ../datas \
13 | #     --task web \
14 | #     --max_pixels 256
15 | # [[0.08974358974358974, 0.10344827586206896]]
16 | # [[0.029914529914529916, 0.034482758620689655]]
17 | 
18 | # CUDA_VISIBLE_DEVICES=2 python scripts/check_attack.py \
19 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
20 | #     --screenspot_imgs ../outputs/os/target/256 \
21 | #     --screenspot_test ../datas \
22 | #     --task all \
23 | #     --max_pixels 256
24 | # [[0.3724137931034483, 0.11848341232227488], [0.10824742268041238, 0.05], [0.08974358974358974, 0.10344827586206896]]
25 | # [[0.020689655172413793, 0.04265402843601896], [0.020618556701030927, 0.07142857142857142], [0.029914529914529916, 0.034482758620689655]]
26 | 
27 | # CUDA_VISIBLE_DEVICES=2 python scripts/check_attack.py \
28 | #     --model_path /data/home/zhr/models/OS-Atlas-Base-7B \
29 | #     --screenspot_imgs ../outputs/os/untarget/256 \
30 | #     --screenspot_test ../datas \
31 | #     --task all \
32 | #     --max_pixels 256
33 | # [[0.4379310344827586, 0.16113744075829384], [0.15979381443298968, 0.1], [0.07692307692307693, 0.11822660098522167]]
34 | # [[0.010344827586206896, 0.004739336492890996], [0.0, 0.0], [0.0, 0.0]]


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/Uground/check.sh:
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 1 | # python Uground/check_attack.py \
 2 | #     --model_path osunlp/UGround-V1-7B \
 3 | #     --screenspot_imgs output \
 4 | #     --screenspot_test datas \
 5 | #     --task all \
 6 | #     --max_pixels 1024
 7 | 
 8 | # CUDA_VISIBLE_DEVICES=0 python scripts/check_attack.py \
 9 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
10 | #     --screenspot_imgs ../outputs/ug/untarget \
11 | #     --screenspot_test ../datas \
12 | #     --task web \
13 | #     --max_pixels 1024
14 | # [[0.6495726495726496, 0.46798029556650245]] web untarget
15 | # [[0.0, 0.0]]
16 | 
17 | # CUDA_VISIBLE_DEVICES=0 python scripts/check_attack.py \
18 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
19 | #     --screenspot_imgs ../outputs/ug/untarget \
20 | #     --screenspot_test ../datas \
21 | #     --task desktop \
22 | #     --max_pixels 1024
23 | # [[0.5154639175257731, 0.2571428571428571]]
24 | # [[0.0, 0.0]]
25 | 
26 | # CUDA_VISIBLE_DEVICES=3 python scripts/check_attack.py \
27 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
28 | #     --screenspot_imgs ../outputs/ug/untarget \
29 | #     --screenspot_test ../datas \
30 | #     --task mobile \
31 | #     --max_pixels 1024
32 | # [[0.7448275862068966, 0.46919431279620855]]
33 | # [[0.0, 0.0]]
34 | 
35 | # CUDA_VISIBLE_DEVICES=0 python scripts/check_attack.py \
36 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
37 | #     --screenspot_imgs ../outputs/ug/target/256 \
38 | #     --screenspot_test ../datas \
39 | #     --task web \
40 | #     --max_pixels 256
41 | # [[0.2264957264957265, 0.1477832512315271]]
42 | # [[0.20085470085470086, 0.30049261083743845]]
43 | 


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/Uground/scripts/preprocess/patch_image.py:
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 1 | def restore_images(flatten_patches, grid_t, grid_h, grid_w, merge_size, temporal_patch_size, patch_size, channel, data_format):
 2 |     grid_h_re = grid_h // merge_size
 3 |     grid_w_re = grid_w // merge_size
 4 | 
 5 |     restored = flatten_patches.reshape(
 6 |         grid_t,
 7 |         grid_h_re,
 8 |         grid_w_re,
 9 |         merge_size,
10 |         merge_size,
11 |         channel,
12 |         temporal_patch_size,
13 |         patch_size,
14 |         patch_size
15 |     )
16 | 
17 |     restored = restored.permute(0, 6, 5, 1, 3, 7, 2, 4, 8)
18 | 
19 |     total_time = grid_t * temporal_patch_size
20 |     height = grid_h_re * merge_size * patch_size
21 |     width = grid_w_re * merge_size * patch_size
22 | 
23 |     restored = restored.reshape(total_time, channel, height, width)
24 | 
25 |     if data_format == "channels_last":
26 |         restored = restored.transpose(0, 2, 3, 1)
27 | 
28 |     return restored
29 | 
30 | def pixel_reshape(image, patch_size, merge_size, temporal_patch_size):
31 |     
32 |     if image.dim() == 3:
33 |         patches = image.unsqueeze(0)
34 |     else:
35 |         patches = image
36 |     if patches.shape[0] == 1:
37 |         patches = patches.repeat(temporal_patch_size, 1, 1, 1)
38 | 
39 |     channel = patches.shape[1]
40 |     resized_height = patches.shape[2]
41 |     resized_width = patches.shape[3]
42 |     grid_t = patches.shape[0] // temporal_patch_size
43 |     grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
44 | 
45 |     patches = patches.view(
46 |         grid_t,
47 |         temporal_patch_size,
48 |         channel,
49 |         grid_h // merge_size, merge_size, patch_size,
50 |         grid_w // merge_size, merge_size, patch_size,
51 |     )
52 | 
53 |     patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8)
54 |     flatten_patches = patches.reshape(
55 |         grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size
56 |     )
57 |     return flatten_patches, (grid_t, grid_h, grid_w)
58 | 


--------------------------------------------------------------------------------
/OS-Atlas-7B/scripts/preprocess/patch_image.py:
--------------------------------------------------------------------------------
 1 | def restore_images(flatten_patches, grid_t, grid_h, grid_w, merge_size, temporal_patch_size, patch_size, channel, data_format):
 2 |     grid_h_re = grid_h // merge_size
 3 |     grid_w_re = grid_w // merge_size
 4 | 
 5 |     restored = flatten_patches.reshape(
 6 |         grid_t,
 7 |         grid_h_re,
 8 |         grid_w_re,
 9 |         merge_size,
10 |         merge_size,
11 |         channel,
12 |         temporal_patch_size,
13 |         patch_size,
14 |         patch_size
15 |     )
16 | 
17 |     restored = restored.permute(0, 6, 5, 1, 3, 7, 2, 4, 8)
18 | 
19 |     total_time = grid_t * temporal_patch_size
20 |     height = grid_h_re * merge_size * patch_size
21 |     width = grid_w_re * merge_size * patch_size
22 | 
23 |     restored = restored.reshape(total_time, channel, height, width)
24 | 
25 |     if data_format == "channels_last":
26 |         restored = restored.transpose(0, 2, 3, 1)
27 | 
28 |     return restored
29 | 
30 | def pixel_reshape(image, patch_size, merge_size, temporal_patch_size):
31 |     
32 |     if image.dim() == 3:
33 |         patches = image.unsqueeze(0)
34 |     else:
35 |         patches = image
36 |     if patches.shape[0] == 1:
37 |         patches = patches.repeat(temporal_patch_size, 1, 1, 1)
38 | 
39 |     channel = patches.shape[1]
40 |     resized_height = patches.shape[2]
41 |     resized_width = patches.shape[3]
42 |     grid_t = patches.shape[0] // temporal_patch_size
43 |     grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
44 | 
45 |     patches = patches.view(
46 |         grid_t,
47 |         temporal_patch_size,
48 |         channel,
49 |         grid_h // merge_size, merge_size, patch_size,
50 |         grid_w // merge_size, merge_size, patch_size,
51 |     )
52 | 
53 |     patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8)
54 |     flatten_patches = patches.reshape(
55 |         grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size
56 |     )
57 |     return flatten_patches, (grid_t, grid_h, grid_w)
58 | 


--------------------------------------------------------------------------------
/Uground/target.sh:
--------------------------------------------------------------------------------
 1 | # CUDA_VISIBLE_DEVICES=3 python scripts/target.py \
 2 | #     --model_path osunlp/UGround-V1-7B \
 3 | #     --screenspot_imgs ../datas/screenspotv2_image \
 4 | #     --screenspot_test ../datas/screenspotv2_web_ug_target.json \
 5 | #     --output_path ../outputs/ug/target\
 6 | #     --max_pixels 1024
 7 | 
 8 | # CUDA_VISIBLE_DEVICES=3 python scripts/target.py \
 9 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
10 | #     --screenspot_imgs ../datas/screenspotv2_image \
11 | #     --screenspot_test ../datas/screenspotv2_web_ug_target.json \
12 | #     --output_path ../outputs/ug/target/256\
13 | #     --max_pixels 256
14 | # [[0.2264957264957265, 0.1477832512315271]]
15 | # [[0.20085470085470086, 0.30049261083743845]]
16 | 
17 | # CUDA_VISIBLE_DEVICES=3 python scripts/target.py \
18 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
19 | #     --screenspot_imgs ../datas/screenspotv2_image \
20 | #     --screenspot_test ../datas/screenspotv2_mobile_ug_target.json \
21 | #     --output_path ../outputs/ug/target/256\
22 | #     --max_pixels 256
23 | 
24 | # CUDA_VISIBLE_DEVICES=3 python scripts/target.py \
25 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
26 | #     --screenspot_imgs ../datas/screenspotv2_image \
27 | #     --screenspot_test ../datas/screenspotv2_desktop_ug_target.json \
28 | #     --output_path ../outputs/ug/target/256\
29 | #     --max_pixels 256
30 | 
31 | # CUDA_VISIBLE_DEVICES=3 python scripts/untarget.py \
32 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
33 | #     --screenspot_imgs ../datas/screenspotv2_image \
34 | #     --screenspot_test ../datas/screenspotv2_mobile_ug_target.json \
35 | #     --output_path ../outputs/ug/untarget/256\
36 | #     --max_pixels 256
37 | 
38 | # CUDA_VISIBLE_DEVICES=3 python scripts/untarget.py \
39 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
40 | #     --screenspot_imgs ../datas/screenspotv2_image \
41 | #     --screenspot_test ../datas/screenspotv2_desktop_ug_target.json \
42 | #     --output_path ../outputs/ug/untarget/256\
43 | #     --max_pixels 256
44 | 
45 | # CUDA_VISIBLE_DEVICES=3 python scripts/untarget.py \
46 | #     --model_path /data/home/zhr/models/UGround-V1-7B \
47 | #     --screenspot_imgs ../datas/screenspotv2_image \
48 | #     --screenspot_test ../datas/screenspotv2_web_ug_target.json \
49 | #     --output_path ../outputs/ug/untarget/256\
50 | #     --max_pixels 256


--------------------------------------------------------------------------------
/create_noisy.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | import numpy as np
 3 | import cv2
 4 | import torchvision.transforms as transforms
 5 | from PIL import Image, ImageEnhance
 6 | from tqdm import tqdm  
 7 | 
 8 | input_folder = ""
 9 | output_folder = ""
10 | 
11 | effects = {
12 |     "gaussian_noise": "gaussian_noise",
13 |     "gaussian_blur": "gaussian_blur",
14 |     "color_jitter": "color_jitter",
15 |     "contrast_adjusted": "contrast_adjusted"
16 | }
17 | 
18 | 
19 | for effect in effects.values():
20 |     os.makedirs(os.path.join(output_folder, effect), exist_ok=True)
21 | 
22 | 
23 | 
24 | 
25 | def add_gaussian_noise(image, mean=0, std=30):
26 |     image_np = np.array(image, dtype=np.float32)
27 |     noise = np.random.normal(mean, std, image_np.shape)
28 |     noisy_image = np.clip(image_np + noise, 0, 255).astype(np.uint8)
29 |     return Image.fromarray(noisy_image)
30 | 
31 | def apply_gaussian_blur(image, kernel_size=9):
32 |     image_np = np.array(image)
33 |     blurred_image = cv2.GaussianBlur(image_np, (kernel_size, kernel_size), 0)
34 |     return Image.fromarray(blurred_image)
35 | 
36 | def apply_color_jitter(image, brightness=0.5, contrast=0.5, saturation=0.5):
37 |     color_jitter = transforms.ColorJitter(brightness=brightness, contrast=contrast, saturation=saturation)
38 |     return color_jitter(image)
39 | 
40 | def random_shift(image, relative_shift_x=0.1, relative_shift_y=0.1):
41 |     transform = transforms.RandomAffine(degrees=0, translate=(relative_shift_x, relative_shift_y))
42 |     return transform(image)
43 | 
44 | def adjust_contrast(image, factor=0.5):
45 |     enhancer = ImageEnhance.Contrast(image)
46 |     return enhancer.enhance(factor)
47 | 
48 | 
49 | for filename in tqdm(os.listdir(input_folder)):
50 |     if filename.lower().endswith((".png", ".jpg", ".jpeg")):
51 |         image_path = os.path.join(input_folder, filename)
52 |         image = Image.open(image_path).convert("RGB")
53 | 
54 | 
55 |         noisy_image = add_gaussian_noise(image)
56 |         blurred_image = apply_gaussian_blur(image)
57 |         color_jitter_image = apply_color_jitter(image)
58 |         contrast_image = adjust_contrast(image)
59 | 
60 | 
61 |         noisy_image.save(os.path.join(output_folder, "gaussian_noise", filename))
62 |         blurred_image.save(os.path.join(output_folder, "gaussian_blur", filename))
63 |         color_jitter_image.save(os.path.join(output_folder, "color_jitter", filename))
64 |         contrast_image.save(os.path.join(output_folder, "contrast_adjusted", filename))
65 | 


--------------------------------------------------------------------------------
/Uground/scripts/preprocess/params.py:
--------------------------------------------------------------------------------
 1 | from dataclasses import dataclass, field
 2 | from typing import Optional
 3 | 
 4 | from transformers import TrainingArguments
 5 | 
 6 | 
 7 | @dataclass
 8 | class ModelArguments:
 9 |     model_id: Optional[str] = field(default="Qwen/Qwen2-VL-7B-Instruct")
10 | 
11 | 
12 | @dataclass
13 | class TrainingArguments(TrainingArguments):
14 |     cache_dir: Optional[str] = field(default=None)
15 |     optim: str = field(default="adamw_torch")
16 |     adam_beta1: float = field(default=0.9)
17 |     adam_beta2: float = field(default=0.999)
18 |     adam_epsilon: float = field(default=1e-8)
19 | 
20 |     freeze_vision_tower: bool = field(default=False)
21 |     freeze_llm: bool = field(default=False)
22 |     tune_merger: bool = field(default=False)
23 |     disable_flash_attn2: bool = field(default=False)
24 | 
25 |     max_seq_length: int = field(
26 |         default=32768, # This is the default value of the qwen2-vl model
27 |         metadata={
28 |             "help":
29 |                 "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
30 |         },
31 |     )
32 | 
33 |     double_quant: bool = field(
34 |         default=True,
35 |         metadata={"help": "Compress the quantization statistics through double quantization."}
36 |     )
37 |     quant_type: str = field(
38 |         default="nf4",
39 |         metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
40 |     )
41 |     bits: int = field(
42 |         default=16,
43 |         metadata={"help": "How many bits to use."}
44 |     )
45 |     lora_enable: bool = False
46 |     vision_lora: bool = False
47 |     use_dora: bool = False
48 |     lora_rank: int = 64
49 |     lora_alpha: int = 16
50 |     lora_dropout: float = 0.05
51 |     lora_weight_path: str = ""
52 |     lora_bias: str = "none"
53 |     vision_lr: Optional[float] = None
54 |     merger_lr: Optional[float] = None
55 |     lora_namespan_exclude: str = field(default=None, metadata={"help": "List of namespan to exclude for LoRA"})
56 |     num_lora_modules: int = -1
57 | 
58 | 
59 | @dataclass
60 | class DataArguments:
61 |     data_path: str = field(
62 |         default=None, metadata={"help": "Path to the training data."}
63 |     )
64 |     lazy_preprocess: bool = False
65 |     image_folder: Optional[str] = field(default=None)
66 |     image_min_pixels: Optional[int] = field(default=3136)
67 |     image_max_pixels: Optional[int] = field(default=12845056)
68 |     video_min_pixels: Optional[int] = field(default=100352)
69 |     video_max_pixels: Optional[int] = field(default=602112)
70 |     fps: float = 1.0


--------------------------------------------------------------------------------
/OS-Atlas-7B/scripts/preprocess/params.py:
--------------------------------------------------------------------------------
 1 | from dataclasses import dataclass, field
 2 | from typing import Optional
 3 | 
 4 | from transformers import TrainingArguments
 5 | 
 6 | 
 7 | @dataclass
 8 | class ModelArguments:
 9 |     model_id: Optional[str] = field(default="Qwen/Qwen2-VL-7B-Instruct")
10 | 
11 | 
12 | @dataclass
13 | class TrainingArguments(TrainingArguments):
14 |     cache_dir: Optional[str] = field(default=None)
15 |     optim: str = field(default="adamw_torch")
16 |     adam_beta1: float = field(default=0.9)
17 |     adam_beta2: float = field(default=0.999)
18 |     adam_epsilon: float = field(default=1e-8)
19 | 
20 |     freeze_vision_tower: bool = field(default=False)
21 |     freeze_llm: bool = field(default=False)
22 |     tune_merger: bool = field(default=False)
23 |     disable_flash_attn2: bool = field(default=False)
24 | 
25 |     max_seq_length: int = field(
26 |         default=32768, # This is the default value of the qwen2-vl model
27 |         metadata={
28 |             "help":
29 |                 "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
30 |         },
31 |     )
32 | 
33 |     double_quant: bool = field(
34 |         default=True,
35 |         metadata={"help": "Compress the quantization statistics through double quantization."}
36 |     )
37 |     quant_type: str = field(
38 |         default="nf4",
39 |         metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
40 |     )
41 |     bits: int = field(
42 |         default=16,
43 |         metadata={"help": "How many bits to use."}
44 |     )
45 |     lora_enable: bool = False
46 |     vision_lora: bool = False
47 |     use_dora: bool = False
48 |     lora_rank: int = 64
49 |     lora_alpha: int = 16
50 |     lora_dropout: float = 0.05
51 |     lora_weight_path: str = ""
52 |     lora_bias: str = "none"
53 |     vision_lr: Optional[float] = None
54 |     merger_lr: Optional[float] = None
55 |     lora_namespan_exclude: str = field(default=None, metadata={"help": "List of namespan to exclude for LoRA"})
56 |     num_lora_modules: int = -1
57 | 
58 | 
59 | @dataclass
60 | class DataArguments:
61 |     data_path: str = field(
62 |         default=None, metadata={"help": "Path to the training data."}
63 |     )
64 |     lazy_preprocess: bool = False
65 |     image_folder: Optional[str] = field(default=None)
66 |     image_min_pixels: Optional[int] = field(default=3136)
67 |     image_max_pixels: Optional[int] = field(default=12845056)
68 |     video_min_pixels: Optional[int] = field(default=100352)
69 |     video_max_pixels: Optional[int] = field(default=602112)
70 |     fps: float = 1.0


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | ## Environment
  2 | 
  3 | python 3.10
  4 | 
  5 | transformers==4.45.1
  6 | 
  7 | `pip install -r requirements.txt`
  8 | 
  9 | ## Download ScreenSpot-v2
 10 | 
 11 | `bash download.sh` Download screenspot-v2 images to datas
 12 | 
 13 | Download Model `osunlp/UGround-V1-7B`&`OS-Copilot/OS-Atlas-Base-7B`
 14 | 
 15 | ## Uground
 16 | 
 17 | replace `generation_config.json` & `preprocessor_config.json` for Uground
 18 | 
 19 | (Default huggingface model path: ~/.cache/huggingface/modules/transformers_modules/UGround-V1-7B)
 20 | 
 21 | cd Uground
 22 | 
 23 | ### target
 24 | 
 25 | bash target.sh
 26 | 
 27 | ```
 28 | CUDA_VISIBLE_DEVICES=3 python scripts/target.py \
 29 |      --model_path osunlp/UGround-V1-7B \
 30 |      --screenspot_imgs ../datas/screenspotv2_image \
 31 |      --screenspot_test ../datas/screenspotv2_web_ug_target.json \
 32 |      --output_path ../outputs/ug/target\
 33 |      --max_pixels 1024
 34 | ```
 35 | 
 36 | model_path: path to model
 37 | 
 38 | screenspot_imgs: image path
 39 | 
 40 | screenspot_test: preprocessed json data path (mobile\desktop\web, ug\os)
 41 | 
 42 | output_path: image output path
 43 | 
 44 | max_pixels: max_piexl
 45 | 
 46 | ### untarget
 47 | 
 48 | bash untarget.sh
 49 | 
 50 | ```
 51 | CUDA_VISIBLE_DEVICES=3 python scripts/untarget.py \
 52 |      --model_path osunlp/UGround-V1-7B \
 53 |      --screenspot_imgs ../datas/screenspotv2_image \
 54 |      --screenspot_test ../datas/screenspotv2_web_ug_target.json \
 55 |      --output_path ../outputs/ug/target\
 56 |      --max_pixels 1024
 57 | ```
 58 | 
 59 | ## OS-Atlas-7B
 60 | 
 61 | cd OS-Atlas-7B
 62 | 
 63 | ### target
 64 | 
 65 | bash target.sh
 66 | 
 67 | ```
 68 | CUDA_VISIBLE_DEVICES=3 python scripts/target.py \
 69 |      --model_path OS-Copilot/OS-Atlas-Base-7B \
 70 |      --screenspot_imgs ../datas/screenspotv2_image \
 71 |      --screenspot_test ../datas/screenspot_web_os_target.json \
 72 |      --output_path ../outputs/os/target\
 73 |      --max_pixels 1024
 74 | ```
 75 | 
 76 | ### untarget
 77 | 
 78 | bash untarget.sh
 79 | 
 80 | ```
 81 | CUDA_VISIBLE_DEVICES=3 python scripts/untarget.py \
 82 |      --model_path OS-Copilot/OS-Atlas-Base-7B \
 83 |      --screenspot_imgs ../datas/screenspotv2_image \
 84 |      --screenspot_test ../datas/screenspot_web_os_target.json \
 85 |      --output_path ../outputs/os/untarget\
 86 |      --max_pixels 1024
 87 | ```
 88 | 
 89 | ## Evaluate
 90 | 
 91 | Example
 92 | 
 93 | bash check.sh
 94 | 
 95 | ```
 96 | CUDA_VISIBLE_DEVICES=3 python scripts/check_attack.py \
 97 |      --model_path OS-Copilot/OS-Atlas-Base-7B \
 98 |      --screenspot_imgs ../outputs/os/target \
 99 |      --screenspot_test ../datas \
100 |      --task all \
101 |      --max_pixels 1024
102 | ```
103 | 
104 | output: os_check_result.txt
105 | 
106 | ## Infer
107 | 
108 | Example
109 | 
110 | bash infer.sh
111 | 
112 | ```
113 | CUDA_VISIBLE_DEVICES=3 python scripts/infer_screenspot.py \
114 |     --model_path OS-Copilot/OS-Atlas-Base-7B \
115 |     --screenspot_imgs ../datas/screenspotv2_image \
116 |     --screenspot_test ../datas \
117 |     --task all \
118 |     --max_pixels 1024
119 | ```
120 | 
121 | output: os_infer_result.txt
122 | 
123 | ## Citation
124 | 
125 | If you find our work helpful, please consider citing our paper:
126 | 
127 | ```
128 | @misc{zhao2025robustnessguigroundingmodels,
129 |       title={On the Robustness of GUI Grounding Models Against Image Attacks}, 
130 |       author={Haoren Zhao and Tianyi Chen and Zhen Wang},
131 |       year={2025},
132 |       eprint={2504.04716},
133 |       archivePrefix={arXiv},
134 |       primaryClass={cs.CV},
135 |       url={https://arxiv.org/abs/2504.04716}, 
136 | }
137 | ```
138 | If you have any questions, please feel free to leave issues.


--------------------------------------------------------------------------------
/Uground/scripts/untarget.py:
--------------------------------------------------------------------------------
  1 | from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
  2 | from qwen_vl_utils import process_vision_info
  3 | from preprocess.data_process import make_supervised_data_module
  4 | from preprocess.params import DataArguments
  5 | from preprocess.patch_image import restore_images, pixel_reshape
  6 | from torch.utils.data import DataLoader
  7 | import torch
  8 | import torch.nn.functional as F
  9 | from tqdm import tqdm
 10 | import torchvision
 11 | from PIL import Image
 12 | import os
 13 | import argparse
 14 | 
 15 | parser = argparse.ArgumentParser()
 16 | parser.add_argument('--model_path', type=str, required=True)
 17 | parser.add_argument('--screenspot_imgs', type=str, required=True)
 18 | parser.add_argument('--screenspot_test', type=str, required=True)
 19 | parser.add_argument('--output_path', type=str, required=True)
 20 | parser.add_argument('--max_pixels', type=int, required=True)
 21 | args = parser.parse_args()
 22 | 
 23 | 
 24 | model_id = args.model_path
 25 | # Default: Load the model on the available device(s)
 26 | model = Qwen2VLForConditionalGeneration.from_pretrained(
 27 |     model_id, device_map="cuda:0", torch_dtype=torch.bfloat16
 28 | ).eval()
 29 | for name, param in model.named_parameters():
 30 |     param.requires_grad = False
 31 | processor = AutoProcessor.from_pretrained(model_id)
 32 | 
 33 | data_args = DataArguments(
 34 |     data_path=args.screenspot_test,
 35 |     image_min_pixels=4 * 28 * 28,
 36 |     image_max_pixels=args.max_pixels * 28 * 28,
 37 |     image_folder=args.screenspot_imgs
 38 | )
 39 | 
 40 | sft_dataset = make_supervised_data_module(model_id, processor, data_args)
 41 | dataset = sft_dataset['train_dataset']
 42 | 
 43 | data_collator = sft_dataset['data_collator']
 44 | 
 45 | train_dataloader = DataLoader(
 46 |     dataset, 
 47 |     batch_size=1,
 48 |     shuffle=False, 
 49 |     collate_fn=data_collator
 50 | )
 51 | 
 52 | tokenizer = processor.tokenizer
 53 | scaling_tensor = torch.tensor((0.26862954, 0.26130258, 0.27577711)).to('cuda')
 54 | scaling_tensor = scaling_tensor.reshape((3, 1, 1)).to('cuda')
 55 | iters = 100
 56 | step_size = 1
 57 | epsilon = 16
 58 | step_size = step_size / 255.0 / scaling_tensor
 59 | epsilon = epsilon / 255.0 / scaling_tensor
 60 | inverse_normalize = torchvision.transforms.Normalize(mean=[-0.48145466 / 0.26862954, -0.4578275 / 0.26130258, -0.40821073 / 0.27577711], std=[1.0 / 0.26862954, 1.0 / 0.26130258, 1.0 / 0.27577711])
 61 | output_path = args.output_path
 62 | os.makedirs(output_path, exist_ok=True)
 63 | criterion = torch.nn.MSELoss()
 64 | 
 65 | for idx, batch in enumerate(train_dataloader):
 66 |     input_ids = batch['input_ids'].to(model.device)
 67 |     attention_mask = batch['attention_mask'].to(model.device)
 68 |     labels = batch['labels'].to(model.device)
 69 |     pixel_values = batch['pixel_values'].to(model.device)
 70 |     image_grid_thw = batch['image_grid_thw'].to(model.device)
 71 | 
 72 |     filenames = batch['filenames'][0]
 73 |     ids = batch['ids']
 74 | 
 75 |     print(filenames)
 76 |     print(ids)
 77 | 
 78 |     print(pixel_values.shape)
 79 |     grid_t, grid_h, grid_w = image_grid_thw[0][0], image_grid_thw[0][1], image_grid_thw[0][2]
 80 |     restored = restore_images(pixel_values, grid_t=grid_t, grid_h=grid_h, grid_w=grid_w, merge_size=2, temporal_patch_size=2, patch_size=14, channel=3, data_format='channels_first')[0]
 81 |     with torch.no_grad():
 82 |         org_embedding = model.visual(pixel_values, image_grid_thw)
 83 |     delta = torch.randn_like(restored, requires_grad=True).to(model.device)
 84 |     iter_bar = tqdm(range(iters), desc="Adversarial Iterations", leave=False)
 85 |     for i in iter_bar:
 86 |         adv_images = restored + delta
 87 |         pixels, thw = pixel_reshape(image=adv_images, patch_size=14, merge_size=2, temporal_patch_size=2)
 88 |         adv_embedding = model.visual(pixels, image_grid_thw)
 89 |         loss = criterion(org_embedding, adv_embedding)
 90 |         loss.backward()
 91 |         delta_data = torch.clamp(delta + step_size * torch.sign(delta.grad.detach()), -epsilon, epsilon)
 92 |         delta.data = delta_data
 93 |         delta.grad.zero_()
 94 |         iter_bar.set_postfix(batch=f"{idx} / {len(train_dataloader)}", loss=loss.item())
 95 | 
 96 |     adv_images = restored + delta
 97 |     adv_images = torch.clamp(inverse_normalize(adv_images), 0.0, 1.0)
 98 | 
 99 |     adv_images = adv_images.cpu().detach().numpy().transpose((1, 2, 0))
100 |     adv_images = (adv_images * 255).astype('uint8')
101 |     adv_images = Image.fromarray(adv_images)
102 |     adv_images_file = ids[0] + '_' + filenames[0]
103 |     save_path = os.path.join(output_path, adv_images_file)
104 |     adv_images.save(save_path)
105 | 


--------------------------------------------------------------------------------
/Uground/scripts/target.py:
--------------------------------------------------------------------------------
  1 | from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
  2 | from qwen_vl_utils import process_vision_info
  3 | from preprocess.data_process import make_supervised_data_module
  4 | from preprocess.params import DataArguments
  5 | from preprocess.patch_image import restore_images, pixel_reshape
  6 | from torch.utils.data import DataLoader
  7 | import torch
  8 | import torch.nn.functional as F
  9 | from tqdm import tqdm
 10 | import torchvision
 11 | from PIL import Image
 12 | import os
 13 | import argparse
 14 | 
 15 | parser = argparse.ArgumentParser()
 16 | parser.add_argument('--model_path', type=str, required=True)
 17 | parser.add_argument('--screenspot_imgs', type=str, required=True)
 18 | parser.add_argument('--screenspot_test', type=str, required=True)
 19 | parser.add_argument('--output_path', type=str, required=True)
 20 | parser.add_argument('--max_pixels', type=int, required=True)
 21 | args = parser.parse_args()
 22 | 
 23 | 
 24 | model_id = args.model_path
 25 | # Default: Load the model on the available device(s)
 26 | model = Qwen2VLForConditionalGeneration.from_pretrained(
 27 |     model_id, device_map="cuda:0", torch_dtype=torch.bfloat16
 28 | ).eval()
 29 | for name, param in model.named_parameters():
 30 |     param.requires_grad = False
 31 | processor = AutoProcessor.from_pretrained(model_id)
 32 | 
 33 | data_args = DataArguments(
 34 |     data_path=args.screenspot_test,
 35 |     image_min_pixels=4 * 28 * 28,
 36 |     image_max_pixels=args.max_pixels * 28 * 28,
 37 |     # image_max_pixels=1024*1024,
 38 |     image_folder=args.screenspot_imgs
 39 | )
 40 | 
 41 | # print(processor.tokenizer.pad_token_id)
 42 | sft_dataset = make_supervised_data_module(model_id, processor, data_args)
 43 | dataset = sft_dataset['train_dataset']
 44 | 
 45 | data_collator = sft_dataset['data_collator']
 46 | 
 47 | train_dataloader = DataLoader(
 48 |     dataset, 
 49 |     batch_size=1,
 50 |     shuffle=False, 
 51 |     collate_fn=data_collator
 52 | )
 53 | 
 54 | tokenizer = processor.tokenizer
 55 | scaling_tensor = torch.tensor((0.26862954, 0.26130258, 0.27577711)).to('cuda')
 56 | scaling_tensor = scaling_tensor.reshape((3, 1, 1)).to('cuda')
 57 | iters = 100
 58 | step_size = 1
 59 | epsilon = 16
 60 | step_size = step_size / 255.0 / scaling_tensor
 61 | epsilon = epsilon / 255.0 / scaling_tensor
 62 | inverse_normalize = torchvision.transforms.Normalize(mean=[-0.48145466 / 0.26862954, -0.4578275 / 0.26130258, -0.40821073 / 0.27577711], std=[1.0 / 0.26862954, 1.0 / 0.26130258, 1.0 / 0.27577711])
 63 | output_path = args.output_path
 64 | os.makedirs(output_path, exist_ok=True)
 65 | 
 66 | for idx, batch in enumerate(train_dataloader):
 67 |     input_ids = batch['input_ids'].to(model.device)
 68 |     attention_mask = batch['attention_mask'].to(model.device)
 69 |     labels = batch['labels'].to(model.device)
 70 |     pixel_values = batch['pixel_values'].to(model.device)
 71 |     image_grid_thw = batch['image_grid_thw'].to(model.device)
 72 | 
 73 |     filenames = batch['filenames'][0]
 74 |     ids = batch['ids']
 75 | 
 76 |     grid_t, grid_h, grid_w = image_grid_thw[0][0], image_grid_thw[0][1], image_grid_thw[0][2]
 77 |     restored = restore_images(pixel_values, grid_t=grid_t, grid_h=grid_h, grid_w=grid_w, merge_size=2, temporal_patch_size=2, patch_size=14, channel=3, data_format='channels_first')[0]
 78 |     delta = torch.zeros_like(restored, requires_grad=True).to(model.device)
 79 |     iter_bar = tqdm(range(iters), desc="Adversarial Iterations")
 80 |     for i in iter_bar:
 81 |         adv_images = restored + delta
 82 |         pixels, thw = pixel_reshape(image=adv_images, patch_size=14, merge_size=2, temporal_patch_size=2)
 83 |         outputs = model(
 84 |             input_ids=input_ids,
 85 |             attention_mask=attention_mask,
 86 |             labels=labels,
 87 |             pixel_values=pixels,
 88 |             image_grid_thw=image_grid_thw
 89 |         )
 90 |         loss = outputs.loss
 91 |         loss.backward()
 92 |         delta_data = torch.clamp(delta - step_size * torch.sign(delta.grad.detach()), -epsilon, epsilon)
 93 |         delta.data = delta_data
 94 |         delta.grad.zero_()  # reset the gradient
 95 |         iter_bar.set_postfix(batch=f"{idx} / {len(train_dataloader)}", loss=loss.item())
 96 | 
 97 |     adv_images = restored + delta
 98 |     adv_images = torch.clamp(inverse_normalize(adv_images), 0.0, 1.0)
 99 | 
100 |     adv_images = adv_images.cpu().detach().numpy().transpose((1, 2, 0))
101 |     adv_images = (adv_images * 255).astype('uint8')
102 |     adv_images = Image.fromarray(adv_images)
103 |     adv_images_file = ids[0] + '_' + filenames[0]
104 |     save_path = os.path.join(output_path, adv_images_file)
105 |     adv_images.save(save_path)
106 | 


--------------------------------------------------------------------------------
/OS-Atlas-7B/scripts/target.py:
--------------------------------------------------------------------------------
  1 | from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
  2 | from qwen_vl_utils import process_vision_info
  3 | from preprocess.data_process import make_supervised_data_module
  4 | from preprocess.params import DataArguments
  5 | from preprocess.patch_image import restore_images, pixel_reshape
  6 | from torch.utils.data import DataLoader
  7 | import torch
  8 | import torch.nn.functional as F
  9 | from tqdm import tqdm
 10 | import torchvision
 11 | from PIL import Image
 12 | import os
 13 | import argparse
 14 | 
 15 | parser = argparse.ArgumentParser()
 16 | parser.add_argument('--model_path', type=str, required=True)
 17 | parser.add_argument('--screenspot_imgs', type=str, required=True)
 18 | parser.add_argument('--screenspot_test', type=str, required=True)
 19 | parser.add_argument('--output_path', type=str, required=True)
 20 | parser.add_argument('--max_pixels', type=int, required=True)
 21 | args = parser.parse_args()
 22 | 
 23 | model_id = args.model_path
 24 | # Default: Load the model on the available device(s)
 25 | model = Qwen2VLForConditionalGeneration.from_pretrained(
 26 |     model_id, device_map="cuda:0", torch_dtype=torch.bfloat16
 27 | ).eval()
 28 | for name, param in model.named_parameters():
 29 |     param.requires_grad = False
 30 | processor = AutoProcessor.from_pretrained(model_id)
 31 | 
 32 | data_args = DataArguments(
 33 |     data_path=args.screenspot_test,
 34 |     image_min_pixels=4 * 28 * 28,
 35 |     image_max_pixels=args.max_pixels * 28 * 28,
 36 |     # image_max_pixels=1024*1024,
 37 |     image_folder=args.screenspot_imgs
 38 | )
 39 | 
 40 | # print(processor.tokenizer.pad_token_id)
 41 | sft_dataset = make_supervised_data_module(model_id, processor, data_args)
 42 | dataset = sft_dataset['train_dataset']
 43 | 
 44 | data_collator = sft_dataset['data_collator']
 45 | 
 46 | train_dataloader = DataLoader(
 47 |     dataset, 
 48 |     batch_size=1,
 49 |     shuffle=False, 
 50 |     collate_fn=data_collator
 51 | )
 52 | 
 53 | tokenizer = processor.tokenizer
 54 | scaling_tensor = torch.tensor((0.26862954, 0.26130258, 0.27577711)).to('cuda')
 55 | scaling_tensor = scaling_tensor.reshape((3, 1, 1)).to('cuda')
 56 | iters = 100
 57 | step_size = 1
 58 | epsilon = 16
 59 | step_size = step_size / 255.0 / scaling_tensor
 60 | epsilon = epsilon / 255.0 / scaling_tensor
 61 | inverse_normalize = torchvision.transforms.Normalize(mean=[-0.48145466 / 0.26862954, -0.4578275 / 0.26130258, -0.40821073 / 0.27577711], std=[1.0 / 0.26862954, 1.0 / 0.26130258, 1.0 / 0.27577711])
 62 | output_path = args.output_path
 63 | os.makedirs(output_path, exist_ok=True)
 64 | 
 65 | for idx, batch in enumerate(train_dataloader):
 66 |     input_ids = batch['input_ids'].to(model.device)
 67 |     attention_mask = batch['attention_mask'].to(model.device)
 68 |     labels = batch['labels'].to(model.device)
 69 |     pixel_values = batch['pixel_values'].to(model.device)
 70 |     image_grid_thw = batch['image_grid_thw'].to(model.device)
 71 | 
 72 |     filenames = batch['filenames'][0]
 73 |     ids = batch['ids']
 74 | 
 75 |     grid_t, grid_h, grid_w = image_grid_thw[0][0], image_grid_thw[0][1], image_grid_thw[0][2]
 76 | 
 77 |     restored = restore_images(pixel_values, grid_t=grid_t, grid_h=grid_h, grid_w=grid_w, merge_size=2, temporal_patch_size=2, patch_size=14, channel=3, data_format='channels_first')[0]
 78 |     delta = torch.zeros_like(restored, requires_grad=True).to(model.device)
 79 |     iter_bar = tqdm(range(iters), desc="Adversarial Iterations")
 80 |     for i in iter_bar:
 81 |         adv_images = restored + delta
 82 |         pixels, thw = pixel_reshape(image=adv_images, patch_size=14, merge_size=2, temporal_patch_size=2)
 83 |         outputs = model(
 84 |             input_ids=input_ids,
 85 |             attention_mask=attention_mask,
 86 |             labels=labels,
 87 |             pixel_values=pixels,
 88 |             image_grid_thw=image_grid_thw
 89 |         )
 90 |         loss = outputs.loss
 91 |         loss.backward()
 92 |         delta_data = torch.clamp(delta - step_size * torch.sign(delta.grad.detach()), -epsilon, epsilon)
 93 |         delta.data = delta_data
 94 |         delta.grad.zero_()  # reset the gradient
 95 |         iter_bar.set_postfix(batch=f"{idx} / {len(train_dataloader)}", loss=loss.item())
 96 | 
 97 |     adv_images = restored + delta
 98 |     adv_images = torch.clamp(inverse_normalize(adv_images), 0.0, 1.0)
 99 | 
100 |     adv_images = adv_images.cpu().detach().numpy().transpose((1, 2, 0))
101 |     adv_images = (adv_images * 255).astype('uint8')
102 |     adv_images = Image.fromarray(adv_images)
103 |     adv_images_file = ids[0] + '_' + filenames[0]
104 |     save_path = os.path.join(output_path, adv_images_file)
105 |     adv_images.save(save_path)
106 | 


--------------------------------------------------------------------------------
/OS-Atlas-7B/scripts/untarget.py:
--------------------------------------------------------------------------------
  1 | from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
  2 | from qwen_vl_utils import process_vision_info
  3 | from preprocess.data_process import make_supervised_data_module
  4 | from preprocess.params import DataArguments
  5 | from preprocess.patch_image import restore_images, pixel_reshape
  6 | from torch.utils.data import DataLoader
  7 | import torch
  8 | import torch.nn.functional as F
  9 | from tqdm import tqdm
 10 | import torchvision
 11 | from PIL import Image
 12 | import os
 13 | 
 14 | 
 15 | import argparse
 16 | 
 17 | parser = argparse.ArgumentParser()
 18 | parser.add_argument('--model_path', type=str, required=True)
 19 | parser.add_argument('--screenspot_imgs', type=str, required=True)
 20 | parser.add_argument('--screenspot_test', type=str, required=True)
 21 | parser.add_argument('--output_path', type=str, required=True)
 22 | parser.add_argument('--max_pixels', type=int, required=True)
 23 | args = parser.parse_args()
 24 | 
 25 | model_id = args.model_path
 26 | # Default: Load the model on the available device(s)
 27 | model = Qwen2VLForConditionalGeneration.from_pretrained(
 28 |     model_id, device_map="cuda:0", torch_dtype=torch.bfloat16
 29 | ).eval()
 30 | for name, param in model.named_parameters():
 31 |     param.requires_grad = False
 32 | processor = AutoProcessor.from_pretrained(model_id)
 33 | 
 34 | data_args = DataArguments(
 35 |     data_path=args.screenspot_test,
 36 |     image_min_pixels=4 * 28 * 28,
 37 |     image_max_pixels=args.max_pixels * 28 * 28,
 38 |     # image_max_pixels=1024*1024,
 39 |     image_folder=args.screenspot_imgs
 40 | )
 41 | 
 42 | # print(processor.tokenizer.pad_token_id)
 43 | sft_dataset = make_supervised_data_module(model_id, processor, data_args)
 44 | dataset = sft_dataset['train_dataset']
 45 | 
 46 | data_collator = sft_dataset['data_collator']
 47 | 
 48 | train_dataloader = DataLoader(
 49 |     dataset, 
 50 |     batch_size=1,
 51 |     shuffle=False, 
 52 |     collate_fn=data_collator
 53 | )
 54 | 
 55 | tokenizer = processor.tokenizer
 56 | scaling_tensor = torch.tensor((0.26862954, 0.26130258, 0.27577711)).to('cuda')
 57 | # scaling_tensor = scaling_tensor.reshape((3, 1, 1)).unsqueeze(0).to('cuda')
 58 | scaling_tensor = scaling_tensor.reshape((3, 1, 1)).to('cuda')
 59 | iters = 100
 60 | step_size = 1
 61 | epsilon = 16
 62 | step_size = step_size / 255.0 / scaling_tensor
 63 | epsilon = epsilon / 255.0 / scaling_tensor
 64 | inverse_normalize = torchvision.transforms.Normalize(mean=[-0.48145466 / 0.26862954, -0.4578275 / 0.26130258, -0.40821073 / 0.27577711], std=[1.0 / 0.26862954, 1.0 / 0.26130258, 1.0 / 0.27577711])
 65 | output_path = args.output_path
 66 | os.makedirs(output_path, exist_ok=True)
 67 | criterion = torch.nn.MSELoss()
 68 | 
 69 | for idx, batch in enumerate(train_dataloader):
 70 |     input_ids = batch['input_ids'].to(model.device)
 71 |     attention_mask = batch['attention_mask'].to(model.device)
 72 |     labels = batch['labels'].to(model.device)
 73 |     pixel_values = batch['pixel_values'].to(model.device)
 74 |     image_grid_thw = batch['image_grid_thw'].to(model.device)
 75 | 
 76 |     filenames = batch['filenames'][0]
 77 |     ids = batch['ids']
 78 | 
 79 |     print(filenames)
 80 |     print(ids)
 81 | 
 82 |     print(pixel_values.shape)
 83 |     grid_t, grid_h, grid_w = image_grid_thw[0][0], image_grid_thw[0][1], image_grid_thw[0][2]
 84 |     # print(grid_t, grid_h, grid_w)
 85 |     restored = restore_images(pixel_values, grid_t=grid_t, grid_h=grid_h, grid_w=grid_w, merge_size=2, temporal_patch_size=2, patch_size=14, channel=3, data_format='channels_first')[0]
 86 |     with torch.no_grad():
 87 |         org_embedding = model.visual(pixel_values, image_grid_thw)
 88 |         # print(org_embedding.shape)
 89 |     delta = torch.randn_like(restored, requires_grad=True).to(model.device)
 90 |     iter_bar = tqdm(range(iters), desc="Adversarial Iterations", leave=False)
 91 |     for i in iter_bar:
 92 |         adv_images = restored + delta
 93 |         # print(adv_images.dtype)
 94 |         # print(adv_images.shape, adv_images.requires_grad)
 95 |         pixels, thw = pixel_reshape(image=adv_images, patch_size=14, merge_size=2, temporal_patch_size=2)
 96 |         # print(pixels, thw)
 97 |         adv_embedding = model.visual(pixels, image_grid_thw)
 98 |         # print(adv_embedding)
 99 |         loss = criterion(org_embedding, adv_embedding)
100 |         loss.backward()
101 |         # print(delta.grad)
102 |         delta_data = torch.clamp(delta + step_size * torch.sign(delta.grad.detach()), -epsilon, epsilon)
103 |     #     print(delta_data.shape)
104 |         delta.data = delta_data
105 |         delta.grad.zero_()  # reset the gradient
106 |         iter_bar.set_postfix(batch=f"{idx} / {len(train_dataloader)}", loss=loss.item())
107 | 
108 |     adv_images = restored + delta
109 |     adv_images = torch.clamp(inverse_normalize(adv_images), 0.0, 1.0)
110 | 
111 |     adv_images = adv_images.cpu().detach().numpy().transpose((1, 2, 0))
112 |     adv_images = (adv_images * 255).astype('uint8')
113 |     adv_images = Image.fromarray(adv_images)
114 |     adv_images_file = ids[0] + '_' + filenames[0]
115 |     save_path = os.path.join(output_path, adv_images_file)
116 |     adv_images.save(save_path)
117 |     # generated_ids = model.generate(
118 |     #         input_ids=input_ids,
119 |     #         attention_mask=attention_mask,
120 |     #         pixel_values=adv_images,
121 |     #         image_grid_thw=image_grid_thw
122 |     #     )
123 |     # generated_ids_trimmed = [
124 |     #         out_ids[len(in_ids):] for in_ids, out_ids in zip(input_ids, generated_ids)
125 |     # ]
126 |     # output_text = processor.batch_decode(
127 |     #     generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
128 |     # )[0]
129 |     # print(output_text)
130 |     # break


--------------------------------------------------------------------------------
/Uground/scripts/infer_screenspot.py:
--------------------------------------------------------------------------------
  1 | import json
  2 | import argparse
  3 | import os
  4 | import logging
  5 | import torchvision.transforms as T
  6 | from tqdm import tqdm
  7 | from PIL import Image
  8 | from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
  9 | from qwen_vl_utils import process_vision_info
 10 | import re
 11 | from transformers.generation import GenerationConfig
 12 | from preprocess.point_extract import extract_bbox, pred_2_point
 13 | 
 14 | logging.basicConfig(level=logging.INFO)
 15 | parser = argparse.ArgumentParser()
 16 | parser.add_argument('--model_path', type=str, required=True)
 17 | parser.add_argument('--screenspot_imgs', type=str, required=True)
 18 | parser.add_argument('--screenspot_test', type=str, required=True)
 19 | parser.add_argument('--task', type=str, required=True)
 20 | parser.add_argument('--max_pixels', type=int, required=True)
 21 | args = parser.parse_args()
 22 | 
 23 | model = Qwen2VLForConditionalGeneration.from_pretrained(
 24 |     args.model_path, torch_dtype="auto", device_map="cuda:0"
 25 | ).eval()
 26 | processor = AutoProcessor.from_pretrained(args.model_path)
 27 | model.generation_config = GenerationConfig.from_pretrained(args.model_path, trust_remote_code=True)
 28 | print("Load Success")
 29 | min_pixels=4*28*28
 30 | max_pixels=args.max_pixels*28*28
 31 | 
 32 | 
 33 | if args.task == "all":
 34 |     tasks = ["mobile", "desktop", "web"]
 35 | else:
 36 |     tasks = [args.task]
 37 | tasks_result = []
 38 | result = []
 39 | max_num_patchs = 0
 40 | for task in tasks:
 41 |     dataset = "screenspot_" + task + "_v2.json"
 42 |     screenspot_data = json.load(open(os.path.join(args.screenspot_test, dataset), 'r'))
 43 |     print("Num of sample: " + str(len(screenspot_data)))
 44 | 
 45 |     num_action = 0
 46 |     corr_action = 0
 47 |     text_correct = []
 48 |     icon_correct = []
 49 |     num_wrong_format = 0
 50 |     for j, item in tqdm(enumerate(screenspot_data)):
 51 |         num_action += 1
 52 |         filename = item["img_filename"]
 53 |         img_path = os.path.join(args.screenspot_imgs, filename)
 54 |         image = Image.open(img_path)
 55 |         if not os.path.exists(img_path):
 56 |             print("img not found")
 57 |             input()
 58 |             continue
 59 |         instruction = item["instruction"]
 60 |         bbox = item["bbox"]
 61 |         img_size = image.size
 62 |         bbox = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
 63 |         bbox = [bbox[0] / img_size[0], bbox[1] / img_size[1], bbox[2] / img_size[0], bbox[3] / img_size[1]]
 64 |         
 65 |         messages = [
 66 |             {
 67 |                 "role": "user",
 68 |                 "content": [
 69 |                     {"type": "image", "image": img_path, "min_pixels": min_pixels, "max_pixels": max_pixels},
 70 |                     {"type": "text", "text": f"""
 71 | Your task is to help the user identify the precise coordinates (x, y) of a specific area/element/object on the screen based on a description.
 72 | Description: {instruction}
 73 | Answer:"""},
 74 |                 ],
 75 |             },
 76 |         ]
 77 |         text = processor.apply_chat_template(
 78 |             messages, tokenize=False, add_generation_prompt=True
 79 |         )
 80 |         image_inputs, video_inputs = process_vision_info(messages)
 81 |         inputs = processor(
 82 |             text=[text],
 83 |             images=image_inputs,
 84 |             videos=video_inputs,
 85 |             padding=True,
 86 |             return_tensors="pt",
 87 |         )
 88 |         inputs = inputs.to("cuda")
 89 |         # print(inputs.pixel_values.shape)
 90 |         # Inference: Generation of the output
 91 |         generated_ids = model.generate(**inputs, max_new_tokens=128)
 92 | 
 93 |         generated_ids_trimmed = [
 94 |             out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
 95 |         ]
 96 |         output_text = processor.batch_decode(
 97 |             generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
 98 |         )[0]
 99 | 
100 |         try:
101 |             if 'box' in output_text:
102 |                 pred_bbox = extract_bbox(output_text)
103 |                 click_point = [(pred_bbox[0][0] + pred_bbox[1][0]) / 2, (pred_bbox[0][1] + pred_bbox[1][1]) / 2]
104 |                 click_point = [item / 1000 for item in click_point]
105 |             else:
106 |                 click_point = pred_2_point(output_text)
107 |                 click_point = [item / 1000 for item in click_point]
108 |             if (bbox[0] <= click_point[0] <= bbox[2]) and (bbox[1] <= click_point[1] <= bbox[3]):
109 |                 corr_action += 1
110 |                 if item["data_type"] == 'text':
111 |                     text_correct.append(1)
112 |                 else:
113 |                     icon_correct.append(1)
114 |                 logging.info("match " + str(corr_action / num_action))
115 |             else:
116 |                 if item["data_type"] == 'text':
117 |                     text_correct.append(0)
118 |                 else:
119 |                     icon_correct.append(0)
120 |                 with open("temp2.txt", 'a+') as f:
121 |                     f.write(str(filename)+' '+str(click_point)+'\n')
122 |                 logging.info("unmatch " + str(corr_action / num_action))
123 |             result.append({"img_path": img_path, "text": instruction, "bbox": bbox, "pred": click_point,
124 |                            "type": item["data_type"], "source": item["data_source"]})
125 |         except:
126 |             num_wrong_format += 1
127 |             if item["data_type"] == 'text':
128 |                 text_correct.append(0)
129 |             else:
130 |                 icon_correct.append(0)
131 |             logging.info("Step: " + str(j) + " wrong format")
132 | 
133 |     logging.info("Action Acc: " + str(corr_action / num_action))
134 |     logging.info("Total num: " + str(num_action))
135 |     logging.info("Wrong format num: " + str(num_wrong_format))
136 |     logging.info("Text Acc: " + str(sum(text_correct) / len(text_correct) if len(text_correct) != 0 else 0))
137 |     logging.info("Icon Acc: " + str(sum(icon_correct) / len(icon_correct) if len(icon_correct) != 0 else 0))
138 | 
139 |     text_acc = sum(text_correct) / len(text_correct) if len(text_correct) != 0 else 0
140 |     icon_acc = sum(icon_correct) / len(icon_correct) if len(icon_correct) != 0 else 0
141 |     tasks_result.append([text_acc, icon_acc])
142 | 
143 | logging.info(tasks_result)
144 | with open("Uground_infer_result.txt", 'a+') as f:
145 |     f.write(str(tasks_result)+'\n')
146 | 


--------------------------------------------------------------------------------
/OS-Atlas-7B/scripts/infer_screenspot.py:
--------------------------------------------------------------------------------
  1 | import json
  2 | import argparse
  3 | import os
  4 | import logging
  5 | import torchvision.transforms as T
  6 | from tqdm import tqdm
  7 | from PIL import Image
  8 | from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
  9 | from qwen_vl_utils import process_vision_info
 10 | from preprocess.point_extract import extract_bbox, pred_2_point
 11 | 
 12 | logging.basicConfig(level=logging.INFO)
 13 | parser = argparse.ArgumentParser()
 14 | parser.add_argument('--model_path', type=str, required=True)
 15 | parser.add_argument('--screenspot_imgs', type=str, required=True)
 16 | parser.add_argument('--screenspot_test', type=str, required=True)
 17 | parser.add_argument('--task', type=str, required=True)
 18 | parser.add_argument('--max_pixels', type=int, required=True)
 19 | args = parser.parse_args()
 20 | 
 21 | model = Qwen2VLForConditionalGeneration.from_pretrained(
 22 |     args.model_path, torch_dtype="auto", device_map="cuda:0"
 23 | )
 24 | processor = AutoProcessor.from_pretrained(args.model_path)
 25 | print("Load Success")
 26 | min_pixels=4*28*28
 27 | max_pixels=args.max_pixels*28*28
 28 | 
 29 | 
 30 | if args.task == "all":
 31 |     tasks = ["mobile", "desktop", "web"]
 32 | else:
 33 |     tasks = [args.task]
 34 | tasks_result = []
 35 | result = []
 36 | max_num_patchs = 0
 37 | for task in tasks:
 38 |     dataset = "screenspot_" + task + "_v2.json"
 39 |     screenspot_data = json.load(open(os.path.join(args.screenspot_test, dataset), 'r'))
 40 |     # screenspot_data = json.load(open(args.screenspot_test, 'r'))
 41 |     print("Num of sample: " + str(len(screenspot_data)))
 42 | 
 43 |     num_action = 0
 44 |     corr_action = 0
 45 |     text_correct = []
 46 |     icon_correct = []
 47 |     num_wrong_format = 0
 48 |     for j, item in tqdm(enumerate(screenspot_data)):
 49 |         num_action += 1
 50 |         filename = item["img_filename"]
 51 |         # filename = item["image"]
 52 |         img_path = os.path.join(args.screenspot_imgs, filename)
 53 |         image = Image.open(img_path)
 54 |         if not os.path.exists(img_path):
 55 |             print("img not found")
 56 |             input()
 57 |             continue
 58 |         instruction = item["instruction"]
 59 |         bbox = item["bbox"]
 60 |         img_size = image.size
 61 |         bbox = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
 62 |         bbox = [bbox[0] / img_size[0], bbox[1] / img_size[1], bbox[2] / img_size[0], bbox[3] / img_size[1]]
 63 |         
 64 |         messages = [
 65 |             {
 66 |                 "role": "user",
 67 |                 "content": [
 68 |                     {
 69 |                         "type": "image",
 70 |                         "image": img_path,
 71 |                         "min_pixels": min_pixels,
 72 |                         "max_pixels": max_pixels,
 73 |                     },
 74 |                     {"type": "text", "text": f"In this UI screenshot, what is the position of the element corresponding to the command \"{instruction}\" (with bbox)?"},
 75 |                     # {"type": "text", "text": f"In this UI screenshot, what is the position of the element corresponding to the command \"{instruction}\" (with point)?"}
 76 |                 ],
 77 |             }
 78 |         ]
 79 |         text = processor.apply_chat_template(
 80 |             messages, tokenize=False, add_generation_prompt=True
 81 |         )
 82 |         image_inputs, video_inputs = process_vision_info(messages)
 83 |         inputs = processor(
 84 |             text=[text],
 85 |             images=image_inputs,
 86 |             videos=video_inputs,
 87 |             padding=True,
 88 |             return_tensors="pt",
 89 |         )
 90 |         inputs = inputs.to("cuda")
 91 |         # if inputs.pixel_values.shape[0] > max_num_patchs:
 92 |         #     max_num_patchs = inputs.pixel_values.shape[0]
 93 |         # print(inputs.pixel_values.shape)
 94 |         # Inference: Generation of the output
 95 |         generated_ids = model.generate(**inputs, max_new_tokens=128)
 96 | 
 97 |         generated_ids_trimmed = [
 98 |             out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
 99 |         ]
100 |         output_text = processor.batch_decode(
101 |             generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
102 |         )[0]
103 |         # print(output_text)
104 |         output_text = output_text.split('<|box_start|>')[-1].split('<|box_end|>')[0]
105 |         # print(output_text)
106 |         try:
107 |             if 'box' in output_text:
108 |                 pred_bbox = extract_bbox(output_text)
109 |                 print(pred_bbox)
110 |                 click_point = [(pred_bbox[0][0] + pred_bbox[1][0]) / 2, (pred_bbox[0][1] + pred_bbox[1][1]) / 2]
111 |                 click_point = [item / 1000 for item in click_point]
112 |             else:
113 |                 click_point = pred_2_point(output_text)
114 |                 click_point = [item / 1000 for item in click_point]
115 |             if (bbox[0] <= click_point[0] <= bbox[2]) and (bbox[1] <= click_point[1] <= bbox[3]):
116 |                 corr_action += 1
117 |                 if item["data_type"] == 'text':
118 |                     text_correct.append(1)
119 |                 else:
120 |                     icon_correct.append(1)
121 |                 logging.info("match " + str(corr_action / num_action))
122 |             else:
123 |                 if item["data_type"] == 'text':
124 |                     text_correct.append(0)
125 |                 else:
126 |                     icon_correct.append(0)
127 |                 logging.info("unmatch " + str(corr_action / num_action))
128 |             result.append({"img_path": img_path, "text": instruction, "bbox": bbox, "pred": click_point,
129 |                            "type": item["data_type"], "source": item["data_source"]})
130 |         except:
131 |             num_wrong_format += 1
132 |             if item["data_type"] == 'text':
133 |                 text_correct.append(0)
134 |             else:
135 |                 icon_correct.append(0)
136 |             logging.info("Step: " + str(j) + " wrong format")
137 | 
138 |     logging.info("Action Acc: " + str(corr_action / num_action))
139 |     logging.info("Total num: " + str(num_action))
140 |     logging.info("Wrong format num: " + str(num_wrong_format))
141 |     logging.info("Text Acc: " + str(sum(text_correct) / len(text_correct) if len(text_correct) != 0 else 0))
142 |     logging.info("Icon Acc: " + str(sum(icon_correct) / len(icon_correct) if len(icon_correct) != 0 else 0))
143 | 
144 |     text_acc = sum(text_correct) / len(text_correct) if len(text_correct) != 0 else 0
145 |     icon_acc = sum(icon_correct) / len(icon_correct) if len(icon_correct) != 0 else 0
146 |     tasks_result.append([text_acc, icon_acc])
147 | 
148 | logging.info(tasks_result)
149 | with open("OS_infer_result.txt", 'a+') as f:
150 |     f.write(str(tasks_result)+'\n')
151 | 


--------------------------------------------------------------------------------
/Uground/scripts/check_attack.py:
--------------------------------------------------------------------------------
  1 | import json
  2 | import argparse
  3 | import os
  4 | import logging
  5 | import torchvision.transforms as T
  6 | from tqdm import tqdm
  7 | from PIL import Image
  8 | from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
  9 | from qwen_vl_utils import process_vision_info
 10 | import re
 11 | from preprocess.point_extract import extract_bbox, pred_2_point
 12 | 
 13 | logging.basicConfig(level=logging.INFO)
 14 | parser = argparse.ArgumentParser()
 15 | parser.add_argument('--model_path', type=str, required=True)
 16 | parser.add_argument('--screenspot_imgs', type=str, required=True)
 17 | parser.add_argument('--screenspot_test', type=str, required=True)
 18 | parser.add_argument('--task', type=str, required=True)
 19 | parser.add_argument('--max_pixels', type=int, required=True)
 20 | args = parser.parse_args()
 21 | 
 22 | model = Qwen2VLForConditionalGeneration.from_pretrained(
 23 |     args.model_path, torch_dtype="auto", device_map="auto"
 24 | )
 25 | processor = AutoProcessor.from_pretrained(args.model_path)
 26 | print("Load Success")
 27 | min_pixels=4*28*28
 28 | max_pixels=args.max_pixels*28*28
 29 | 
 30 | 
 31 | if args.task == "all":
 32 |     tasks = ["mobile", "desktop", "web"]
 33 | else:
 34 |     tasks = [args.task]
 35 | tasks_result = []
 36 | tasks_result_asr = []
 37 | result = []
 38 | for task in tasks:
 39 |     dataset = "screenspotv2_" + task + "_ug_target.json"
 40 |     screenspot_data = json.load(open(os.path.join(args.screenspot_test, dataset), 'r'))
 41 |     # screenspot_data = json.load(open(args.screenspot_test, 'r'))
 42 |     print("Num of sample: " + str(len(screenspot_data)))
 43 | 
 44 |     num_action = 0
 45 |     corr_action = 0
 46 |     text_correct = []
 47 |     icon_correct = []
 48 | 
 49 |     num_action_asr = 0
 50 |     corr_action_asr = 0
 51 |     text_correct_asr = []
 52 |     icon_correct_asr = []
 53 | 
 54 |     num_wrong_format = 0
 55 |     for j, item in tqdm(enumerate(screenspot_data)):
 56 |         num_action += 1
 57 |         filename = item["image"]
 58 |         id = item["id"]
 59 |         filename = id + '_' + filename
 60 |         img_path = os.path.join(args.screenspot_imgs, filename)
 61 |         image = Image.open(img_path)
 62 |         if not os.path.exists(img_path):
 63 |             print("img not found")
 64 |             # input()
 65 |             continue
 66 |         instruction = item["instruction"]
 67 |         bbox = item["bbox"]
 68 |         img_size = image.size
 69 |         # bbox = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
 70 |         # bbox = [bbox[0] / img_size[0], bbox[1] / img_size[1], bbox[2] / img_size[0], bbox[3] / img_size[1]]
 71 |         
 72 |         messages = [
 73 |             {
 74 |                 "role": "user",
 75 |                 "content": [
 76 |                     {"type": "image", "image": img_path, "min_pixels": min_pixels, "max_pixels": max_pixels},
 77 |                     {"type": "text", "text": f"""
 78 | Your task is to help the user identify the precise coordinates (x, y) of a specific area/element/object on the screen based on a description.
 79 | Description: {instruction}
 80 | Answer:"""},
 81 |                 ],
 82 |             },
 83 |         ]
 84 |         text = processor.apply_chat_template(
 85 |             messages, tokenize=False, add_generation_prompt=True
 86 |         )
 87 |         image_inputs, video_inputs = process_vision_info(messages)
 88 |         inputs = processor(
 89 |             text=[text],
 90 |             images=image_inputs,
 91 |             videos=video_inputs,
 92 |             padding=True,
 93 |             return_tensors="pt",
 94 |         )
 95 |         inputs = inputs.to("cuda")
 96 |         # Inference: Generation of the output
 97 |         generated_ids = model.generate(**inputs, max_new_tokens=128)
 98 | 
 99 |         generated_ids_trimmed = [
100 |             out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
101 |         ]
102 |         output_text = processor.batch_decode(
103 |             generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
104 |         )[0]
105 | 
106 |         try:
107 |             if 'box' in output_text:
108 |                 pred_bbox = extract_bbox(output_text)
109 |                 click_point = [(pred_bbox[0][0] + pred_bbox[1][0]) / 2, (pred_bbox[0][1] + pred_bbox[1][1]) / 2]
110 |                 click_point = [item / 1000 for item in click_point]
111 |             else:
112 |                 click_point = pred_2_point(output_text)
113 |                 click_point = [item / 1000 for item in click_point]
114 |             # check acc
115 |             print(click_point)
116 |             if (bbox[0] <= click_point[0] <= bbox[2]) and (bbox[1] <= click_point[1] <= bbox[3]):
117 |                 corr_action += 1
118 |                 if item["data_type"] == 'text':
119 |                     text_correct.append(1)
120 |                 else:
121 |                     icon_correct.append(1)
122 |                 logging.info("match " + str(corr_action / num_action))
123 |             else:
124 |                 if item["data_type"] == 'text':
125 |                     text_correct.append(0)
126 |                 else:
127 |                     icon_correct.append(0)
128 |                 logging.info("unmatch " + str(corr_action / num_action))
129 |             # check asr
130 |             if (0.0 <= click_point[0] <= 0.02) and (0.0 <= click_point[1] <= 0.02):
131 |                 corr_action_asr += 1
132 |                 if item["data_type"] == 'text':
133 |                     text_correct_asr.append(1)
134 |                 else:
135 |                     icon_correct_asr.append(1)
136 |                 logging.info("attack sr " + str(corr_action_asr / num_action))
137 |                 # with open("temp.txt", 'a+') as f:
138 |                 #     f.write(str(id)+' '+str(click_point)+'\n')
139 |             else:
140 |                 if item["data_type"] == 'text':
141 |                     text_correct_asr.append(0)
142 |                 else:
143 |                     icon_correct_asr.append(0)
144 |                 logging.info("attack fr " + str(corr_action_asr / num_action))
145 |             result.append({"img_path": img_path, "text": instruction, "click_point": click_point, "bbox": bbox, "img_size": img_size, "output_box": output_text,
146 |                            "type": item["data_type"], "source": item["data_source"]})
147 |         except:
148 |             num_wrong_format += 1
149 |             if item["data_type"] == 'text':
150 |                 text_correct.append(0)
151 |                 text_correct_asr.append(0)
152 |             else:
153 |                 icon_correct.append(0)
154 |                 icon_correct_asr.append(0)
155 |             logging.info("Step: " + str(j) + " wrong format")
156 | 
157 |     logging.info("Action ACC: " + str(corr_action / num_action))
158 |     logging.info("Total num: " + str(num_action))
159 |     logging.info("Wrong format num: " + str(num_wrong_format))
160 |     logging.info("Text ACC: " + str(sum(text_correct) / len(text_correct) if len(text_correct) != 0 else 0))
161 |     logging.info("Icon ACC: " + str(sum(icon_correct) / len(icon_correct) if len(icon_correct) != 0 else 0))
162 | 
163 |     text_acc = sum(text_correct) / len(text_correct) if len(text_correct) != 0 else 0
164 |     icon_acc = sum(icon_correct) / len(icon_correct) if len(icon_correct) != 0 else 0
165 |     tasks_result.append([text_acc, icon_acc])
166 | 
167 |     logging.info("Action ASR: " + str(corr_action_asr / num_action))
168 |     logging.info("Total num: " + str(num_action))
169 |     logging.info("Wrong format num: " + str(num_wrong_format))
170 |     logging.info("Text ASR: " + str(sum(text_correct_asr) / len(text_correct_asr) if len(text_correct_asr) != 0 else 0))
171 |     logging.info("Icon ASR: " + str(sum(icon_correct_asr) / len(icon_correct_asr) if len(icon_correct_asr) != 0 else 0))
172 | 
173 |     text_asr = sum(text_correct_asr) / len(text_correct_asr) if len(text_correct_asr) != 0 else 0
174 |     icon_asr = sum(icon_correct_asr) / len(icon_correct_asr) if len(icon_correct_asr) != 0 else 0
175 |     tasks_result_asr.append([text_asr, icon_asr])
176 | 
177 | logging.info(tasks_result)
178 | logging.info(tasks_result_asr)
179 | 
180 | with open("Uground_check_result.txt", 'a+') as f:
181 |     f.write(str(tasks_result)+'\n'+str(tasks_result_asr)+'\n')
182 | json.dump(result, open("result_check_web.json", 'w'), indent=2)
183 | 


--------------------------------------------------------------------------------
/OS-Atlas-7B/scripts/check_attack.py:
--------------------------------------------------------------------------------
  1 | import json
  2 | import argparse
  3 | import os
  4 | import logging
  5 | import torchvision.transforms as T
  6 | from tqdm import tqdm
  7 | from PIL import Image
  8 | from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
  9 | from qwen_vl_utils import process_vision_info
 10 | import re
 11 | from preprocess.point_extract import extract_bbox, pred_2_point
 12 | 
 13 | logging.basicConfig(level=logging.INFO)
 14 | parser = argparse.ArgumentParser()
 15 | parser.add_argument('--model_path', type=str, required=True)
 16 | parser.add_argument('--screenspot_imgs', type=str, required=True)
 17 | parser.add_argument('--screenspot_test', type=str, required=True)
 18 | parser.add_argument('--task', type=str, required=True)
 19 | parser.add_argument('--max_pixels', type=int, required=True)
 20 | args = parser.parse_args()
 21 | 
 22 | model = Qwen2VLForConditionalGeneration.from_pretrained(
 23 |     args.model_path, torch_dtype="auto", device_map="auto"
 24 | )
 25 | processor = AutoProcessor.from_pretrained(args.model_path)
 26 | print("Load Success")
 27 | min_pixels=4*28*28
 28 | max_pixels=args.max_pixels*28*28
 29 | 
 30 | 
 31 | if args.task == "all":
 32 |     tasks = ["mobile", "desktop", "web"]
 33 | else:
 34 |     tasks = [args.task]
 35 | tasks_result = []
 36 | tasks_result_asr = []
 37 | result = []
 38 | for task in tasks:
 39 |     dataset = "screenspot_" + task + "_os_target.json"
 40 |     screenspot_data = json.load(open(os.path.join(args.screenspot_test, dataset), 'r'))
 41 |     # screenspot_data = json.load(open(args.screenspot_test, 'r'))
 42 |     print("Num of sample: " + str(len(screenspot_data)))
 43 | 
 44 |     num_action = 0
 45 |     corr_action = 0
 46 |     text_correct = []
 47 |     icon_correct = []
 48 | 
 49 |     num_action_asr = 0
 50 |     corr_action_asr = 0
 51 |     text_correct_asr = []
 52 |     icon_correct_asr = []
 53 | 
 54 |     num_wrong_format = 0
 55 |     for j, item in tqdm(enumerate(screenspot_data)):
 56 |         num_action += 1
 57 |         filename = item["image"]
 58 |         id = item["id"]
 59 |         filename = id + '_' + filename
 60 |         img_path = os.path.join(args.screenspot_imgs, filename)
 61 |         image = Image.open(img_path)
 62 |         if not os.path.exists(img_path):
 63 |             print("img not found")
 64 |             input()
 65 |             continue
 66 |         instruction = item["instruction"]
 67 |         bbox = item["bbox"]
 68 |         img_size = image.size
 69 |         # bbox = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
 70 |         # bbox = [bbox[0] / img_size[0], bbox[1] / img_size[1], bbox[2] / img_size[0], bbox[3] / img_size[1]]
 71 |         
 72 |         messages = [
 73 |             {
 74 |                 "role": "user",
 75 |                 "content": [
 76 |                     {
 77 |                         "type": "image",
 78 |                         "image": img_path,
 79 |                         "min_pixels": min_pixels,
 80 |                         "max_pixels": max_pixels,
 81 |                     },
 82 |                     {"type": "text", "text": f"In this UI screenshot, what is the position of the element corresponding to the command \"{instruction}\" (with bbox)?"},
 83 |                 ],
 84 |             }
 85 |         ]
 86 |         text = processor.apply_chat_template(
 87 |             messages, tokenize=False, add_generation_prompt=True
 88 |         )
 89 |         image_inputs, video_inputs = process_vision_info(messages)
 90 |         inputs = processor(
 91 |             text=[text],
 92 |             images=image_inputs,
 93 |             videos=video_inputs,
 94 |             padding=True,
 95 |             return_tensors="pt",
 96 |         )
 97 |         inputs = inputs.to("cuda")
 98 |         print(inputs.pixel_values.shape)
 99 |         # Inference: Generation of the output
100 |         generated_ids = model.generate(**inputs, max_new_tokens=128)
101 | 
102 |         generated_ids_trimmed = [
103 |             out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
104 |         ]
105 |         output_text = processor.batch_decode(
106 |             generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
107 |         )[0]
108 |         output_text = output_text.split('<|box_start|>')[-1].split('<|box_end|>')[0]
109 |         # print(output_text)
110 |         try:
111 |             if 'box' in output_text:
112 |                 pred_bbox = extract_bbox(output_text)
113 |                 print(pred_bbox)
114 |                 click_point = [(pred_bbox[0][0] + pred_bbox[1][0]) / 2, (pred_bbox[0][1] + pred_bbox[1][1]) / 2]
115 |                 click_point = [item / 1000 for item in click_point]
116 |             else:
117 |                 click_point = pred_2_point(output_text)
118 |                 click_point = [item / 1000 for item in click_point]
119 |             # check acc
120 |             if (bbox[0] <= click_point[0] <= bbox[2]) and (bbox[1] <= click_point[1] <= bbox[3]):
121 |                 corr_action += 1
122 |                 if item["data_type"] == 'text':
123 |                     text_correct.append(1)
124 |                 else:
125 |                     icon_correct.append(1)
126 |                 logging.info("match " + str(corr_action / num_action))
127 |             else:
128 |                 if item["data_type"] == 'text':
129 |                     text_correct.append(0)
130 |                 else:
131 |                     icon_correct.append(0)
132 |                 logging.info("unmatch " + str(corr_action / num_action))
133 |             # check asr
134 |             if (0.0 <= click_point[0] <= 0.02) and (0.0 <= click_point[1] <= 0.02):
135 |                 corr_action_asr += 1
136 |                 if item["data_type"] == 'text':
137 |                     text_correct_asr.append(1)
138 |                 else:
139 |                     icon_correct_asr.append(1)
140 |                 logging.info("attack sr " + str(corr_action_asr / num_action))
141 |             else:
142 |                 if item["data_type"] == 'text':
143 |                     text_correct_asr.append(0)
144 |                 else:
145 |                     icon_correct_asr.append(0)
146 |                 logging.info("attack fr " + str(corr_action_asr / num_action))
147 |             result.append({"img_path": img_path, "text": instruction, "click_point": click_point, "bbox": bbox, "img_size": img_size, "output_box": output_text,
148 |                            "type": item["data_type"], "source": item["data_source"]})
149 |         except:
150 |             num_wrong_format += 1
151 |             if item["data_type"] == 'text':
152 |                 text_correct.append(0)
153 |                 text_correct_asr.append(0)
154 |             else:
155 |                 icon_correct.append(0)
156 |                 icon_correct_asr.append(0)
157 |             logging.info("Step: " + str(j) + " wrong format")
158 | 
159 |     logging.info("Action ACC: " + str(corr_action / num_action))
160 |     logging.info("Total num: " + str(num_action))
161 |     logging.info("Wrong format num: " + str(num_wrong_format))
162 |     logging.info("Text ACC: " + str(sum(text_correct) / len(text_correct) if len(text_correct) != 0 else 0))
163 |     logging.info("Icon ACC: " + str(sum(icon_correct) / len(icon_correct) if len(icon_correct) != 0 else 0))
164 | 
165 |     text_acc = sum(text_correct) / len(text_correct) if len(text_correct) != 0 else 0
166 |     icon_acc = sum(icon_correct) / len(icon_correct) if len(icon_correct) != 0 else 0
167 |     tasks_result.append([text_acc, icon_acc])
168 | 
169 |     logging.info("Action ASR: " + str(corr_action_asr / num_action))
170 |     logging.info("Total num: " + str(num_action))
171 |     logging.info("Wrong format num: " + str(num_wrong_format))
172 |     logging.info("Text ASR: " + str(sum(text_correct_asr) / len(text_correct_asr) if len(text_correct_asr) != 0 else 0))
173 |     logging.info("Icon ASR: " + str(sum(icon_correct_asr) / len(icon_correct_asr) if len(icon_correct_asr) != 0 else 0))
174 | 
175 |     text_asr = sum(text_correct_asr) / len(text_correct_asr) if len(text_correct_asr) != 0 else 0
176 |     icon_asr = sum(icon_correct_asr) / len(icon_correct_asr) if len(icon_correct_asr) != 0 else 0
177 |     tasks_result_asr.append([text_asr, icon_asr])
178 | 
179 | logging.info(tasks_result)
180 | logging.info(tasks_result_asr)
181 | 
182 | with open("os_check_result.txt", 'a+') as f:
183 |     f.write(str(tasks_result)+'\n'+str(tasks_result_asr)+'\n')
184 | # json.dump(result, open("result_check_web.json", 'w'), indent=2)
185 | 


--------------------------------------------------------------------------------
/Uground/scripts/preprocess/data_process.py:
--------------------------------------------------------------------------------
  1 | import copy
  2 | import os
  3 | from dataclasses import dataclass, field
  4 | from typing import Dict
  5 | import torch
  6 | import transformers
  7 | import json
  8 | from torch.utils.data import Dataset
  9 | from qwen_vl_utils import process_vision_info
 10 | from PIL import Image
 11 | import re
 12 | 
 13 | from .params import DataArguments
 14 | from .constants import *
 15 | 
 16 | def truncate_sequence(input_ids, labels, max_length, eos_token_id):
 17 |     if input_ids.size(0) > max_length:
 18 |         input_ids = input_ids[:max_length-1]
 19 |         labels = labels[:max_length-1]
 20 | 
 21 |     if eos_token_id is not None:
 22 |         input_ids = torch.cat([input_ids, torch.tensor([eos_token_id])])
 23 |         labels = torch.cat([labels, torch.tensor([eos_token_id])])
 24 | 
 25 |     return input_ids, labels
 26 | 
 27 | def pad_sequence(sequences, padding_side='right', padding_value=0):
 28 |     """
 29 |     Pad a list of sequences to the same length.
 30 |     sequences: list of tensors in [seq_len, *] shape
 31 |     """
 32 |     assert padding_side in ['right', 'left']
 33 |     max_size = sequences[0].size()
 34 |     trailing_dims = max_size[1:]
 35 |     max_len = max(len(seq) for seq in sequences)
 36 |     # print(max_len)
 37 |     # max_len = 1200
 38 |     batch_size = len(sequences)
 39 |     output = sequences[0].new_full((batch_size, max_len) + trailing_dims, padding_value)
 40 |     for i, seq in enumerate(sequences):
 41 |         length = seq.size(0)
 42 |         if padding_side == 'right':
 43 |             output.data[i, :length] = seq
 44 |         else:
 45 |             output.data[i, -length:] = seq
 46 |     return output
 47 | 
 48 | def get_image_info(image_path, min_pixels, max_pixels):
 49 |     # Using this because of process_vision_info function
 50 |     # Need to fix this in the future    
 51 |     
 52 |     messages = [
 53 |         {"role": "user", 
 54 |          "content": [
 55 |              {
 56 |                 "type": "image", 
 57 |                 "image": image_path,
 58 |                 "min_pixels": min_pixels,
 59 |                 "max_pixels": max_pixels,
 60 | 
 61 |             }
 62 |             ]
 63 |         }
 64 |     ]
 65 | 
 66 |     image_input, _ = process_vision_info(messages)
 67 | 
 68 |     return image_input[0]
 69 | 
 70 | def get_video_info(video_path, min_pixels, max_pixels, fps):
 71 |     # Using this because of process_vision_info function
 72 |     # Need to fix this in the future
 73 | 
 74 |     messages = [
 75 |         {"role": "user", 
 76 |          "content": [
 77 |              {
 78 |                 "type": "video", 
 79 |                 "video": video_path,
 80 |                 "min_pixels": min_pixels,
 81 |                 "max_pixels": max_pixels,
 82 |                 "fps": fps
 83 |             }
 84 |             ]
 85 |         }
 86 |     ]
 87 | 
 88 |     _, video_input, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
 89 | 
 90 |     return video_input[0], video_kwargs
 91 | 
 92 | class SupervisedDataset(Dataset):
 93 |     """Dataset for supervised fine-tuning."""
 94 | 
 95 |     def __init__(
 96 |         self,
 97 |         data_path: str | list,
 98 |         processor: transformers.ProcessorMixin,
 99 |         data_args: DataArguments,
100 |         model_id,
101 |         padding=True,
102 |     ):
103 |         super(SupervisedDataset, self).__init__()
104 |         if isinstance(data_path, str):
105 |             list_data_dict = json.load(open(data_path, "r"))
106 |         else:
107 |             list_data_dict = data_path
108 | 
109 |         self.model_id = model_id
110 |         self.processor = processor
111 |         self.list_data_dict = list_data_dict
112 |         self.data_args = data_args
113 |         self.padding = padding
114 |         self.image_min_pixel = data_args.image_min_pixels
115 |         self.image_max_pixel = data_args.image_max_pixels
116 |         self.video_min_pixel = data_args.video_min_pixels
117 |         self.video_max_pixel = data_args.video_max_pixels
118 |         self.fps = data_args.fps
119 | 
120 |     def __len__(self):
121 |         return len(self.list_data_dict)
122 | 
123 |     def __getitem__(self, i) -> Dict[str, torch.Tensor]:
124 |         sources = self.list_data_dict[i]
125 | 
126 |         is_video = False
127 | 
128 |         processor = self.processor
129 |         if "image" in sources:
130 |             videos = None
131 |             grid_key = "image_grid_thw"
132 |             pixel_key = "pixel_values"
133 |             
134 |             image_files = sources["image"]
135 |             ids = sources["id"]
136 |             image_folder = self.data_args.image_folder
137 | 
138 |             if isinstance(image_files, str):
139 |                 image_files = [image_files]
140 | 
141 |             images = []
142 |             for image_file in image_files:
143 |                 if not os.path.exists(image_file):
144 |                     if not image_file.startswith("http"):
145 |                         image_file = os.path.join(image_folder, image_file)
146 |                 images.append(get_image_info(image_file, self.image_min_pixel, self.image_max_pixel))
147 |             
148 | 
149 |         elif "video" in sources:
150 |             is_video = True
151 |             images=None
152 |             grid_key = "video_grid_thw"
153 |             pixel_key = "pixel_values_videos"
154 | 
155 |             video_files = sources["video"]
156 |             video_folder = self.data_args.image_folder
157 | 
158 |             if isinstance(video_files, str):
159 |                 video_files = [video_files]
160 | 
161 |             videos = []
162 |             for video_file in video_files:
163 |                 if not os.path.exists(video_file):
164 |                     if not video_file.startswith("http"):
165 |                         video_file = os.path.join(video_folder, video_file)
166 |                 video_input, video_kwargs = get_video_info(video_file, self.video_min_pixel, self.video_max_pixel, self.data_args.fps)
167 |                 videos.append(video_input)
168 |         else:
169 |             grid_key = None
170 |             pixel_key = None
171 |             images=None
172 |             videos=None
173 | 
174 |         sources = copy.deepcopy(llava_to_openai(sources['conversations'], is_video=is_video))
175 | 
176 |         all_input_ids = [] 
177 |         all_labels = []
178 |         all_pixel_values = []
179 |         all_image_grid_thw = []
180 |         all_second_gird = []
181 | 
182 |         # all_user_inputs = []
183 | 
184 |         # Qwen2-VL uses a default system message so I've added this.
185 |         if len(SYSTEM_MESSAGE) > 0:
186 |             # system_message = f"{DEFAULT_IM_START_TOKEN}system\n{SYSTEM_MESSAGE}\n{DEFAULT_IM_END_TOKEN}\n"
187 |             system_message = f"{DEFAULT_IM_START_TOKEN}system\n{SYSTEM_MESSAGE}{DEFAULT_IM_END_TOKEN}\n"
188 |             system_message_input_ids = processor.tokenizer(system_message, add_special_tokens=False, return_tensors='pt')['input_ids']
189 |             system_labels = torch.full_like(system_message_input_ids, IGNORE_INDEX) 
190 |             
191 |             all_input_ids.append(system_message_input_ids.squeeze(0))
192 |             all_labels.append(system_labels.squeeze(0))
193 | 
194 |         for _, j in enumerate(range(0, len(sources), 2)):
195 |             user_input = sources[j]
196 |             
197 |             gpt_response = sources[j + 1]
198 | 
199 |             # user_input = f"{DEFAULT_IM_START_TOKEN}{user_input['role']}\n{user_input['content']}\n{DEFAULT_IM_END_TOKEN}\n{DEFAULT_IM_START_TOKEN}{gpt_response['role']}\n"
200 |             user_input = f"{DEFAULT_IM_START_TOKEN}{user_input['role']}\n{user_input['content']}{DEFAULT_IM_END_TOKEN}\n{DEFAULT_IM_START_TOKEN}{gpt_response['role']}\n"
201 |             # all_user_inputs.append(user_input)
202 |             # gpt_response = f"{gpt_response['content']}\n{DEFAULT_IM_END_TOKEN}\n"
203 |             gpt_response = f"{gpt_response['content']}{DEFAULT_IM_END_TOKEN}\n"
204 |             
205 |             if DEFAULT_IMAGE_TOKEN in user_input:
206 |                 # pass
207 |                 # print(images)
208 |                 inputs = processor(text=[user_input], images=images, videos=videos, padding=False, return_tensors='pt')
209 |                 # processed_images, resized_height, resized_width = processor.image_processor._process_p1(images=images)
210 |                 # inputs = inputs = processor(
211 |                 #     text=[user_input],
212 |                 #     images=None,
213 |                 #     resized_height = resized_height,
214 |                 #     resized_width = resized_width,
215 |                 #     processed_images = processed_images,
216 |                 #     videos=videos,
217 |                 #     padding=False,
218 |                 #     return_tensors="pt",
219 |                 # )
220 |                 prompt_input_ids = inputs['input_ids']
221 |                 all_pixel_values.append(inputs[pixel_key])
222 |                 all_image_grid_thw.append(inputs[grid_key])
223 |             
224 |             elif DEFAULT_VIDEO_TOKEN in user_input:
225 |                 if "Qwen2.5" in self.model_id:
226 |                     inputs = processor(text=[user_input], images=images, videos=videos, padding=False, return_tensors='pt', **video_kwargs)
227 |                     all_second_gird.extend(inputs["second_per_grid_ts"])
228 |                 else:
229 |                     inputs = processor(text=[user_input], images=images, videos=videos, padding=False, return_tensors='pt')
230 |                 prompt_input_ids = inputs['input_ids']
231 |                 all_pixel_values.append(inputs[pixel_key])
232 |                 all_image_grid_thw.append(inputs[grid_key])
233 | 
234 |             else:
235 |                 prompt_input_ids = processor.tokenizer(user_input, add_special_tokens=False, padding=False, return_tensors='pt')['input_ids']
236 | 
237 |             response_input_ids = processor.tokenizer(gpt_response, add_special_tokens=False, padding=False, return_tensors='pt')['input_ids']
238 | 
239 |             input_ids = torch.cat([prompt_input_ids, response_input_ids], dim=1).squeeze(0)
240 |             labels = torch.cat(
241 |                 [
242 |                     torch.tensor([IGNORE_INDEX] * len(prompt_input_ids[0])),  
243 |                     response_input_ids.squeeze(0),
244 |                 ],
245 |                 dim=0,
246 |             )
247 | 
248 |             all_input_ids.append(input_ids)
249 |             all_labels.append(labels)
250 |         
251 |         # There is no need for eos or bos tokens in the input_ids
252 |         # Qwen2-VL does not use them
253 |         input_ids = torch.cat(all_input_ids, dim=0).to(torch.long)
254 |         labels = torch.cat(all_labels, dim=0).to(torch.long)
255 | 
256 | 
257 |         # eos_token_id = processor.tokenizer.convert_tokens_to_ids(DEFAULT_IM_END_TOKEN)
258 |         # input_ids, labels = truncate_sequence(input_ids, labels, self.max_length, eos_token_id)
259 | 
260 |         attention_mask = (input_ids > -1000000).to(torch.long)
261 | 
262 |         data_dict = dict(
263 |             input_ids=input_ids,
264 |             attention_mask=attention_mask,
265 |             labels=labels,
266 |             image_files=image_files,
267 |             ids=ids
268 |             # images=images,
269 |             # user_inputs=all_user_inputs
270 |         )
271 | 
272 |         if pixel_key and grid_key:
273 |             pixel_values = torch.cat(all_pixel_values, dim=0)
274 |             image_thw = torch.cat(all_image_grid_thw, dim=0)
275 |             data_dict[pixel_key] = pixel_values
276 |             data_dict[grid_key] = image_thw
277 | 
278 |         if len(all_second_gird) > 0:
279 |             second_gird = all_second_gird
280 |             data_dict["second_per_grid_ts"] = second_gird
281 |         
282 |         return data_dict
283 | 
284 | class DataCollatorForSupervisedDataset(object):
285 |     """Collate examples for supervised fine-tuning."""
286 | 
287 |     def __init__(self, pad_token_id: int):
288 |         self.pad_token_id = pad_token_id
289 | 
290 |     def __call__(self, examples):
291 |         batch_input_ids = []
292 |         batch_label_ids = []
293 |         batch_pixel_values = []
294 |         batch_pixel_video_values = []
295 |         batch_video_thw = []
296 |         batch_image_thw = []
297 |         batch_second_per_grid_ts = []
298 |         # batch_images = []
299 |         # batch_user_inputs = []
300 |         batch_filename = []
301 |         batch_ids = []
302 | 
303 |         for example in examples:
304 |             keys = example.keys()
305 |             if "pixel_values_videos" in keys:
306 |                 batch_pixel_video_values.append(example["pixel_values_videos"])
307 |                 batch_video_thw.append(example["video_grid_thw"])
308 |             elif "pixel_values" in keys:
309 |                 batch_pixel_values.append(example["pixel_values"])
310 |                 batch_image_thw.append(example["image_grid_thw"])
311 |             
312 |             batch_input_ids.append(example["input_ids"])
313 |             batch_label_ids.append(example["labels"])
314 | 
315 |             # batch_images.append(example["images"])
316 |             # batch_user_inputs.append(example["user_inputs"])
317 |             batch_filename.append(example["image_files"])
318 |             batch_ids.append(example["ids"])
319 | 
320 |             if "second_per_grid_ts" in keys:
321 |                 batch_second_per_grid_ts.extend(example["second_per_grid_ts"])
322 |         
323 |         input_ids = pad_sequence(
324 |             batch_input_ids, padding_side='right', padding_value=self.pad_token_id
325 |         )
326 | 
327 |         attention_mask = input_ids != self.pad_token_id
328 |         labels = pad_sequence(batch_label_ids, padding_side='right', padding_value=IGNORE_INDEX)
329 | 
330 |         data_dict = {
331 |             'input_ids': input_ids,
332 |             'labels': labels,
333 |             'attention_mask': attention_mask,
334 |             "filenames": batch_filename,
335 |             "ids": batch_ids
336 |         }
337 | 
338 |         # images = torch.cat(batch_images)
339 |         # data_dict["images"] = batch_images
340 | 
341 |         # data_dict["user_inputs"] = batch_user_inputs
342 | 
343 |         if len(batch_pixel_values) > 0:
344 |             pixel_values = torch.cat(batch_pixel_values, dim=0)
345 |             image_thw = torch.cat(batch_image_thw, dim=0)
346 |             data_dict["pixel_values"] = pixel_values
347 |             data_dict["image_grid_thw"] = image_thw
348 | 
349 |         if len(batch_pixel_video_values) > 0:
350 |             pixel_video_values = torch.cat(batch_pixel_video_values, dim=0)
351 |             video_thw = torch.cat(batch_video_thw, dim=0)
352 |             data_dict["pixel_values_videos"] = pixel_video_values
353 |             data_dict["video_grid_thw"] = video_thw
354 | 
355 |         if len(batch_second_per_grid_ts) > 0:
356 |             data_dict["second_per_grid_ts"] = batch_second_per_grid_ts
357 | 
358 |         return data_dict
359 |     
360 | 
361 | def replace_image_tokens(input_string, is_video=False):
362 |     if is_video:
363 |         pattern = r'\n?' + re.escape(LLAVA_VIDEO_TOKEN) + r'\n?'
364 |         replacement = VISION_START_TOKEN + DEFAULT_VIDEO_TOKEN + VISION_END_TOKEN
365 |     else:
366 |         pattern = r'\n?' + re.escape(LLAVA_IMAGE_TOKEN) + r'\n?'
367 |         replacement = VISION_START_TOKEN + DEFAULT_IMAGE_TOKEN + VISION_END_TOKEN
368 | 
369 |     return re.sub(pattern, replacement, input_string)
370 | 
371 | def llava_to_openai(conversations, is_video=False):
372 |     role_mapping = {"human": "user", "gpt": "assistant"}
373 | 
374 |     transformed_data = []
375 |     for conversation in conversations:
376 |         transformed_content = replace_image_tokens(conversation["value"], is_video=is_video)
377 |         transformed_entry = {
378 |             "role": role_mapping.get(conversation["from"], conversation["from"]),
379 |             "content": transformed_content,
380 |         }
381 |         transformed_data.append(transformed_entry)
382 | 
383 |     return transformed_data
384 | 
385 | def make_supervised_data_module(model_id, processor, data_args):
386 |     """Make dataset and collator for supervised fine-tuning."""
387 |     sft_dataset = SupervisedDataset(
388 |         data_path=data_args.data_path, processor=processor, data_args=data_args, model_id=model_id
389 |     )
390 |     data_collator = DataCollatorForSupervisedDataset(pad_token_id=processor.tokenizer.pad_token_id)
391 | 
392 |     return dict(train_dataset=sft_dataset,
393 |                 eval_dataset=None,
394 |                 data_collator=data_collator)


--------------------------------------------------------------------------------
/OS-Atlas-7B/scripts/preprocess/data_process.py:
--------------------------------------------------------------------------------
  1 | import copy
  2 | import os
  3 | from dataclasses import dataclass, field
  4 | from typing import Dict
  5 | import torch
  6 | import transformers
  7 | import json
  8 | from torch.utils.data import Dataset
  9 | from qwen_vl_utils import process_vision_info
 10 | from PIL import Image
 11 | import re
 12 | 
 13 | from .params import DataArguments
 14 | from .constants import *
 15 | 
 16 | def truncate_sequence(input_ids, labels, max_length, eos_token_id):
 17 |     if input_ids.size(0) > max_length:
 18 |         input_ids = input_ids[:max_length-1]
 19 |         labels = labels[:max_length-1]
 20 | 
 21 |     if eos_token_id is not None:
 22 |         input_ids = torch.cat([input_ids, torch.tensor([eos_token_id])])
 23 |         labels = torch.cat([labels, torch.tensor([eos_token_id])])
 24 | 
 25 |     return input_ids, labels
 26 | 
 27 | def pad_sequence(sequences, padding_side='right', padding_value=0):
 28 |     """
 29 |     Pad a list of sequences to the same length.
 30 |     sequences: list of tensors in [seq_len, *] shape
 31 |     """
 32 |     assert padding_side in ['right', 'left']
 33 |     max_size = sequences[0].size()
 34 |     trailing_dims = max_size[1:]
 35 |     max_len = max(len(seq) for seq in sequences)
 36 |     # print(max_len)
 37 |     # max_len = 1200
 38 |     batch_size = len(sequences)
 39 |     output = sequences[0].new_full((batch_size, max_len) + trailing_dims, padding_value)
 40 |     for i, seq in enumerate(sequences):
 41 |         length = seq.size(0)
 42 |         if padding_side == 'right':
 43 |             output.data[i, :length] = seq
 44 |         else:
 45 |             output.data[i, -length:] = seq
 46 |     return output
 47 | 
 48 | def get_image_info(image_path, min_pixels, max_pixels):
 49 |     # Using this because of process_vision_info function
 50 |     # Need to fix this in the future    
 51 |     
 52 |     messages = [
 53 |         {"role": "user", 
 54 |          "content": [
 55 |              {
 56 |                 "type": "image", 
 57 |                 "image": image_path,
 58 |                 "min_pixels": min_pixels,
 59 |                 "max_pixels": max_pixels,
 60 | 
 61 |             }
 62 |             ]
 63 |         }
 64 |     ]
 65 | 
 66 |     image_input, _ = process_vision_info(messages)
 67 | 
 68 |     return image_input[0]
 69 | 
 70 | def get_video_info(video_path, min_pixels, max_pixels, fps):
 71 |     # Using this because of process_vision_info function
 72 |     # Need to fix this in the future
 73 | 
 74 |     messages = [
 75 |         {"role": "user", 
 76 |          "content": [
 77 |              {
 78 |                 "type": "video", 
 79 |                 "video": video_path,
 80 |                 "min_pixels": min_pixels,
 81 |                 "max_pixels": max_pixels,
 82 |                 "fps": fps
 83 |             }
 84 |             ]
 85 |         }
 86 |     ]
 87 | 
 88 |     _, video_input, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
 89 | 
 90 |     return video_input[0], video_kwargs
 91 | 
 92 | class SupervisedDataset(Dataset):
 93 |     """Dataset for supervised fine-tuning."""
 94 | 
 95 |     def __init__(
 96 |         self,
 97 |         data_path: str | list,
 98 |         processor: transformers.ProcessorMixin,
 99 |         data_args: DataArguments,
100 |         model_id,
101 |         padding=True,
102 |     ):
103 |         super(SupervisedDataset, self).__init__()
104 |         if isinstance(data_path, str):
105 |             list_data_dict = json.load(open(data_path, "r"))
106 |         else:
107 |             list_data_dict = data_path
108 | 
109 |         self.model_id = model_id
110 |         self.processor = processor
111 |         self.list_data_dict = list_data_dict
112 |         self.data_args = data_args
113 |         self.padding = padding
114 |         self.image_min_pixel = data_args.image_min_pixels
115 |         self.image_max_pixel = data_args.image_max_pixels
116 |         self.video_min_pixel = data_args.video_min_pixels
117 |         self.video_max_pixel = data_args.video_max_pixels
118 |         self.fps = data_args.fps
119 | 
120 |     def __len__(self):
121 |         return len(self.list_data_dict)
122 | 
123 |     def __getitem__(self, i) -> Dict[str, torch.Tensor]:
124 |         sources = self.list_data_dict[i]
125 | 
126 |         is_video = False
127 | 
128 |         processor = self.processor
129 |         if "image" in sources:
130 |             videos = None
131 |             grid_key = "image_grid_thw"
132 |             pixel_key = "pixel_values"
133 |             
134 |             image_files = sources["image"]
135 |             ids = sources["id"]
136 |             image_folder = self.data_args.image_folder
137 | 
138 |             if isinstance(image_files, str):
139 |                 image_files = [image_files]
140 | 
141 |             images = []
142 |             for image_file in image_files:
143 |                 if not os.path.exists(image_file):
144 |                     if not image_file.startswith("http"):
145 |                         image_file = os.path.join(image_folder, image_file)
146 |                 images.append(get_image_info(image_file, self.image_min_pixel, self.image_max_pixel))
147 |             
148 | 
149 |         elif "video" in sources:
150 |             is_video = True
151 |             images=None
152 |             grid_key = "video_grid_thw"
153 |             pixel_key = "pixel_values_videos"
154 | 
155 |             video_files = sources["video"]
156 |             video_folder = self.data_args.image_folder
157 | 
158 |             if isinstance(video_files, str):
159 |                 video_files = [video_files]
160 | 
161 |             videos = []
162 |             for video_file in video_files:
163 |                 if not os.path.exists(video_file):
164 |                     if not video_file.startswith("http"):
165 |                         video_file = os.path.join(video_folder, video_file)
166 |                 video_input, video_kwargs = get_video_info(video_file, self.video_min_pixel, self.video_max_pixel, self.data_args.fps)
167 |                 videos.append(video_input)
168 |         else:
169 |             grid_key = None
170 |             pixel_key = None
171 |             images=None
172 |             videos=None
173 | 
174 |         sources = copy.deepcopy(llava_to_openai(sources['conversations'], is_video=is_video))
175 | 
176 |         all_input_ids = [] 
177 |         all_labels = []
178 |         all_pixel_values = []
179 |         all_image_grid_thw = []
180 |         all_second_gird = []
181 | 
182 |         # all_user_inputs = []
183 | 
184 |         # Qwen2-VL uses a default system message so I've added this.
185 |         if len(SYSTEM_MESSAGE) > 0:
186 |             # system_message = f"{DEFAULT_IM_START_TOKEN}system\n{SYSTEM_MESSAGE}\n{DEFAULT_IM_END_TOKEN}\n"
187 |             system_message = f"{DEFAULT_IM_START_TOKEN}system\n{SYSTEM_MESSAGE}{DEFAULT_IM_END_TOKEN}\n"
188 |             system_message_input_ids = processor.tokenizer(system_message, add_special_tokens=False, return_tensors='pt')['input_ids']
189 |             system_labels = torch.full_like(system_message_input_ids, IGNORE_INDEX) 
190 |             
191 |             all_input_ids.append(system_message_input_ids.squeeze(0))
192 |             all_labels.append(system_labels.squeeze(0))
193 | 
194 |         for _, j in enumerate(range(0, len(sources), 2)):
195 |             user_input = sources[j]
196 |             
197 |             gpt_response = sources[j + 1]
198 | 
199 |             # user_input = f"{DEFAULT_IM_START_TOKEN}{user_input['role']}\n{user_input['content']}\n{DEFAULT_IM_END_TOKEN}\n{DEFAULT_IM_START_TOKEN}{gpt_response['role']}\n"
200 |             user_input = f"{DEFAULT_IM_START_TOKEN}{user_input['role']}\n{user_input['content']}{DEFAULT_IM_END_TOKEN}\n{DEFAULT_IM_START_TOKEN}{gpt_response['role']}\n"
201 |             # all_user_inputs.append(user_input)
202 |             # gpt_response = f"{gpt_response['content']}\n{DEFAULT_IM_END_TOKEN}\n"
203 |             gpt_response = f"{gpt_response['content']}{DEFAULT_IM_END_TOKEN}\n"
204 |             
205 |             if DEFAULT_IMAGE_TOKEN in user_input:
206 |                 # pass
207 |                 # print(images)
208 |                 inputs = processor(text=[user_input], images=images, videos=videos, padding=False, return_tensors='pt')
209 |                 # processed_images, resized_height, resized_width = processor.image_processor._process_p1(images=images)
210 |                 # inputs = inputs = processor(
211 |                 #     text=[user_input],
212 |                 #     images=None,
213 |                 #     resized_height = resized_height,
214 |                 #     resized_width = resized_width,
215 |                 #     processed_images = processed_images,
216 |                 #     videos=videos,
217 |                 #     padding=False,
218 |                 #     return_tensors="pt",
219 |                 # )
220 |                 prompt_input_ids = inputs['input_ids']
221 |                 all_pixel_values.append(inputs[pixel_key])
222 |                 all_image_grid_thw.append(inputs[grid_key])
223 |             
224 |             elif DEFAULT_VIDEO_TOKEN in user_input:
225 |                 if "Qwen2.5" in self.model_id:
226 |                     inputs = processor(text=[user_input], images=images, videos=videos, padding=False, return_tensors='pt', **video_kwargs)
227 |                     all_second_gird.extend(inputs["second_per_grid_ts"])
228 |                 else:
229 |                     inputs = processor(text=[user_input], images=images, videos=videos, padding=False, return_tensors='pt')
230 |                 prompt_input_ids = inputs['input_ids']
231 |                 all_pixel_values.append(inputs[pixel_key])
232 |                 all_image_grid_thw.append(inputs[grid_key])
233 | 
234 |             else:
235 |                 prompt_input_ids = processor.tokenizer(user_input, add_special_tokens=False, padding=False, return_tensors='pt')['input_ids']
236 | 
237 |             response_input_ids = processor.tokenizer(gpt_response, add_special_tokens=False, padding=False, return_tensors='pt')['input_ids']
238 | 
239 |             input_ids = torch.cat([prompt_input_ids, response_input_ids], dim=1).squeeze(0)
240 |             labels = torch.cat(
241 |                 [
242 |                     torch.tensor([IGNORE_INDEX] * len(prompt_input_ids[0])),  
243 |                     response_input_ids.squeeze(0),
244 |                 ],
245 |                 dim=0,
246 |             )
247 | 
248 |             all_input_ids.append(input_ids)
249 |             all_labels.append(labels)
250 |         
251 |         # There is no need for eos or bos tokens in the input_ids
252 |         # Qwen2-VL does not use them
253 |         input_ids = torch.cat(all_input_ids, dim=0).to(torch.long)
254 |         labels = torch.cat(all_labels, dim=0).to(torch.long)
255 | 
256 | 
257 |         # eos_token_id = processor.tokenizer.convert_tokens_to_ids(DEFAULT_IM_END_TOKEN)
258 |         # input_ids, labels = truncate_sequence(input_ids, labels, self.max_length, eos_token_id)
259 | 
260 |         attention_mask = (input_ids > -1000000).to(torch.long)
261 | 
262 |         data_dict = dict(
263 |             input_ids=input_ids,
264 |             attention_mask=attention_mask,
265 |             labels=labels,
266 |             image_files=image_files,
267 |             ids=ids
268 |             # images=images,
269 |             # user_inputs=all_user_inputs
270 |         )
271 | 
272 |         if pixel_key and grid_key:
273 |             pixel_values = torch.cat(all_pixel_values, dim=0)
274 |             image_thw = torch.cat(all_image_grid_thw, dim=0)
275 |             data_dict[pixel_key] = pixel_values
276 |             data_dict[grid_key] = image_thw
277 | 
278 |         if len(all_second_gird) > 0:
279 |             second_gird = all_second_gird
280 |             data_dict["second_per_grid_ts"] = second_gird
281 |         
282 |         return data_dict
283 | 
284 | class DataCollatorForSupervisedDataset(object):
285 |     """Collate examples for supervised fine-tuning."""
286 | 
287 |     def __init__(self, pad_token_id: int):
288 |         self.pad_token_id = pad_token_id
289 | 
290 |     def __call__(self, examples):
291 |         batch_input_ids = []
292 |         batch_label_ids = []
293 |         batch_pixel_values = []
294 |         batch_pixel_video_values = []
295 |         batch_video_thw = []
296 |         batch_image_thw = []
297 |         batch_second_per_grid_ts = []
298 |         # batch_images = []
299 |         # batch_user_inputs = []
300 |         batch_filename = []
301 |         batch_ids = []
302 | 
303 |         for example in examples:
304 |             keys = example.keys()
305 |             if "pixel_values_videos" in keys:
306 |                 batch_pixel_video_values.append(example["pixel_values_videos"])
307 |                 batch_video_thw.append(example["video_grid_thw"])
308 |             elif "pixel_values" in keys:
309 |                 batch_pixel_values.append(example["pixel_values"])
310 |                 batch_image_thw.append(example["image_grid_thw"])
311 |             
312 |             batch_input_ids.append(example["input_ids"])
313 |             batch_label_ids.append(example["labels"])
314 | 
315 |             # batch_images.append(example["images"])
316 |             # batch_user_inputs.append(example["user_inputs"])
317 |             batch_filename.append(example["image_files"])
318 |             batch_ids.append(example["ids"])
319 | 
320 |             if "second_per_grid_ts" in keys:
321 |                 batch_second_per_grid_ts.extend(example["second_per_grid_ts"])
322 |         
323 |         input_ids = pad_sequence(
324 |             batch_input_ids, padding_side='right', padding_value=self.pad_token_id
325 |         )
326 | 
327 |         attention_mask = input_ids != self.pad_token_id
328 |         labels = pad_sequence(batch_label_ids, padding_side='right', padding_value=IGNORE_INDEX)
329 | 
330 |         data_dict = {
331 |             'input_ids': input_ids,
332 |             'labels': labels,
333 |             'attention_mask': attention_mask,
334 |             "filenames": batch_filename,
335 |             "ids": batch_ids
336 |         }
337 | 
338 |         # images = torch.cat(batch_images)
339 |         # data_dict["images"] = batch_images
340 | 
341 |         # data_dict["user_inputs"] = batch_user_inputs
342 | 
343 |         if len(batch_pixel_values) > 0:
344 |             pixel_values = torch.cat(batch_pixel_values, dim=0)
345 |             image_thw = torch.cat(batch_image_thw, dim=0)
346 |             data_dict["pixel_values"] = pixel_values
347 |             data_dict["image_grid_thw"] = image_thw
348 | 
349 |         if len(batch_pixel_video_values) > 0:
350 |             pixel_video_values = torch.cat(batch_pixel_video_values, dim=0)
351 |             video_thw = torch.cat(batch_video_thw, dim=0)
352 |             data_dict["pixel_values_videos"] = pixel_video_values
353 |             data_dict["video_grid_thw"] = video_thw
354 | 
355 |         if len(batch_second_per_grid_ts) > 0:
356 |             data_dict["second_per_grid_ts"] = batch_second_per_grid_ts
357 | 
358 |         return data_dict
359 |     
360 | 
361 | def replace_image_tokens(input_string, is_video=False):
362 |     if is_video:
363 |         pattern = r'\n?' + re.escape(LLAVA_VIDEO_TOKEN) + r'\n?'
364 |         replacement = VISION_START_TOKEN + DEFAULT_VIDEO_TOKEN + VISION_END_TOKEN
365 |     else:
366 |         pattern = r'\n?' + re.escape(LLAVA_IMAGE_TOKEN) + r'\n?'
367 |         replacement = VISION_START_TOKEN + DEFAULT_IMAGE_TOKEN + VISION_END_TOKEN
368 | 
369 |     return re.sub(pattern, replacement, input_string)
370 | 
371 | def llava_to_openai(conversations, is_video=False):
372 |     role_mapping = {"human": "user", "gpt": "assistant"}
373 | 
374 |     transformed_data = []
375 |     for conversation in conversations:
376 |         transformed_content = replace_image_tokens(conversation["value"], is_video=is_video)
377 |         transformed_entry = {
378 |             "role": role_mapping.get(conversation["from"], conversation["from"]),
379 |             "content": transformed_content,
380 |         }
381 |         transformed_data.append(transformed_entry)
382 | 
383 |     return transformed_data
384 | 
385 | def make_supervised_data_module(model_id, processor, data_args):
386 |     """Make dataset and collator for supervised fine-tuning."""
387 |     sft_dataset = SupervisedDataset(
388 |         data_path=data_args.data_path, processor=processor, data_args=data_args, model_id=model_id
389 |     )
390 |     data_collator = DataCollatorForSupervisedDataset(pad_token_id=processor.tokenizer.pad_token_id)
391 | 
392 |     return dict(train_dataset=sft_dataset,
393 |                 eval_dataset=None,
394 |                 data_collator=data_collator)


--------------------------------------------------------------------------------
/datas/screenspot_desktop_v2.json:
--------------------------------------------------------------------------------
   1 | [
   2 |   {
   3 |     "img_filename": "pc_ede36f9b-1154-4f76-b7f8-c15d7d3f9b6e.png",
   4 |     "bbox": [
   5 |       910,
   6 |       78,
   7 |       44,
   8 |       34
   9 |     ],
  10 |     "instruction": "close this window",
  11 |     "data_type": "icon",
  12 |     "data_source": "windows"
  13 |   },
  14 |   {
  15 |     "img_filename": "pc_5e09e8e9-7ef6-44be-ad54-aeaeac151897.png",
  16 |     "bbox": [
  17 |       835,
  18 |       0,
  19 |       36,
  20 |       30
  21 |     ],
  22 |     "instruction": "minimize this window",
  23 |     "data_type": "icon",
  24 |     "data_source": "windows"
  25 |   },
  26 |   {
  27 |     "img_filename": "pc_2c6dc6ea-fd6f-4cbb-9954-2d6a68610a48.png",
  28 |     "bbox": [
  29 |       1477,
  30 |       450,
  31 |       184,
  32 |       230
  33 |     ],
  34 |     "instruction": "view solitaire daily challenges",
  35 |     "data_type": "text",
  36 |     "data_source": "windows"
  37 |   },
  38 |   {
  39 |     "img_filename": "pc_b072d03a-51cb-4dd6-bdeb-152d3fd44e6b.png",
  40 |     "bbox": [
  41 |       176,
  42 |       202,
  43 |       99,
  44 |       101
  45 |     ],
  46 |     "instruction": "open the Microsoft store",
  47 |     "data_type": "text",
  48 |     "data_source": "windows"
  49 |   },
  50 |   {
  51 |     "img_filename": "pc_ac2d2c5e-fe5a-4233-95b4-2de18ef76a9e.png",
  52 |     "bbox": [
  53 |       635,
  54 |       360,
  55 |       42,
  56 |       63
  57 |     ],
  58 |     "instruction": "adjust the style",
  59 |     "data_type": "text",
  60 |     "data_source": "windows"
  61 |   },
  62 |   {
  63 |     "img_filename": "pc_f0122b91-3f8b-4510-9a6e-f0239d84b94e.png",
  64 |     "bbox": [
  65 |       697,
  66 |       63,
  67 |       34,
  68 |       32
  69 |     ],
  70 |     "instruction": "minimize calculator",
  71 |     "data_type": "icon",
  72 |     "data_source": "windows"
  73 |   },
  74 |   {
  75 |     "img_filename": "pc_354a6cb2-3fe0-4cea-950c-d7f8e01fdf45.png",
  76 |     "bbox": [
  77 |       843,
  78 |       7,
  79 |       48,
  80 |       39
  81 |     ],
  82 |     "instruction": "open the download page",
  83 |     "data_type": "icon",
  84 |     "data_source": "windows"
  85 |   },
  86 |   {
  87 |     "img_filename": "pc_d6b09201-c944-4ad9-a0a2-ff78e8f056ce.png",
  88 |     "bbox": [
  89 |       1,
  90 |       434,
  91 |       67,
  92 |       66
  93 |     ],
  94 |     "instruction": "open the help menu",
  95 |     "data_type": "text",
  96 |     "data_source": "windows"
  97 |   },
  98 |   {
  99 |     "img_filename": "pc_db6b6fa2-11cc-4b06-88cb-22c180f07a3b.png",
 100 |     "bbox": [
 101 |       9,
 102 |       0,
 103 |       35,
 104 |       31
 105 |     ],
 106 |     "instruction": "save the file",
 107 |     "data_type": "icon",
 108 |     "data_source": "windows"
 109 |   },
 110 |   {
 111 |     "img_filename": "pc_6dfa677c-7772-46fb-8c83-14a33b0806b4.png",
 112 |     "bbox": [
 113 |       316,
 114 |       0,
 115 |       601,
 116 |       38
 117 |     ],
 118 |     "instruction": "search for software ",
 119 |     "data_type": "text",
 120 |     "data_source": "windows"
 121 |   },
 122 |   {
 123 |     "img_filename": "pc_bc9ed2f6-79c1-4a35-bcb5-4760bf4ed57d.png",
 124 |     "bbox": [
 125 |       880,
 126 |       6,
 127 |       36,
 128 |       31
 129 |     ],
 130 |     "instruction": "add this page to favourites",
 131 |     "data_type": "icon",
 132 |     "data_source": "windows"
 133 |   },
 134 |   {
 135 |     "img_filename": "pc_2c56f301-8e71-4498-8e52-44b37ac1a298.png",
 136 |     "bbox": [
 137 |       458,
 138 |       133,
 139 |       452,
 140 |       36
 141 |     ],
 142 |     "instruction": "choose language for windows",
 143 |     "data_type": "icon",
 144 |     "data_source": "windows"
 145 |   },
 146 |   {
 147 |     "img_filename": "pc_e618dc7e-8ffb-4a37-a317-9d3908c38395.png",
 148 |     "bbox": [
 149 |       883,
 150 |       496,
 151 |       62,
 152 |       44
 153 |     ],
 154 |     "instruction": "input method toolbar",
 155 |     "data_type": "icon",
 156 |     "data_source": "windows"
 157 |   },
 158 |   {
 159 |     "img_filename": "pc_54779155-8627-4cac-8ea6-98277cfe0647.png",
 160 |     "bbox": [
 161 |       778,
 162 |       4,
 163 |       38,
 164 |       41
 165 |     ],
 166 |     "instruction": "access the personal account interface",
 167 |     "data_type": "icon",
 168 |     "data_source": "windows"
 169 |   },
 170 |   {
 171 |     "img_filename": "pc_ad16765f-de52-4903-b406-fb5213b94db9.png",
 172 |     "bbox": [
 173 |       924,
 174 |       496,
 175 |       33,
 176 |       44
 177 |     ],
 178 |     "instruction": "network and internet settings",
 179 |     "data_type": "icon",
 180 |     "data_source": "windows"
 181 |   },
 182 |   {
 183 |     "img_filename": "pc_1ebb6beb-3187-45a6-a5a6-e50aa9a327e5.png",
 184 |     "bbox": [
 185 |       813,
 186 |       0,
 187 |       34,
 188 |       32
 189 |     ],
 190 |     "instruction": "open a new tab",
 191 |     "data_type": "icon",
 192 |     "data_source": "windows"
 193 |   },
 194 |   {
 195 |     "img_filename": "pc_221d3c5a-0a1b-456e-9676-7b5ff95cc1b1.png",
 196 |     "bbox": [
 197 |       10,
 198 |       84,
 199 |       50,
 200 |       39
 201 |     ],
 202 |     "instruction": "click the file processing menu",
 203 |     "data_type": "text",
 204 |     "data_source": "windows"
 205 |   },
 206 |   {
 207 |     "img_filename": "pc_a8b0f843-7aaf-4369-ac2b-a121f1ccb024.png",
 208 |     "bbox": [
 209 |       1,
 210 |       30,
 211 |       41,
 212 |       39
 213 |     ],
 214 |     "instruction": "open settings",
 215 |     "data_type": "icon",
 216 |     "data_source": "windows"
 217 |   },
 218 |   {
 219 |     "img_filename": "pc_7f0b3e40-e5f5-462b-96c3-e8a97a3a504b.png",
 220 |     "bbox": [
 221 |       2,
 222 |       48,
 223 |       319,
 224 |       40
 225 |     ],
 226 |     "instruction": "open the music library",
 227 |     "data_type": "text",
 228 |     "data_source": "windows"
 229 |   },
 230 |   {
 231 |     "img_filename": "pc_8f88d498-d49a-4ab5-8aa6-aafa1428c3b7.png",
 232 |     "bbox": [
 233 |       177,
 234 |       69,
 235 |       282,
 236 |       28
 237 |     ],
 238 |     "instruction": "copy the file",
 239 |     "data_type": "text",
 240 |     "data_source": "windows"
 241 |   },
 242 |   {
 243 |     "img_filename": "pc_870d6da1-bc85-4e74-a1bd-ef6f975c2146.png",
 244 |     "bbox": [
 245 |       855,
 246 |       495,
 247 |       76,
 248 |       45
 249 |     ],
 250 |     "instruction": "check the date and time menu",
 251 |     "data_type": "icon",
 252 |     "data_source": "windows"
 253 |   },
 254 |   {
 255 |     "img_filename": "pc_45c2712a-337d-4b6b-a47b-37ca4d5bdddd.png",
 256 |     "bbox": [
 257 |       0,
 258 |       354,
 259 |       260,
 260 |       42
 261 |     ],
 262 |     "instruction": "open the sleep mode",
 263 |     "data_type": "text",
 264 |     "data_source": "windows"
 265 |   },
 266 |   {
 267 |     "img_filename": "pc_133ac2cd-3eb3-4d45-be35-255a83831f97.png",
 268 |     "bbox": [
 269 |       607,
 270 |       8,
 271 |       349,
 272 |       36
 273 |     ],
 274 |     "instruction": "search for local files",
 275 |     "data_type": "text",
 276 |     "data_source": "windows"
 277 |   },
 278 |   {
 279 |     "img_filename": "pc_bd6711e1-ddc1-4873-8cf1-19654285f4ea.png",
 280 |     "bbox": [
 281 |       0,
 282 |       0,
 283 |       40,
 284 |       32
 285 |     ],
 286 |     "instruction": "view the personal account",
 287 |     "data_type": "icon",
 288 |     "data_source": "windows"
 289 |   },
 290 |   {
 291 |     "img_filename": "pc_83462e63-b0cd-4921-9d19-8a534ed445c7.png",
 292 |     "bbox": [
 293 |       916,
 294 |       13,
 295 |       44,
 296 |       39
 297 |     ],
 298 |     "instruction": "click the \"settings and more\" button",
 299 |     "data_type": "icon",
 300 |     "data_source": "windows"
 301 |   },
 302 |   {
 303 |     "img_filename": "pc_2b40c066-2f8f-4389-8df8-e70e8dfc1309.png",
 304 |     "bbox": [
 305 |       894,
 306 |       0,
 307 |       66,
 308 |       64
 309 |     ],
 310 |     "instruction": "full-screen display",
 311 |     "data_type": "icon",
 312 |     "data_source": "windows"
 313 |   },
 314 |   {
 315 |     "img_filename": "pc_1f371830-d909-41ac-a340-921a594f05b0.png",
 316 |     "bbox": [
 317 |       2,
 318 |       0,
 319 |       32,
 320 |       30
 321 |     ],
 322 |     "instruction": "undo",
 323 |     "data_type": "icon",
 324 |     "data_source": "windows"
 325 |   },
 326 |   {
 327 |     "img_filename": "pc_14fd6f5f-37d5-4b22-953e-626e11044f3e.png",
 328 |     "bbox": [
 329 |       918,
 330 |       336,
 331 |       41,
 332 |       46
 333 |     ],
 334 |     "instruction": "Add a new shortcut to the toolbar",
 335 |     "data_type": "icon",
 336 |     "data_source": "windows"
 337 |   },
 338 |   {
 339 |     "img_filename": "pc_8834317b-7d08-40f1-9036-40743aba5d70.png",
 340 |     "bbox": [
 341 |       204,
 342 |       205,
 343 |       86,
 344 |       84
 345 |     ],
 346 |     "instruction": "open local disk E",
 347 |     "data_type": "text",
 348 |     "data_source": "windows"
 349 |   },
 350 |   {
 351 |     "img_filename": "pc_fa28755f-aee4-48f3-a484-02e670a6ff8f.png",
 352 |     "bbox": [
 353 |       537,
 354 |       127,
 355 |       279,
 356 |       25
 357 |     ],
 358 |     "instruction": "create a new file",
 359 |     "data_type": "text",
 360 |     "data_source": "windows"
 361 |   },
 362 |   {
 363 |     "img_filename": "pc_712496c8-6588-4b57-a89d-b17eaecc761b.png",
 364 |     "bbox": [
 365 |       6,
 366 |       470,
 367 |       104,
 368 |       41
 369 |     ],
 370 |     "instruction": "open player settings",
 371 |     "data_type": "icon",
 372 |     "data_source": "windows"
 373 |   },
 374 |   {
 375 |     "img_filename": "pc_b1f1df2a-6c3d-40f9-ac70-dea7925c85dc.png",
 376 |     "bbox": [
 377 |       296,
 378 |       157,
 379 |       124,
 380 |       37
 381 |     ],
 382 |     "instruction": "set up onedrive",
 383 |     "data_type": "text",
 384 |     "data_source": "windows"
 385 |   },
 386 |   {
 387 |     "img_filename": "pc_61beef4d-1a93-47b3-8aff-ba0f7774cb43.png",
 388 |     "bbox": [
 389 |       605,
 390 |       261,
 391 |       17,
 392 |       17
 393 |     ],
 394 |     "instruction": "fill in red",
 395 |     "data_type": "icon",
 396 |     "data_source": "windows"
 397 |   },
 398 |   {
 399 |     "img_filename": "pc_73f3744f-675f-44e3-8c55-ef34bdce0da9.png",
 400 |     "bbox": [
 401 |       420,
 402 |       121,
 403 |       305,
 404 |       273
 405 |     ],
 406 |     "instruction": "open map",
 407 |     "data_type": "icon",
 408 |     "data_source": "windows"
 409 |   },
 410 |   {
 411 |     "img_filename": "pc_488287f0-9da5-4c6a-b236-109d3df56137.png",
 412 |     "bbox": [
 413 |       875,
 414 |       253,
 415 |       41,
 416 |       33
 417 |     ],
 418 |     "instruction": "maximize window",
 419 |     "data_type": "icon",
 420 |     "data_source": "windows"
 421 |   },
 422 |   {
 423 |     "img_filename": "pc_eb0b756c-e471-40eb-bfaa-06b432094ac6.png",
 424 |     "bbox": [
 425 |       0,
 426 |       105,
 427 |       317,
 428 |       40
 429 |     ],
 430 |     "instruction": "open account protection",
 431 |     "data_type": "text",
 432 |     "data_source": "windows"
 433 |   },
 434 |   {
 435 |     "img_filename": "pc_dd51aeab-7e2c-40aa-82e6-0f870e49e6ba.png",
 436 |     "bbox": [
 437 |       605,
 438 |       15,
 439 |       61,
 440 |       93
 441 |     ],
 442 |     "instruction": "add equation ",
 443 |     "data_type": "text",
 444 |     "data_source": "windows"
 445 |   },
 446 |   {
 447 |     "img_filename": "pc_41efdca0-442c-471e-8188-45457f9287c3.png",
 448 |     "bbox": [
 449 |       601,
 450 |       19,
 451 |       37,
 452 |       34
 453 |     ],
 454 |     "instruction": "minimize the window",
 455 |     "data_type": "icon",
 456 |     "data_source": "windows"
 457 |   },
 458 |   {
 459 |     "img_filename": "pc_8ce8a3b6-c2a9-4b69-986e-34b7d96e27ef.png",
 460 |     "bbox": [
 461 |       213,
 462 |       63,
 463 |       266,
 464 |       69
 465 |     ],
 466 |     "instruction": "view the devices",
 467 |     "data_type": "text",
 468 |     "data_source": "windows"
 469 |   },
 470 |   {
 471 |     "img_filename": "pc_80ab83db-da70-4a1d-b10e-a438a59b2197.png",
 472 |     "bbox": [
 473 |       299,
 474 |       175,
 475 |       182,
 476 |       34
 477 |     ],
 478 |     "instruction": "Adjust screen timeout",
 479 |     "data_type": "icon",
 480 |     "data_source": "windows"
 481 |   },
 482 |   {
 483 |     "img_filename": "pc_0c634288-11b9-498f-8fb6-582e26ea17c9.png",
 484 |     "bbox": [
 485 |       165,
 486 |       467,
 487 |       53,
 488 |       52
 489 |     ],
 490 |     "instruction": "start playback",
 491 |     "data_type": "icon",
 492 |     "data_source": "windows"
 493 |   },
 494 |   {
 495 |     "img_filename": "pc_dfd488a9-d3c6-4e00-844a-6d56834a367e.png",
 496 |     "bbox": [
 497 |       145,
 498 |       12,
 499 |       64,
 500 |       90
 501 |     ],
 502 |     "instruction": "set the screenshot option",
 503 |     "data_type": "text",
 504 |     "data_source": "windows"
 505 |   },
 506 |   {
 507 |     "img_filename": "pc_17bc1fc0-a71c-453d-a431-5ebcddeff38f.png",
 508 |     "bbox": [
 509 |       560,
 510 |       603,
 511 |       1095,
 512 |       203
 513 |     ],
 514 |     "instruction": "add the subtitle",
 515 |     "data_type": "text",
 516 |     "data_source": "windows"
 517 |   },
 518 |   {
 519 |     "img_filename": "pc_05f0185e-a291-4e4c-a777-ffb2c46037c6.png",
 520 |     "bbox": [
 521 |       390,
 522 |       183,
 523 |       304,
 524 |       248
 525 |     ],
 526 |     "instruction": "adjust light rotation",
 527 |     "data_type": "text",
 528 |     "data_source": "windows"
 529 |   },
 530 |   {
 531 |     "img_filename": "pc_aae370cb-9618-497e-8877-1a862725d28e.png",
 532 |     "bbox": [
 533 |       225,
 534 |       139,
 535 |       41,
 536 |       69
 537 |     ],
 538 |     "instruction": "click the colour 2 button",
 539 |     "data_type": "text",
 540 |     "data_source": "windows"
 541 |   },
 542 |   {
 543 |     "img_filename": "pc_f317f992-32e1-4470-ab0b-31910a70bcd4.png",
 544 |     "bbox": [
 545 |       561,
 546 |       308,
 547 |       1093,
 548 |       289
 549 |     ],
 550 |     "instruction": "add the title",
 551 |     "data_type": "text",
 552 |     "data_source": "windows"
 553 |   },
 554 |   {
 555 |     "img_filename": "pc_776b1aa0-a892-4461-b03a-88253e628426.png",
 556 |     "bbox": [
 557 |       18,
 558 |       15,
 559 |       44,
 560 |       39
 561 |     ],
 562 |     "instruction": "refresh the page",
 563 |     "data_type": "icon",
 564 |     "data_source": "windows"
 565 |   },
 566 |   {
 567 |     "img_filename": "pc_580035fc-f55e-4817-b0c5-b741fb034cd0.png",
 568 |     "bbox": [
 569 |       441,
 570 |       458,
 571 |       32,
 572 |       51
 573 |     ],
 574 |     "instruction": "click the button to play previous track",
 575 |     "data_type": "icon",
 576 |     "data_source": "windows"
 577 |   },
 578 |   {
 579 |     "img_filename": "pc_ac6779f8-a188-4633-bfcc-ab05ada6848c.png",
 580 |     "bbox": [
 581 |       872,
 582 |       40,
 583 |       37,
 584 |       32
 585 |     ],
 586 |     "instruction": "pin to taskbar",
 587 |     "data_type": "icon",
 588 |     "data_source": "windows"
 589 |   },
 590 |   {
 591 |     "img_filename": "pc_3b590e58-aab0-42c4-9b74-7d1a6b7f26fe.png",
 592 |     "bbox": [
 593 |       17,
 594 |       52,
 595 |       483,
 596 |       36
 597 |     ],
 598 |     "instruction": "enter the wegameapps folder",
 599 |     "data_type": "text",
 600 |     "data_source": "windows"
 601 |   },
 602 |   {
 603 |     "img_filename": "pc_4ba904da-bd51-42ea-a96f-c5681c630058.png",
 604 |     "bbox": [
 605 |       232,
 606 |       417,
 607 |       88,
 608 |       42
 609 |     ],
 610 |     "instruction": "check hourly weather",
 611 |     "data_type": "text",
 612 |     "data_source": "windows"
 613 |   },
 614 |   {
 615 |     "img_filename": "pc_4cb20510-1be3-4ef8-ac47-2b49f4463555.png",
 616 |     "bbox": [
 617 |       0,
 618 |       498,
 619 |       48,
 620 |       42
 621 |     ],
 622 |     "instruction": "open the menu of windows",
 623 |     "data_type": "icon",
 624 |     "data_source": "windows"
 625 |   },
 626 |   {
 627 |     "img_filename": "pc_f9867d85-5680-44ac-9bea-23acf5c9cc71.png",
 628 |     "bbox": [
 629 |       0,
 630 |       0,
 631 |       79,
 632 |       84
 633 |     ],
 634 |     "instruction": "open the recycle bin",
 635 |     "data_type": "text",
 636 |     "data_source": "windows"
 637 |   },
 638 |   {
 639 |     "img_filename": "pc_155fc26b-cddb-4a03-b636-322a45c054e6.png",
 640 |     "bbox": [
 641 |       912,
 642 |       8,
 643 |       43,
 644 |       43
 645 |     ],
 646 |     "instruction": "open settings",
 647 |     "data_type": "icon",
 648 |     "data_source": "windows"
 649 |   },
 650 |   {
 651 |     "img_filename": "pc_942d98d0-92d0-49ab-bc98-49a700c1822b.png",
 652 |     "bbox": [
 653 |       552,
 654 |       37,
 655 |       158,
 656 |       116
 657 |     ],
 658 |     "instruction": "check weather on tuesday",
 659 |     "data_type": "text",
 660 |     "data_source": "windows"
 661 |   },
 662 |   {
 663 |     "img_filename": "pc_aa124117-8877-4805-9c69-0ce80e1afc71.png",
 664 |     "bbox": [
 665 |       0,
 666 |       247,
 667 |       318,
 668 |       46
 669 |     ],
 670 |     "instruction": "open the playlists",
 671 |     "data_type": "text",
 672 |     "data_source": "windows"
 673 |   },
 674 |   {
 675 |     "img_filename": "pc_e650b1a7-a1e4-4cb4-a2f0-a50f04ce9925.png",
 676 |     "bbox": [
 677 |       0,
 678 |       498,
 679 |       348,
 680 |       42
 681 |     ],
 682 |     "instruction": "click the search bar",
 683 |     "data_type": "icon",
 684 |     "data_source": "windows"
 685 |   },
 686 |   {
 687 |     "img_filename": "pc_88dde48f-6594-4f17-b117-3529b2bde02e.png",
 688 |     "bbox": [
 689 |       7,
 690 |       203,
 691 |       28,
 692 |       39
 693 |     ],
 694 |     "instruction": "back to previous folder",
 695 |     "data_type": "icon",
 696 |     "data_source": "windows"
 697 |   },
 698 |   {
 699 |     "img_filename": "pc_5b1e5fe2-e063-463f-ab56-2b09d17b88a5.png",
 700 |     "bbox": [
 701 |       154,
 702 |       352,
 703 |       100,
 704 |       99
 705 |     ],
 706 |     "instruction": "open Samsung Printer Center",
 707 |     "data_type": "text",
 708 |     "data_source": "windows"
 709 |   },
 710 |   {
 711 |     "img_filename": "pc_8ed762cb-06dd-493f-b770-7e02298433f8.png",
 712 |     "bbox": [
 713 |       732,
 714 |       29,
 715 |       55,
 716 |       37
 717 |     ],
 718 |     "instruction": "turn on the mixed reality",
 719 |     "data_type": "icon",
 720 |     "data_source": "windows"
 721 |   },
 722 |   {
 723 |     "img_filename": "pc_56d9172e-1780-4fb7-8377-f9987b87508c.png",
 724 |     "bbox": [
 725 |       894,
 726 |       126,
 727 |       43,
 728 |       52
 729 |     ],
 730 |     "instruction": "delete the downloaded file",
 731 |     "data_type": "icon",
 732 |     "data_source": "windows"
 733 |   },
 734 |   {
 735 |     "img_filename": "pc_5165f0b8-ea19-448e-bcfc-66de25666bb9.png",
 736 |     "bbox": [
 737 |       0,
 738 |       26,
 739 |       64,
 740 |       65
 741 |     ],
 742 |     "instruction": "open the menu",
 743 |     "data_type": "text",
 744 |     "data_source": "windows"
 745 |   },
 746 |   {
 747 |     "img_filename": "pc_ecf34119-ce66-4699-a169-60e5279a650f.png",
 748 |     "bbox": [
 749 |       660,
 750 |       401,
 751 |       299,
 752 |       64
 753 |     ],
 754 |     "instruction": "switch to english",
 755 |     "data_type": "text",
 756 |     "data_source": "windows"
 757 |   },
 758 |   {
 759 |     "img_filename": "pc_445877f4-ba58-468b-97b6-e770b1a9f02a.png",
 760 |     "bbox": [
 761 |       77,
 762 |       0,
 763 |       282,
 764 |       28
 765 |     ],
 766 |     "instruction": "open the file",
 767 |     "data_type": "text",
 768 |     "data_source": "windows"
 769 |   },
 770 |   {
 771 |     "img_filename": "pc_92e1062b-0d4a-4e14-8b25-b79e4b6c9b55.png",
 772 |     "bbox": [
 773 |       392,
 774 |       50,
 775 |       341,
 776 |       36
 777 |     ],
 778 |     "instruction": "search for settings",
 779 |     "data_type": "icon",
 780 |     "data_source": "windows"
 781 |   },
 782 |   {
 783 |     "img_filename": "pc_09e2a811-2e5f-4cfc-9d6c-97acda34edf7.png",
 784 |     "bbox": [
 785 |       747,
 786 |       302,
 787 |       173,
 788 |       26
 789 |     ],
 790 |     "instruction": "gradient option",
 791 |     "data_type": "text",
 792 |     "data_source": "windows"
 793 |   },
 794 |   {
 795 |     "img_filename": "pc_c7974702-d944-412f-83b2-61f92cf3c68c.png",
 796 |     "bbox": [
 797 |       872,
 798 |       12,
 799 |       88,
 800 |       31
 801 |     ],
 802 |     "instruction": "share the file",
 803 |     "data_type": "text",
 804 |     "data_source": "windows"
 805 |   },
 806 |   {
 807 |     "img_filename": "pc_04d51f86-b324-4eb6-82f5-c8cd6cb3e79c.png",
 808 |     "bbox": [
 809 |       568,
 810 |       256,
 811 |       82,
 812 |       51
 813 |     ],
 814 |     "instruction": "chech visibility",
 815 |     "data_type": "text",
 816 |     "data_source": "windows"
 817 |   },
 818 |   {
 819 |     "img_filename": "pc_3a625c5d-db8a-41fb-b533-16a7c8a4f345.png",
 820 |     "bbox": [
 821 |       887,
 822 |       20,
 823 |       37,
 824 |       36
 825 |     ],
 826 |     "instruction": "refresh the page",
 827 |     "data_type": "icon",
 828 |     "data_source": "windows"
 829 |   },
 830 |   {
 831 |     "img_filename": "pc_17904586-d58c-4f1d-8c1e-6a6697353b14.png",
 832 |     "bbox": [
 833 |       792,
 834 |       13,
 835 |       156,
 836 |       65
 837 |     ],
 838 |     "instruction": "sign in",
 839 |     "data_type": "text",
 840 |     "data_source": "windows"
 841 |   },
 842 |   {
 843 |     "img_filename": "pc_25eca08c-57eb-45b0-8c0b-79ccec8400ff.png",
 844 |     "bbox": [
 845 |       901,
 846 |       0,
 847 |       33,
 848 |       22
 849 |     ],
 850 |     "instruction": "minimize the window.",
 851 |     "data_type": "icon",
 852 |     "data_source": "windows"
 853 |   },
 854 |   {
 855 |     "img_filename": "pc_cefd90d7-4af2-4370-b71e-8d4d5954c21e.png",
 856 |     "bbox": [
 857 |       620,
 858 |       458,
 859 |       82,
 860 |       54
 861 |     ],
 862 |     "instruction": "number 5",
 863 |     "data_type": "text",
 864 |     "data_source": "windows"
 865 |   },
 866 |   {
 867 |     "img_filename": "pc_8fc42b3c-c8bc-4866-b787-aed98a86d614.png",
 868 |     "bbox": [
 869 |       183,
 870 |       24,
 871 |       316,
 872 |       44
 873 |     ],
 874 |     "instruction": "view sound settings.",
 875 |     "data_type": "text",
 876 |     "data_source": "windows"
 877 |   },
 878 |   {
 879 |     "img_filename": "pc_a2b172fe-7e11-4795-8424-c18d5b65a009.png",
 880 |     "bbox": [
 881 |       503,
 882 |       149,
 883 |       97,
 884 |       37
 885 |     ],
 886 |     "instruction": "get help",
 887 |     "data_type": "text",
 888 |     "data_source": "windows"
 889 |   },
 890 |   {
 891 |     "img_filename": "pc_07faaa86-6e6b-457b-9a51-9b30edff21da.png",
 892 |     "bbox": [
 893 |       916,
 894 |       455,
 895 |       44,
 896 |       41
 897 |     ],
 898 |     "instruction": "share the current page",
 899 |     "data_type": "icon",
 900 |     "data_source": "windows"
 901 |   },
 902 |   {
 903 |     "img_filename": "pc_ec8508be-641d-492a-bccc-a8279ae5bf9b.png",
 904 |     "bbox": [
 905 |       78,
 906 |       29,
 907 |       47,
 908 |       91
 909 |     ],
 910 |     "instruction": "check the shape option",
 911 |     "data_type": "text",
 912 |     "data_source": "windows"
 913 |   },
 914 |   {
 915 |     "img_filename": "pc_733fbb76-a188-4804-a2c6-788acf844d7c.png",
 916 |     "bbox": [
 917 |       105,
 918 |       47,
 919 |       42,
 920 |       33
 921 |     ],
 922 |     "instruction": "back to previous page",
 923 |     "data_type": "icon",
 924 |     "data_source": "windows"
 925 |   },
 926 |   {
 927 |     "img_filename": "pc_02a8e358-8282-498b-b82e-c0f2b1b8d26a.png",
 928 |     "bbox": [
 929 |       614,
 930 |       45,
 931 |       345,
 932 |       33
 933 |     ],
 934 |     "instruction": "search for files on disk e:",
 935 |     "data_type": "text",
 936 |     "data_source": "windows"
 937 |   },
 938 |   {
 939 |     "img_filename": "pc_6f580940-6536-452e-9310-f9ebb87879eb.png",
 940 |     "bbox": [
 941 |       613,
 942 |       52,
 943 |       73,
 944 |       86
 945 |     ],
 946 |     "instruction": "edit with paint 3d",
 947 |     "data_type": "text",
 948 |     "data_source": "windows"
 949 |   },
 950 |   {
 951 |     "img_filename": "pc_8c0c6fa3-39d8-4633-aeb1-6544036c85bf.png",
 952 |     "bbox": [
 953 |       913,
 954 |       504,
 955 |       30,
 956 |       36
 957 |     ],
 958 |     "instruction": "adjust the volume",
 959 |     "data_type": "icon",
 960 |     "data_source": "windows"
 961 |   },
 962 |   {
 963 |     "img_filename": "pc_ecde084e-e461-4c7c-9ddc-bccabc874747.png",
 964 |     "bbox": [
 965 |       30,
 966 |       399,
 967 |       108,
 968 |       37
 969 |     ],
 970 |     "instruction": "learn more",
 971 |     "data_type": "text",
 972 |     "data_source": "windows"
 973 |   },
 974 |   {
 975 |     "img_filename": "pc_140cfa7d-6933-4591-ae0d-a1eadda2d7b5.png",
 976 |     "bbox": [
 977 |       703,
 978 |       51,
 979 |       160,
 980 |       73
 981 |     ],
 982 |     "instruction": "open videos folder",
 983 |     "data_type": "text",
 984 |     "data_source": "windows"
 985 |   },
 986 |   {
 987 |     "img_filename": "pc_5a8a71e4-4c80-4b17-b724-a6b6d48a339f.png",
 988 |     "bbox": [
 989 |       610,
 990 |       499,
 991 |       123,
 992 |       41
 993 |     ],
 994 |     "instruction": "check weather forecast",
 995 |     "data_type": "icon",
 996 |     "data_source": "windows"
 997 |   },
 998 |   {
 999 |     "img_filename": "pc_986f4ae9-9dd8-4c5c-b3d8-fb3fb5557d17.png",
1000 |     "bbox": [
1001 |       145,
1002 |       13,
1003 |       81,
1004 |       56
1005 |     ],
1006 |     "instruction": "multiplicate",
1007 |     "data_type": "icon",
1008 |     "data_source": "windows"
1009 |   },
1010 |   {
1011 |     "img_filename": "pc_6dfaf0c7-a3e5-4e71-8431-5eeb9fb8e493.png",
1012 |     "bbox": [
1013 |       527,
1014 |       243,
1015 |       362,
1016 |       36
1017 |     ],
1018 |     "instruction": "see more information",
1019 |     "data_type": "text",
1020 |     "data_source": "windows"
1021 |   },
1022 |   {
1023 |     "img_filename": "pc_89fc8216-3292-418c-864f-edcce3c34dc5.png",
1024 |     "bbox": [
1025 |       790,
1026 |       444,
1027 |       42,
1028 |       39
1029 |     ],
1030 |     "instruction": "adjust volume in the music player",
1031 |     "data_type": "icon",
1032 |     "data_source": "windows"
1033 |   },
1034 |   {
1035 |     "img_filename": "pc_c00e575a-fa2d-47c7-83cc-9f4e99f15720.png",
1036 |     "bbox": [
1037 |       0,
1038 |       459,
1039 |       260,
1040 |       41
1041 |     ],
1042 |     "instruction": "restart the computer",
1043 |     "data_type": "text",
1044 |     "data_source": "windows"
1045 |   },
1046 |   {
1047 |     "img_filename": "pc_b2579d6f-b494-47e3-872a-5e42a43f251a.png",
1048 |     "bbox": [
1049 |       782,
1050 |       394,
1051 |       146,
1052 |       30
1053 |     ],
1054 |     "instruction": "adjust screen size",
1055 |     "data_type": "icon",
1056 |     "data_source": "windows"
1057 |   },
1058 |   {
1059 |     "img_filename": "pc_79eaf625-93f5-42a4-977e-71aba5418ff4.png",
1060 |     "bbox": [11, 120, 145, 22],
1061 |     "instruction": "go to the desktop",
1062 |     "data_type": "text",
1063 |     "data_source": "windows"
1064 |   },
1065 |   {
1066 |     "img_filename": "pc_1f3eb0eb-2ab2-4e48-b278-de334846ee0d.png",
1067 |     "bbox": [
1068 |       406,
1069 |       81,
1070 |       351,
1071 |       291
1072 |     ],
1073 |     "instruction": "choose the pig",
1074 |     "data_type": "icon",
1075 |     "data_source": "windows"
1076 |   },
1077 |   {
1078 |     "img_filename": "pc_6a8d5384-ca12-4ae2-b14c-4c9dc08d4896.png",
1079 |     "bbox": [
1080 |       97,
1081 |       33,
1082 |       1551,
1083 |       35
1084 |     ],
1085 |     "instruction": "input a url",
1086 |     "data_type": "icon",
1087 |     "data_source": "windows"
1088 |   },
1089 |   {
1090 |     "img_filename": "pc_c0996a5a-3af1-487b-9060-7b1a6779e42f.png",
1091 |     "bbox": [
1092 |       59,
1093 |       92,
1094 |       321,
1095 |       42
1096 |     ],
1097 |     "instruction": "go to the homepage",
1098 |     "data_type": "text",
1099 |     "data_source": "windows"
1100 |   },
1101 |   {
1102 |     "img_filename": "pc_e18b4420-34da-4d60-889e-924a76fb5094.png",
1103 |     "bbox": [
1104 |       840,
1105 |       11,
1106 |       42,
1107 |       37
1108 |     ],
1109 |     "instruction": "open the favourites",
1110 |     "data_type": "icon",
1111 |     "data_source": "windows"
1112 |   },
1113 |   {
1114 |     "img_filename": "pc_4c43fbd2-d84e-4bb4-8169-7a748530c1af.png",
1115 |     "bbox": [
1116 |       56,
1117 |       393,
1118 |       438,
1119 |       117
1120 |     ],
1121 |     "instruction": "choose wechat",
1122 |     "data_type": "text",
1123 |     "data_source": "windows"
1124 |   },
1125 |   {
1126 |     "img_filename": "pc_7e37689a-0657-4856-8adf-72a21ee640c9.png",
1127 |     "bbox": [
1128 |       251,
1129 |       163,
1130 |       227,
1131 |       250
1132 |     ],
1133 |     "instruction": "check windows keyboard shortcuts",
1134 |     "data_type": "text",
1135 |     "data_source": "windows"
1136 |   },
1137 |   {
1138 |     "img_filename": "pc_bb73a431-c05e-47a8-bd27-8c7cbc2f7f23.png",
1139 |     "bbox": [
1140 |       116,
1141 |       22,
1142 |       226,
1143 |       28
1144 |     ],
1145 |     "instruction": "view the file sorting method",
1146 |     "data_type": "text",
1147 |     "data_source": "windows"
1148 |   },
1149 |   {
1150 |     "img_filename": "pc_b4d4f0c2-5f8e-409e-ac20-b1a2c7d6f440.png",
1151 |     "bbox": [
1152 |       2,
1153 |       100,
1154 |       78,
1155 |       92
1156 |     ],
1157 |     "instruction": "open Microsoft edge",
1158 |     "data_type": "text",
1159 |     "data_source": "windows"
1160 |   },
1161 |   {
1162 |     "img_filename": "pc_5da8f8cd-8ba2-490a-86e0-5c82421a9bee.png",
1163 |     "bbox": [
1164 |       322,
1165 |       67,
1166 |       279,
1167 |       26
1168 |     ],
1169 |     "instruction": "click to open the file",
1170 |     "data_type": "text",
1171 |     "data_source": "windows"
1172 |   },
1173 |   {
1174 |     "img_filename": "pc_2d42ba96-b43d-4f3f-81c1-80c8c107ca1c.png",
1175 |     "bbox": [
1176 |       8,
1177 |       33,
1178 |       289,
1179 |       34
1180 |     ],
1181 |     "instruction": "search for files",
1182 |     "data_type": "text",
1183 |     "data_source": "windows"
1184 |   },
1185 |   {
1186 |     "img_filename": "pc_4392332d-683f-4746-bbb2-e9287d9cdae0.png",
1187 |     "bbox": [
1188 |       308,
1189 |       203,
1190 |       81,
1191 |       84
1192 |     ],
1193 |     "instruction": "set the second photo as wallpaper",
1194 |     "data_type": "icon",
1195 |     "data_source": "windows"
1196 |   },
1197 |   {
1198 |     "img_filename": "pc_977de16a-870e-48f3-8a38-ec9e1efb9f77.png",
1199 |     "bbox": [
1200 |       555,
1201 |       238,
1202 |       161,
1203 |       50
1204 |     ],
1205 |     "instruction": "add a language",
1206 |     "data_type": "text",
1207 |     "data_source": "windows"
1208 |   },
1209 |   {
1210 |     "img_filename": "pc_16d04f01-66a7-45df-a189-66c29fa935d7.png",
1211 |     "bbox": [
1212 |       321,
1213 |       482,
1214 |       154,
1215 |       37
1216 |     ],
1217 |     "instruction": "open files",
1218 |     "data_type": "text",
1219 |     "data_source": "windows"
1220 |   },
1221 |   {
1222 |     "img_filename": "pc_d077a08e-e927-4daa-aaf1-2f2385eefa04.png",
1223 |     "bbox": [
1224 |       227,
1225 |       188,
1226 |       188,
1227 |       235
1228 |     ],
1229 |     "instruction": "play the \"spider solitaire\"",
1230 |     "data_type": "text",
1231 |     "data_source": "windows"
1232 |   },
1233 |   {
1234 |     "img_filename": "pc_01c641cb-aa78-4bae-80ea-dda820dd00eb.png",
1235 |     "bbox": [
1236 |       109,
1237 |       144,
1238 |       51,
1239 |       73
1240 |     ],
1241 |     "instruction": "view paste option",
1242 |     "data_type": "text",
1243 |     "data_source": "windows"
1244 |   },
1245 |   {
1246 |     "img_filename": "pc_9c3204c7-8a02-406a-8dee-5b363887258c.png",
1247 |     "bbox": [
1248 |       0,
1249 |       21,
1250 |       50,
1251 |       39
1252 |     ],
1253 |     "instruction": "back to the previous page",
1254 |     "data_type": "icon",
1255 |     "data_source": "windows"
1256 |   },
1257 |   {
1258 |     "img_filename": "pc_471e7c2d-8c16-4626-9b8e-7e0fd79e3a32.png",
1259 |     "bbox": [
1260 |       181,
1261 |       448,
1262 |       31,
1263 |       52
1264 |     ],
1265 |     "instruction": "click to play the next track",
1266 |     "data_type": "icon",
1267 |     "data_source": "windows"
1268 |   },
1269 |   {
1270 |     "img_filename": "pc_f92a83db-cb5b-4295-af83-3606639056be.png",
1271 |     "bbox": [
1272 |       273,
1273 |       26,
1274 |       67,
1275 |       28
1276 |     ],
1277 |     "instruction": "review option",
1278 |     "data_type": "text",
1279 |     "data_source": "windows"
1280 |   },
1281 |   {
1282 |     "img_filename": "pc_daa34642-1973-424e-a752-3b309fa6512b.png",
1283 |     "bbox": [
1284 |       907,
1285 |       499,
1286 |       50,
1287 |       41
1288 |     ],
1289 |     "instruction": "check for two unread messages",
1290 |     "data_type": "icon",
1291 |     "data_source": "windows"
1292 |   },
1293 |   {
1294 |     "img_filename": "pc_58d89b93-10e2-41fe-8b6d-3532fd3bb832.png",
1295 |     "bbox": [
1296 |       0,
1297 |       407,
1298 |       50,
1299 |       45
1300 |     ],
1301 |     "instruction": "view windows settings",
1302 |     "data_type": "icon",
1303 |     "data_source": "windows"
1304 |   },
1305 |   {
1306 |     "img_filename": "pc_79377abe-731c-4d3b-ab04-b332c4054b81.png",
1307 |     "bbox": [
1308 |       412,
1309 |       326,
1310 |       72,
1311 |       45
1312 |     ],
1313 |     "instruction": "dismiss the account protection",
1314 |     "data_type": "text",
1315 |     "data_source": "windows"
1316 |   },
1317 |   {
1318 |     "img_filename": "pc_9d04d927-2a44-4e0d-b752-57abf0a0522f.png",
1319 |     "bbox": [
1320 |       2,
1321 |       23,
1322 |       45,
1323 |       42
1324 |     ],
1325 |     "instruction": "back to the previous page",
1326 |     "data_type": "icon",
1327 |     "data_source": "windows"
1328 |   },
1329 |   {
1330 |     "img_filename": "pc_c8b036c3-da02-490e-a09b-0a42b8d9f344.png",
1331 |     "bbox": [
1332 |       0,
1333 |       391,
1334 |       47,
1335 |       42
1336 |     ],
1337 |     "instruction": "personal account options",
1338 |     "data_type": "icon",
1339 |     "data_source": "windows"
1340 |   },
1341 |   {
1342 |     "img_filename": "pc_3f7ea28c-0425-4837-a540-dc5bb23449b8.png",
1343 |     "bbox": [
1344 |       48,
1345 |       25,
1346 |       58,
1347 |       29
1348 |     ],
1349 |     "instruction": "design option",
1350 |     "data_type": "text",
1351 |     "data_source": "windows"
1352 |   },
1353 |   {
1354 |     "img_filename": "pc_7e7fa34c-9bb9-43bb-bad6-a7e62e7d0f95.png",
1355 |     "bbox": [
1356 |       52,
1357 |       79,
1358 |       247,
1359 |       32
1360 |     ],
1361 |     "instruction": "open paint 3d",
1362 |     "data_type": "text",
1363 |     "data_source": "windows"
1364 |   },
1365 |   {
1366 |     "img_filename": "pc_7e7fa34c-9bb9-43bb-bad6-a7e62e7d0f95.png",
1367 |     "bbox": [
1368 |       322,
1369 |       154,
1370 |       96,
1371 |       96
1372 |     ],
1373 |     "instruction": "open microsoft edge",
1374 |     "data_type": "text",
1375 |     "data_source": "windows"
1376 |   },
1377 |   {
1378 |     "img_filename": "pc_7e7fa34c-9bb9-43bb-bad6-a7e62e7d0f95.png",
1379 |     "bbox": [
1380 |       1,
1381 |       593,
1382 |       39,
1383 |       41
1384 |     ],
1385 |     "instruction": "turn off the computer",
1386 |     "data_type": "icon",
1387 |     "data_source": "windows"
1388 |   },
1389 |   {
1390 |     "img_filename": "pc_17b94559-9897-4b8e-ae34-c47ce5b5d46f.png",
1391 |     "bbox": [
1392 |       4,
1393 |       54,
1394 |       298,
1395 |       37
1396 |     ],
1397 |     "instruction": "switch to home",
1398 |     "data_type": "text",
1399 |     "data_source": "windows"
1400 |   },
1401 |   {
1402 |     "img_filename": "pc_17b94559-9897-4b8e-ae34-c47ce5b5d46f.png",
1403 |     "bbox": [
1404 |       4,
1405 |       3,
1406 |       41,
1407 |       30
1408 |     ],
1409 |     "instruction": "click the \"back to previous page\" button",
1410 |     "data_type": "icon",
1411 |     "data_source": "windows"
1412 |   },
1413 |   {
1414 |     "img_filename": "pc_17b94559-9897-4b8e-ae34-c47ce5b5d46f.png",
1415 |     "bbox": [
1416 |       319,
1417 |       277,
1418 |       241,
1419 |       48
1420 |     ],
1421 |     "instruction": "open fax option",
1422 |     "data_type": "text",
1423 |     "data_source": "windows"
1424 |   },
1425 |   {
1426 |     "img_filename": "pc_17b94559-9897-4b8e-ae34-c47ce5b5d46f.png",
1427 |     "bbox": [925, 370, 131, 23],
1428 |     "instruction": "need some help",
1429 |     "data_type": "text",
1430 |     "data_source": "windows"
1431 |   },
1432 |   {
1433 |     "img_filename": "pc_5377c04c-7d8f-4377-b1ed-d7994722871f.png",
1434 |     "bbox": [
1435 |       12,
1436 |       201,
1437 |       337,
1438 |       50
1439 |     ],
1440 |     "instruction": "check email and account",
1441 |     "data_type": "text",
1442 |     "data_source": "windows"
1443 |   },
1444 |   {
1445 |     "img_filename": "pc_5377c04c-7d8f-4377-b1ed-d7994722871f.png",
1446 |     "bbox": [
1447 |       112,
1448 |       2,
1449 |       84,
1450 |       42
1451 |     ],
1452 |     "instruction": "switch to documents",
1453 |     "data_type": "text",
1454 |     "data_source": "windows"
1455 |   },
1456 |   {
1457 |     "img_filename": "pc_5377c04c-7d8f-4377-b1ed-d7994722871f.png",
1458 |     "bbox": [
1459 |       370,
1460 |       509,
1461 |       132,
1462 |       34
1463 |     ],
1464 |     "instruction": "view the game for me",
1465 |     "data_type": "text",
1466 |     "data_source": "windows"
1467 |   },
1468 |   {
1469 |     "img_filename": "pc_70f27c18-7de8-4445-b6fe-202fa17bbe04.png",
1470 |     "bbox": [
1471 |       149,
1472 |       62,
1473 |       24,
1474 |       31
1475 |     ],
1476 |     "instruction": "use pencil tool",
1477 |     "data_type": "icon",
1478 |     "data_source": "windows"
1479 |   },
1480 |   {
1481 |     "img_filename": "pc_70f27c18-7de8-4445-b6fe-202fa17bbe04.png",
1482 |     "bbox": [
1483 |       283,
1484 |       60,
1485 |       42,
1486 |       67
1487 |     ],
1488 |     "instruction": "click to choose shapes",
1489 |     "data_type": "text",
1490 |     "data_source": "windows"
1491 |   },
1492 |   {
1493 |     "img_filename": "pc_70f27c18-7de8-4445-b6fe-202fa17bbe04.png",
1494 |     "bbox": [
1495 |       368,
1496 |       59,
1497 |       42,
1498 |       69
1499 |     ],
1500 |     "instruction": "select a new size",
1501 |     "data_type": "text",
1502 |     "data_source": "windows"
1503 |   },
1504 |   {
1505 |     "img_filename": "pc_70f27c18-7de8-4445-b6fe-202fa17bbe04.png",
1506 |     "bbox": [
1507 |       63,
1508 |       58,
1509 |       43,
1510 |       70
1511 |     ],
1512 |     "instruction": "use the select tool",
1513 |     "data_type": "text",
1514 |     "data_source": "windows"
1515 |   },
1516 |   {
1517 |     "img_filename": "pc_3b7dab65-2cc4-4545-be95-93d4d2576b47.png",
1518 |     "bbox": [
1519 |       615,
1520 |       229,
1521 |       221,
1522 |       219
1523 |     ],
1524 |     "instruction": "view the setting of protection of pc",
1525 |     "data_type": "text",
1526 |     "data_source": "windows"
1527 |   },
1528 |   {
1529 |     "img_filename": "pc_3b7dab65-2cc4-4545-be95-93d4d2576b47.png",
1530 |     "bbox": [
1531 |       1099,
1532 |       520,
1533 |       204,
1534 |       227
1535 |     ],
1536 |     "instruction": "view the get organized tips",
1537 |     "data_type": "text",
1538 |     "data_source": "windows"
1539 |   },
1540 |   {
1541 |     "img_filename": "pc_44159aab-93c3-4ef6-8841-67a11225ca3f.png",
1542 |     "bbox": [
1543 |       6,
1544 |       157,
1545 |       429,
1546 |       57
1547 |     ],
1548 |     "instruction": "log in using google account",
1549 |     "data_type": "text",
1550 |     "data_source": "windows"
1551 |   },
1552 |   {
1553 |     "img_filename": "pc_44159aab-93c3-4ef6-8841-67a11225ca3f.png",
1554 |     "bbox": [
1555 |       9,
1556 |       396,
1557 |       435,
1558 |       47
1559 |     ],
1560 |     "instruction": "check advanced setup",
1561 |     "data_type": "text",
1562 |     "data_source": "windows"
1563 |   },
1564 |   {
1565 |     "img_filename": "pc_b34a8be5-2b1f-40fa-b348-41aac2bfb952.png",
1566 |     "bbox": [
1567 |       185,
1568 |       432,
1569 |       186,
1570 |       52
1571 |     ],
1572 |     "instruction": "check the detail of uv index",
1573 |     "data_type": "text",
1574 |     "data_source": "windows"
1575 |   },
1576 |   {
1577 |     "img_filename": "pc_b34a8be5-2b1f-40fa-b348-41aac2bfb952.png",
1578 |     "bbox": [
1579 |       624,
1580 |       263,
1581 |       129,
1582 |       28
1583 |     ],
1584 |     "instruction": "see full forecast",
1585 |     "data_type": "text",
1586 |     "data_source": "windows"
1587 |   },
1588 |   {
1589 |     "img_filename": "pc_b34a8be5-2b1f-40fa-b348-41aac2bfb952.png",
1590 |     "bbox": [
1591 |       0,
1592 |       181,
1593 |       47,
1594 |       50
1595 |     ],
1596 |     "instruction": "the internet icon",
1597 |     "data_type": "icon",
1598 |     "data_source": "windows"
1599 |   },
1600 |   {
1601 |     "img_filename": "pc_efc1c0f0-366b-47a0-8e3f-0d197023a2bb.png",
1602 |     "bbox": [
1603 |       9,
1604 |       232,
1605 |       318,
1606 |       48
1607 |     ],
1608 |     "instruction": "play the Klondike game",
1609 |     "data_type": "text",
1610 |     "data_source": "windows"
1611 |   },
1612 |   {
1613 |     "img_filename": "pc_efc1c0f0-366b-47a0-8e3f-0d197023a2bb.png",
1614 |     "bbox": [
1615 |       972,
1616 |       32,
1617 |       164,
1618 |       68
1619 |     ],
1620 |     "instruction": "sign in",
1621 |     "data_type": "text",
1622 |     "data_source": "windows"
1623 |   },
1624 |   {
1625 |     "img_filename": "pc_f2244f33-a9c9-44e7-bacb-3e51f72d619e.png",
1626 |     "bbox": [
1627 |       2,
1628 |       120,
1629 |       247,
1630 |       35
1631 |     ],
1632 |     "instruction": "choose standard mode",
1633 |     "data_type": "text",
1634 |     "data_source": "windows"
1635 |   },
1636 |   {
1637 |     "img_filename": "pc_f2244f33-a9c9-44e7-bacb-3e51f72d619e.png",
1638 |     "bbox": [
1639 |       6,
1640 |       397,
1641 |       249,
1642 |       39
1643 |     ],
1644 |     "instruction": "adjust the volume",
1645 |     "data_type": "text",
1646 |     "data_source": "windows"
1647 |   },
1648 |   {
1649 |     "img_filename": "pc_639ab093-46ed-4332-9229-125647aad9da.png",
1650 |     "bbox": [
1651 |       2,
1652 |       2,
1653 |       30,
1654 |       30
1655 |     ],
1656 |     "instruction": "add a new one",
1657 |     "data_type": "icon",
1658 |     "data_source": "windows"
1659 |   },
1660 |   {
1661 |     "img_filename": "pc_639ab093-46ed-4332-9229-125647aad9da.png",
1662 |     "bbox": [
1663 |       211,
1664 |       274,
1665 |       40,
1666 |       36
1667 |     ],
1668 |     "instruction": "insert an image",
1669 |     "data_type": "icon",
1670 |     "data_source": "windows"
1671 |   },
1672 |   {
1673 |     "img_filename": "pc_57bf0c6a-5cdb-43c9-a807-7b1d83f5ab08.png",
1674 |     "bbox": [
1675 |       5,
1676 |       78,
1677 |       272,
1678 |       38
1679 |     ],
1680 |     "instruction": "view the timer",
1681 |     "data_type": "text",
1682 |     "data_source": "windows"
1683 |   },
1684 |   {
1685 |     "img_filename": "pc_57bf0c6a-5cdb-43c9-a807-7b1d83f5ab08.png",
1686 |     "bbox": [
1687 |       560,
1688 |       555,
1689 |       120,
1690 |       30
1691 |     ],
1692 |     "instruction": "click get start",
1693 |     "data_type": "icon",
1694 |     "data_source": "windows"
1695 |   },
1696 |   {
1697 |     "img_filename": "pc_57bf0c6a-5cdb-43c9-a807-7b1d83f5ab08.png",
1698 |     "bbox": [
1699 |       4,
1700 |       199,
1701 |       272,
1702 |       35
1703 |     ],
1704 |     "instruction": "view world clock",
1705 |     "data_type": "text",
1706 |     "data_source": "windows"
1707 |   },
1708 |   {
1709 |     "img_filename": "pc_050e2feb-60bf-401e-b6b0-28c0fe490347.png",
1710 |     "bbox": [
1711 |       338,
1712 |       29,
1713 |       71,
1714 |       54
1715 |     ],
1716 |     "instruction": "insert 3d shapes item",
1717 |     "data_type": "text",
1718 |     "data_source": "windows"
1719 |   },
1720 |   {
1721 |     "img_filename": "pc_050e2feb-60bf-401e-b6b0-28c0fe490347.png",
1722 |     "bbox": [
1723 |       949,
1724 |       29,
1725 |       59,
1726 |       53
1727 |     ],
1728 |     "instruction": "undo",
1729 |     "data_type": "text",
1730 |     "data_source": "windows"
1731 |   },
1732 |   {
1733 |     "img_filename": "pc_050e2feb-60bf-401e-b6b0-28c0fe490347.png",
1734 |     "bbox": [
1735 |       962,
1736 |       235,
1737 |       98,
1738 |       63
1739 |     ],
1740 |     "instruction": "click \"select all\"",
1741 |     "data_type": "text",
1742 |     "data_source": "windows"
1743 |   },
1744 |   {
1745 |     "img_filename": "pc_050e2feb-60bf-401e-b6b0-28c0fe490347.png",
1746 |     "bbox": [
1747 |       962,
1748 |       544,
1749 |       39,
1750 |       44
1751 |     ],
1752 |     "instruction": "rotate left",
1753 |     "data_type": "icon",
1754 |     "data_source": "windows"
1755 |   },
1756 |   {
1757 |     "img_filename": "pc_7b437f7a-8819-467b-b5a6-96e8588f15a2.png",
1758 |     "bbox": [
1759 |       3,
1760 |       845,
1761 |       289,
1762 |       66
1763 |     ],
1764 |     "instruction": "check the updates",
1765 |     "data_type": "text",
1766 |     "data_source": "windows"
1767 |   },
1768 |   {
1769 |     "img_filename": "pc_7b437f7a-8819-467b-b5a6-96e8588f15a2.png",
1770 |     "bbox": [
1771 |       515,
1772 |       864,
1773 |       66,
1774 |       56
1775 |     ],
1776 |     "instruction": "view in youtube",
1777 |     "data_type": "icon",
1778 |     "data_source": "windows"
1779 |   },
1780 |   {
1781 |     "img_filename": "pc_56c33426-0532-47dd-bf74-2a560c5a63f0.png",
1782 |     "bbox": [
1783 |       2,
1784 |       57,
1785 |       63,
1786 |       71
1787 |     ],
1788 |     "instruction": "empty recycle bin",
1789 |     "data_type": "text",
1790 |     "data_source": "windows"
1791 |   },
1792 |   {
1793 |     "img_filename": "pc_56c33426-0532-47dd-bf74-2a560c5a63f0.png",
1794 |     "bbox": [
1795 |       138,
1796 |       59,
1797 |       49,
1798 |       69
1799 |     ],
1800 |     "instruction": "restore all items",
1801 |     "data_type": "text",
1802 |     "data_source": "windows"
1803 |   },
1804 |   {
1805 |     "img_filename": "pc_1f6ac92f-9273-4758-a571-d0c831bb1dba.png",
1806 |     "bbox": [
1807 |       4,
1808 |       450,
1809 |       309,
1810 |       34
1811 |     ],
1812 |     "instruction": "check microphone permissions",
1813 |     "data_type": "text",
1814 |     "data_source": "windows"
1815 |   },
1816 |   {
1817 |     "img_filename": "pc_1f6ac92f-9273-4758-a571-d0c831bb1dba.png",
1818 |     "bbox": [
1819 |       924,
1820 |       292,
1821 |       155,
1822 |       55
1823 |     ],
1824 |     "instruction": "give feedback",
1825 |     "data_type": "text",
1826 |     "data_source": "windows"
1827 |   },
1828 |   {
1829 |     "img_filename": "pc_85012b9e-3058-4501-a9c7-6415d31c7699.png",
1830 |     "bbox": [
1831 |       347,
1832 |       396,
1833 |       193,
1834 |       30
1835 |     ],
1836 |     "instruction": "check the properties",
1837 |     "data_type": "text",
1838 |     "data_source": "windows"
1839 |   },
1840 |   {
1841 |     "img_filename": "pc_85012b9e-3058-4501-a9c7-6415d31c7699.png",
1842 |     "bbox": [
1843 |       331,
1844 |       593,
1845 |       434,
1846 |       56
1847 |     ],
1848 |     "instruction": "change adapter options",
1849 |     "data_type": "text",
1850 |     "data_source": "windows"
1851 |   },
1852 |   {
1853 |     "img_filename": "pc_5df0bc8e-3005-4a32-b824-0318d6a05bdd.png",
1854 |     "bbox": [
1855 |       346,
1856 |       268,
1857 |       127,
1858 |       130
1859 |     ],
1860 |     "instruction": "report a problem",
1861 |     "data_type": "text",
1862 |     "data_source": "windows"
1863 |   },
1864 |   {
1865 |     "img_filename": "pc_5df0bc8e-3005-4a32-b824-0318d6a05bdd.png",
1866 |     "bbox": [
1867 |       2,
1868 |       161,
1869 |       314,
1870 |       36
1871 |     ],
1872 |     "instruction": "switch to feedback",
1873 |     "data_type": "text",
1874 |     "data_source": "windows"
1875 |   },
1876 |   {
1877 |     "img_filename": "pc_731487b7-f192-464c-8fc7-95af0962861f.png",
1878 |     "bbox": [
1879 |       23,
1880 |       96,
1881 |       334,
1882 |       54
1883 |     ],
1884 |     "instruction": "check microphone",
1885 |     "data_type": "text",
1886 |     "data_source": "windows"
1887 |   },
1888 |   {
1889 |     "img_filename": "pc_731487b7-f192-464c-8fc7-95af0962861f.png",
1890 |     "bbox": [
1891 |       125,
1892 |       40,
1893 |       41,
1894 |       17
1895 |     ],
1896 |     "instruction": "switch to sounds",
1897 |     "data_type": "text",
1898 |     "data_source": "windows"
1899 |   },
1900 |   {
1901 |     "img_filename": "pc_6f79b56c-2b0f-471d-9f9d-93932c69a0ce.png",
1902 |     "bbox": [
1903 |       1025,
1904 |       141,
1905 |       104,
1906 |       127
1907 |     ],
1908 |     "instruction": "check my apple id",
1909 |     "data_type": "text",
1910 |     "data_source": "macos"
1911 |   },
1912 |   {
1913 |     "img_filename": "pc_6f79b56c-2b0f-471d-9f9d-93932c69a0ce.png",
1914 |     "bbox": [
1915 |       1018,
1916 |       368,
1917 |       121,
1918 |       147
1919 |     ],
1920 |     "instruction": "view the language & region settings",
1921 |     "data_type": "text",
1922 |     "data_source": "macos"
1923 |   },
1924 |   {
1925 |     "img_filename": "pc_6f79b56c-2b0f-471d-9f9d-93932c69a0ce.png",
1926 |     "bbox": [
1927 |       859,
1928 |       952,
1929 |       113,
1930 |       153
1931 |     ],
1932 |     "instruction": "open time machine",
1933 |     "data_type": "text",
1934 |     "data_source": "macos"
1935 |   },
1936 |   {
1937 |     "img_filename": "pc_6f79b56c-2b0f-471d-9f9d-93932c69a0ce.png",
1938 |     "bbox": [
1939 |       41,
1940 |       954,
1941 |       110,
1942 |       128
1943 |     ],
1944 |     "instruction": "check desplay settings",
1945 |     "data_type": "text",
1946 |     "data_source": "macos"
1947 |   },
1948 |   {
1949 |     "img_filename": "pc_6f79b56c-2b0f-471d-9f9d-93932c69a0ce.png",
1950 |     "bbox": [
1951 |       39,
1952 |       761,
1953 |       111,
1954 |       164
1955 |     ],
1956 |     "instruction": "software update",
1957 |     "data_type": "text",
1958 |     "data_source": "macos"
1959 |   },
1960 |   {
1961 |     "img_filename": "pc_66906e9c-dc3c-49e9-aaf0-0e7d5efc97db.png",
1962 |     "bbox": [
1963 |       492,
1964 |       141,
1965 |       148,
1966 |       43
1967 |     ],
1968 |     "instruction": "switch to desktop settings",
1969 |     "data_type": "text",
1970 |     "data_source": "macos"
1971 |   },
1972 |   {
1973 |     "img_filename": "pc_66906e9c-dc3c-49e9-aaf0-0e7d5efc97db.png",
1974 |     "bbox": [
1975 |       347,
1976 |       209,
1977 |       350,
1978 |       41
1979 |     ],
1980 |     "instruction": "show screen saver after",
1981 |     "data_type": "text",
1982 |     "data_source": "macos"
1983 |   },
1984 |   {
1985 |     "img_filename": "pc_66906e9c-dc3c-49e9-aaf0-0e7d5efc97db.png",
1986 |     "bbox": [
1987 |       99,
1988 |       425,
1989 |       178,
1990 |       147
1991 |     ],
1992 |     "instruction": "choose the ken burns as screen saver",
1993 |     "data_type": "text",
1994 |     "data_source": "macos"
1995 |   },
1996 |   {
1997 |     "img_filename": "pc_772a850b-eb35-4efb-9767-ef0eadeb70f0.png",
1998 |     "bbox": [
1999 |       672,
2000 |       145,
2001 |       142,
2002 |       134
2003 |     ],
2004 |     "instruction": "switch to dark mode",
2005 |     "data_type": "text",
2006 |     "data_source": "macos"
2007 |   },
2008 |   {
2009 |     "img_filename": "pc_772a850b-eb35-4efb-9767-ef0eadeb70f0.png",
2010 |     "bbox": [
2011 |       499,
2012 |       396,
2013 |       304,
2014 |       40
2015 |     ],
2016 |     "instruction": "choose highlight color",
2017 |     "data_type": "icon",
2018 |     "data_source": "macos"
2019 |   },
2020 |   {
2021 |     "img_filename": "pc_772a850b-eb35-4efb-9767-ef0eadeb70f0.png",
2022 |     "bbox": [
2023 |       500,
2024 |       891,
2025 |       307,
2026 |       46
2027 |     ],
2028 |     "instruction": "select default web browser",
2029 |     "data_type": "icon",
2030 |     "data_source": "macos"
2031 |   },
2032 |   {
2033 |     "img_filename": "pc_772a850b-eb35-4efb-9767-ef0eadeb70f0.png",
2034 |     "bbox": [
2035 |       502,
2036 |       794,
2037 |       423,
2038 |       41
2039 |     ],
2040 |     "instruction": "jump to the spot that's clicked",
2041 |     "data_type": "text",
2042 |     "data_source": "macos"
2043 |   },
2044 |   {
2045 |     "img_filename": "pc_3ffab0d5-e99d-47b8-9684-ec9d8d063419.png",
2046 |     "bbox": [
2047 |       681,
2048 |       243,
2049 |       113,
2050 |       45
2051 |     ],
2052 |     "instruction": "switch to apps",
2053 |     "data_type": "text",
2054 |     "data_source": "macos"
2055 |   },
2056 |   {
2057 |     "img_filename": "pc_3ffab0d5-e99d-47b8-9684-ec9d8d063419.png",
2058 |     "bbox": [
2059 |       78,
2060 |       845,
2061 |       53,
2062 |       42
2063 |     ],
2064 |     "instruction": "add a new preferred language",
2065 |     "data_type": "icon",
2066 |     "data_source": "macos"
2067 |   },
2068 |   {
2069 |     "img_filename": "pc_3ffab0d5-e99d-47b8-9684-ec9d8d063419.png",
2070 |     "bbox": [
2071 |       335,
2072 |       982,
2073 |       338,
2074 |       43
2075 |     ],
2076 |     "instruction": "translation languages setting",
2077 |     "data_type": "text",
2078 |     "data_source": "macos"
2079 |   },
2080 |   {
2081 |     "img_filename": "pc_3ffab0d5-e99d-47b8-9684-ec9d8d063419.png",
2082 |     "bbox": [
2083 |       1048,
2084 |       982,
2085 |       185,
2086 |       44
2087 |     ],
2088 |     "instruction": "advanced setting",
2089 |     "data_type": "text",
2090 |     "data_source": "macos"
2091 |   },
2092 |   {
2093 |     "img_filename": "pc_819d6d6a-0ea9-46e0-869d-e17d271381ca.png",
2094 |     "bbox": [
2095 |       635,
2096 |       13,
2097 |       78,
2098 |       51
2099 |     ],
2100 |     "instruction": "check the battery",
2101 |     "data_type": "icon",
2102 |     "data_source": "macos"
2103 |   },
2104 |   {
2105 |     "img_filename": "pc_819d6d6a-0ea9-46e0-869d-e17d271381ca.png",
2106 |     "bbox": [
2107 |       987,
2108 |       13,
2109 |       60,
2110 |       48
2111 |     ],
2112 |     "instruction": "open siri",
2113 |     "data_type": "icon",
2114 |     "data_source": "macos"
2115 |   },
2116 |   {
2117 |     "img_filename": "pc_819d6d6a-0ea9-46e0-869d-e17d271381ca.png",
2118 |     "bbox": [
2119 |       526,
2120 |       274,
2121 |       144,
2122 |       163
2123 |     ],
2124 |     "instruction": "open mind2web folder",
2125 |     "data_type": "text",
2126 |     "data_source": "macos"
2127 |   },
2128 |   {
2129 |     "img_filename": "pc_15bd214f-b45f-48f8-8125-21d79373a99e.png",
2130 |     "bbox": [
2131 |       969,
2132 |       105,
2133 |       272,
2134 |       128
2135 |     ],
2136 |     "instruction": "switch on do not disturb mode",
2137 |     "data_type": "text",
2138 |     "data_source": "macos"
2139 |   },
2140 |   {
2141 |     "img_filename": "pc_15bd214f-b45f-48f8-8125-21d79373a99e.png",
2142 |     "bbox": [
2143 |       969,
2144 |       250,
2145 |       129,
2146 |       126
2147 |     ],
2148 |     "instruction": "adjust keyboard brightness",
2149 |     "data_type": "text",
2150 |     "data_source": "macos"
2151 |   },
2152 |   {
2153 |     "img_filename": "pc_15bd214f-b45f-48f8-8125-21d79373a99e.png",
2154 |     "bbox": [
2155 |       683,
2156 |       683,
2157 |       558,
2158 |       125
2159 |     ],
2160 |     "instruction": "go to music app",
2161 |     "data_type": "text",
2162 |     "data_source": "macos"
2163 |   },
2164 |   {
2165 |     "img_filename": "pc_15bd214f-b45f-48f8-8125-21d79373a99e.png",
2166 |     "bbox": [
2167 |       701,
2168 |       211,
2169 |       189,
2170 |       63
2171 |     ],
2172 |     "instruction": "check bluetooth",
2173 |     "data_type": "text",
2174 |     "data_source": "macos"
2175 |   },
2176 |   {
2177 |     "img_filename": "pc_a4c60b7d-7f78-4255-8804-2dfbd297e09b.png",
2178 |     "bbox": [
2179 |       20,
2180 |       150,
2181 |       217,
2182 |       47
2183 |     ],
2184 |     "instruction": "view files from airdrop",
2185 |     "data_type": "text",
2186 |     "data_source": "macos"
2187 |   },
2188 |   {
2189 |     "img_filename": "pc_a4c60b7d-7f78-4255-8804-2dfbd297e09b.png",
2190 |     "bbox": [
2191 |       19,
2192 |       376,
2193 |       219,
2194 |       44
2195 |     ],
2196 |     "instruction": "view downloads files",
2197 |     "data_type": "text",
2198 |     "data_source": "macos"
2199 |   },
2200 |   {
2201 |     "img_filename": "pc_a4c60b7d-7f78-4255-8804-2dfbd297e09b.png",
2202 |     "bbox": [
2203 |       542,
2204 |       390,
2205 |       133,
2206 |       171
2207 |     ],
2208 |     "instruction": "open synapse folder",
2209 |     "data_type": "text",
2210 |     "data_source": "macos"
2211 |   },
2212 |   {
2213 |     "img_filename": "pc_33eb9249-4ee4-4752-9541-8466efda3845.png",
2214 |     "bbox": [
2215 |       1800,
2216 |       1558,
2217 |       44,
2218 |       52
2219 |     ],
2220 |     "instruction": "zoom out",
2221 |     "data_type": "icon",
2222 |     "data_source": "macos"
2223 |   },
2224 |   {
2225 |     "img_filename": "pc_33eb9249-4ee4-4752-9541-8466efda3845.png",
2226 |     "bbox": [
2227 |       1846,
2228 |       1556,
2229 |       45,
2230 |       55
2231 |     ],
2232 |     "instruction": "zoom in",
2233 |     "data_type": "icon",
2234 |     "data_source": "macos"
2235 |   },
2236 |   {
2237 |     "img_filename": "pc_33eb9249-4ee4-4752-9541-8466efda3845.png",
2238 |     "bbox": [
2239 |       198,
2240 |       104,
2241 |       187,
2242 |       45
2243 |     ],
2244 |     "instruction": "switch to device",
2245 |     "data_type": "text",
2246 |     "data_source": "macos"
2247 |   },
2248 |   {
2249 |     "img_filename": "pc_33eb9249-4ee4-4752-9541-8466efda3845.png",
2250 |     "bbox": [
2251 |       33,
2252 |       1563,
2253 |       283,
2254 |       56
2255 |     ],
2256 |     "instruction": "share my location",
2257 |     "data_type": "text",
2258 |     "data_source": "macos"
2259 |   },
2260 |   {
2261 |     "img_filename": "pc_dde13919-4c5b-49a1-8c8a-7d03f363c7ac.png",
2262 |     "bbox": [
2263 |       837,
2264 |       2,
2265 |       277,
2266 |       57
2267 |     ],
2268 |     "instruction": "check the date and time",
2269 |     "data_type": "icon",
2270 |     "data_source": "macos"
2271 |   },
2272 |   {
2273 |     "img_filename": "pc_dde13919-4c5b-49a1-8c8a-7d03f363c7ac.png",
2274 |     "bbox": [
2275 |       509,
2276 |       1,
2277 |       67,
2278 |       64
2279 |     ],
2280 |     "instruction": "connect wifi",
2281 |     "data_type": "icon",
2282 |     "data_source": "macos"
2283 |   },
2284 |   {
2285 |     "img_filename": "pc_e2bd639a-f297-48fd-bee0-3b7044b0ad57.png",
2286 |     "bbox": [
2287 |       26,
2288 |       453,
2289 |       556,
2290 |       126
2291 |     ],
2292 |     "instruction": "drag the slider to adjust volume",
2293 |     "data_type": "icon",
2294 |     "data_source": "macos"
2295 |   },
2296 |   {
2297 |     "img_filename": "pc_e2bd639a-f297-48fd-bee0-3b7044b0ad57.png",
2298 |     "bbox": [
2299 |       457,
2300 |       165,
2301 |       125,
2302 |       127
2303 |     ],
2304 |     "instruction": "use the screen mirroring",
2305 |     "data_type": "text",
2306 |     "data_source": "macos"
2307 |   },
2308 |   {
2309 |     "img_filename": "pc_7ba3945d-3975-45f3-9145-71805a9723de.png",
2310 |     "bbox": [
2311 |       958,
2312 |       98,
2313 |       82,
2314 |       56
2315 |     ],
2316 |     "instruction": "turn off airdrop",
2317 |     "data_type": "icon",
2318 |     "data_source": "macos"
2319 |   },
2320 |   {
2321 |     "img_filename": "pc_7ba3945d-3975-45f3-9145-71805a9723de.png",
2322 |     "bbox": [
2323 |       471,
2324 |       176,
2325 |       594,
2326 |       69
2327 |     ],
2328 |     "instruction": "set as contacts only",
2329 |     "data_type": "text",
2330 |     "data_source": "macos"
2331 |   },
2332 |   {
2333 |     "img_filename": "pc_e5c3f9ae-de8d-4457-8bdc-7c0aba699f38.png",
2334 |     "bbox": [
2335 |       470,
2336 |       351,
2337 |       594,
2338 |       66
2339 |     ],
2340 |     "instruction": "open work mode ",
2341 |     "data_type": "text",
2342 |     "data_source": "macos"
2343 |   },
2344 |   {
2345 |     "img_filename": "pc_e5c3f9ae-de8d-4457-8bdc-7c0aba699f38.png",
2346 |     "bbox": [
2347 |       469,
2348 |       494,
2349 |       597,
2350 |       62
2351 |     ],
2352 |     "instruction": "check focus preferences",
2353 |     "data_type": "text",
2354 |     "data_source": "macos"
2355 |   },
2356 |   {
2357 |     "img_filename": "pc_e5c3f9ae-de8d-4457-8bdc-7c0aba699f38.png",
2358 |     "bbox": [
2359 |       328,
2360 |       1,
2361 |       75,
2362 |       66
2363 |     ],
2364 |     "instruction": "select input method",
2365 |     "data_type": "icon",
2366 |     "data_source": "macos"
2367 |   },
2368 |   {
2369 |     "img_filename": "pc_be3ea55b-e1f3-478a-8982-a05f37cc910f.png",
2370 |     "bbox": [
2371 |       181,
2372 |       20,
2373 |       67,
2374 |       63
2375 |     ],
2376 |     "instruction": "back to previous page",
2377 |     "data_type": "icon",
2378 |     "data_source": "macos"
2379 |   },
2380 |   {
2381 |     "img_filename": "pc_be3ea55b-e1f3-478a-8982-a05f37cc910f.png",
2382 |     "bbox": [
2383 |       1245,
2384 |       801,
2385 |       70,
2386 |       63
2387 |     ],
2388 |     "instruction": "question about bluetooth",
2389 |     "data_type": "icon",
2390 |     "data_source": "macos"
2391 |   },
2392 |   {
2393 |     "img_filename": "pc_65c6ab58-5f56-4a53-a65d-5e88511bbe0f.png",
2394 |     "bbox": [
2395 |       646,
2396 |       3,
2397 |       68,
2398 |       75
2399 |     ],
2400 |     "instruction": "share this guide to someone",
2401 |     "data_type": "icon",
2402 |     "data_source": "macos"
2403 |   },
2404 |   {
2405 |     "img_filename": "pc_65c6ab58-5f56-4a53-a65d-5e88511bbe0f.png",
2406 |     "bbox": [
2407 |       720,
2408 |       3,
2409 |       56,
2410 |       79
2411 |     ],
2412 |     "instruction": "search in macos user guide",
2413 |     "data_type": "icon",
2414 |     "data_source": "macos"
2415 |   },
2416 |   {
2417 |     "img_filename": "pc_65c6ab58-5f56-4a53-a65d-5e88511bbe0f.png",
2418 |     "bbox": [
2419 |       14,
2420 |       539,
2421 |       475,
2422 |       68
2423 |     ],
2424 |     "instruction": "open bluetooth preferences for me",
2425 |     "data_type": "text",
2426 |     "data_source": "macos"
2427 |   },
2428 |   {
2429 |     "img_filename": "pc_015a0262-924c-4ced-9f4d-23d07f94a657.png",
2430 |     "bbox": [
2431 |       179,
2432 |       169,
2433 |       596,
2434 |       78
2435 |     ],
2436 |     "instruction": "hidden all shortcut",
2437 |     "data_type": "text",
2438 |     "data_source": "macos"
2439 |   },
2440 |   {
2441 |     "img_filename": "pc_015a0262-924c-4ced-9f4d-23d07f94a657.png",
2442 |     "bbox": [
2443 |       180,
2444 |       3,
2445 |       92,
2446 |       69
2447 |     ],
2448 |     "instruction": "open wechat",
2449 |     "data_type": "icon",
2450 |     "data_source": "macos"
2451 |   },
2452 |   {
2453 |     "img_filename": "pc_2773ea59-8493-4c89-957a-d2ec661e6384.png",
2454 |     "bbox": [
2455 |       995,
2456 |       254,
2457 |       222,
2458 |       44
2459 |     ],
2460 |     "instruction": "turn wifi off",
2461 |     "data_type": "text",
2462 |     "data_source": "macos"
2463 |   },
2464 |   {
2465 |     "img_filename": "pc_2773ea59-8493-4c89-957a-d2ec661e6384.png",
2466 |     "bbox": [
2467 |       37,
2468 |       1092,
2469 |       51,
2470 |       47
2471 |     ],
2472 |     "instruction": "add a device",
2473 |     "data_type": "icon",
2474 |     "data_source": "macos"
2475 |   },
2476 |   {
2477 |     "img_filename": "pc_2773ea59-8493-4c89-957a-d2ec661e6384.png",
2478 |     "bbox": [
2479 |       88,
2480 |       1091,
2481 |       53,
2482 |       51
2483 |     ],
2484 |     "instruction": "remove a network connected device",
2485 |     "data_type": "icon",
2486 |     "data_source": "macos"
2487 |   },
2488 |   {
2489 |     "img_filename": "pc_afcc8160-1ef9-4413-8d5e-b3aa88b2a879.png",
2490 |     "bbox": [
2491 |       531,
2492 |       569,
2493 |       516,
2494 |       41
2495 |     ],
2496 |     "instruction": "select the interface in the bar",
2497 |     "data_type": "icon",
2498 |     "data_source": "macos"
2499 |   },
2500 |   {
2501 |     "img_filename": "pc_afcc8160-1ef9-4413-8d5e-b3aa88b2a879.png",
2502 |     "bbox": [
2503 |       905,
2504 |       711,
2505 |       147,
2506 |       48
2507 |     ],
2508 |     "instruction": "create the new service",
2509 |     "data_type": "text",
2510 |     "data_source": "macos"
2511 |   },
2512 |   {
2513 |     "img_filename": "pc_cc362e8f-6ef3-4273-9dfb-baeab56ad760.png",
2514 |     "bbox": [
2515 |       351,
2516 |       457,
2517 |       698,
2518 |       99
2519 |     ],
2520 |     "instruction": "turn on low power mode",
2521 |     "data_type": "text",
2522 |     "data_source": "macos"
2523 |   },
2524 |   {
2525 |     "img_filename": "pc_cc362e8f-6ef3-4273-9dfb-baeab56ad760.png",
2526 |     "bbox": [
2527 |       2,
2528 |       532,
2529 |       302,
2530 |       71
2531 |     ],
2532 |     "instruction": "check battery usage history",
2533 |     "data_type": "text",
2534 |     "data_source": "macos"
2535 |   },
2536 |   {
2537 |     "img_filename": "pc_f344a1f6-cbf8-4115-9c9b-a9dfd00fbfe7.png",
2538 |     "bbox": [
2539 |       444,
2540 |       184,
2541 |       437,
2542 |       56
2543 |     ],
2544 |     "instruction": "show emoji & symbols",
2545 |     "data_type": "text",
2546 |     "data_source": "macos"
2547 |   },
2548 |   {
2549 |     "img_filename": "pc_f344a1f6-cbf8-4115-9c9b-a9dfd00fbfe7.png",
2550 |     "bbox": [
2551 |       445,
2552 |       360,
2553 |       437,
2554 |       61
2555 |     ],
2556 |     "instruction": "open keyboard preferences",
2557 |     "data_type": "text",
2558 |     "data_source": "macos"
2559 |   },
2560 |   {
2561 |     "img_filename": "pc_cddf10f9-43a9-4cce-9b1b-df4aaf216f6d.png",
2562 |     "bbox": [
2563 |       36,
2564 |       916,
2565 |       56,
2566 |       45
2567 |     ],
2568 |     "instruction": "add a new account",
2569 |     "data_type": "icon",
2570 |     "data_source": "macos"
2571 |   },
2572 |   {
2573 |     "img_filename": "pc_cddf10f9-43a9-4cce-9b1b-df4aaf216f6d.png",
2574 |     "bbox": [
2575 |       985,
2576 |       22,
2577 |       323,
2578 |       58
2579 |     ],
2580 |     "instruction": "search for account",
2581 |     "data_type": "text",
2582 |     "data_source": "macos"
2583 |   },
2584 |   {
2585 |     "img_filename": "pc_63f20c0b-0abc-4cd5-a3cb-2ea0cb2ba3c8.png",
2586 |     "bbox": [
2587 |       179,
2588 |       455,
2589 |       979,
2590 |       42
2591 |     ],
2592 |     "instruction": "cancel the calculator",
2593 |     "data_type": "text",
2594 |     "data_source": "macos"
2595 |   },
2596 |   {
2597 |     "img_filename": "pc_63f20c0b-0abc-4cd5-a3cb-2ea0cb2ba3c8.png",
2598 |     "bbox": [
2599 |       707,
2600 |       245,
2601 |       146,
2602 |       46
2603 |     ],
2604 |     "instruction": "switch to privacy",
2605 |     "data_type": "text",
2606 |     "data_source": "macos"
2607 |   },
2608 |   {
2609 |     "img_filename": "pc_514bf9d5-ca63-4d33-94cb-dc1b11994199.png",
2610 |     "bbox": [
2611 |       1024,
2612 |       444,
2613 |       212,
2614 |       186
2615 |     ],
2616 |     "instruction": "switch to alerts mode",
2617 |     "data_type": "text",
2618 |     "data_source": "macos"
2619 |   },
2620 |   {
2621 |     "img_filename": "pc_514bf9d5-ca63-4d33-94cb-dc1b11994199.png",
2622 |     "bbox": [
2623 |       585,
2624 |       329,
2625 |       96,
2626 |       65
2627 |     ],
2628 |     "instruction": "disable \"allow notifications\" setting",
2629 |     "data_type": "icon",
2630 |     "data_source": "macos"
2631 |   },
2632 |   {
2633 |     "img_filename": "pc_ed2173e7-c929-4f52-9777-db36a36e0c3e.png",
2634 |     "bbox": [
2635 |       529,
2636 |       267,
2637 |       91,
2638 |       72
2639 |     ],
2640 |     "instruction": "switch on do not disturb mode",
2641 |     "data_type": "icon",
2642 |     "data_source": "macos"
2643 |   },
2644 |   {
2645 |     "img_filename": "pc_b93bf9fd-afa5-40c0-b103-48f235f3ffe0.png",
2646 |     "bbox": [
2647 |       965,
2648 |       226,
2649 |       283,
2650 |       47
2651 |     ],
2652 |     "instruction": "change user's password",
2653 |     "data_type": "text",
2654 |     "data_source": "macos"
2655 |   },
2656 |   {
2657 |     "img_filename": "pc_b93bf9fd-afa5-40c0-b103-48f235f3ffe0.png",
2658 |     "bbox": [
2659 |       25,
2660 |       887,
2661 |       95,
2662 |       92
2663 |     ],
2664 |     "instruction": "unlock to make changes",
2665 |     "data_type": "icon",
2666 |     "data_source": "macos"
2667 |   },
2668 |   {
2669 |     "img_filename": "pc_6ca110ef-dfac-49fe-a9eb-4ba4f621cbdc.png",
2670 |     "bbox": [
2671 |       1,
2672 |       766,
2673 |       396,
2674 |       67
2675 |     ],
2676 |     "instruction": "check app limits",
2677 |     "data_type": "text",
2678 |     "data_source": "macos"
2679 |   },
2680 |   {
2681 |     "img_filename": "pc_6ca110ef-dfac-49fe-a9eb-4ba4f621cbdc.png",
2682 |     "bbox": [
2683 |       1,
2684 |       896,
2685 |       395,
2686 |       62
2687 |     ],
2688 |     "instruction": "view the always allowed apps",
2689 |     "data_type": "text",
2690 |     "data_source": "macos"
2691 |   },
2692 |   {
2693 |     "img_filename": "pc_cb3fc7f0-9ed8-4692-8610-0f32b929a5a3.png",
2694 |     "bbox": [
2695 |       203,
2696 |       400,
2697 |       689,
2698 |       56
2699 |     ],
2700 |     "instruction": "turn on \"user apple watch to unlock\"",
2701 |     "data_type": "text",
2702 |     "data_source": "macos"
2703 |   },
2704 |   {
2705 |     "img_filename": "pc_cb3fc7f0-9ed8-4692-8610-0f32b929a5a3.png",
2706 |     "bbox": [
2707 |       675,
2708 |       154,
2709 |       139,
2710 |       46
2711 |     ],
2712 |     "instruction": "view firewall",
2713 |     "data_type": "text",
2714 |     "data_source": "macos"
2715 |   },
2716 |   {
2717 |     "img_filename": "pc_8c34d9b6-4f21-4cfa-a277-8a4fe45c9ef7.png",
2718 |     "bbox": [
2719 |       672,
2720 |       149,
2721 |       183,
2722 |       50
2723 |     ],
2724 |     "instruction": "switch to time zone settings",
2725 |     "data_type": "text",
2726 |     "data_source": "macos"
2727 |   },
2728 |   {
2729 |     "img_filename": "pc_35c339d2-810d-4bbe-93db-0c1db024638f.png",
2730 |     "bbox": [
2731 |       1827,
2732 |       99,
2733 |       251,
2734 |       236
2735 |     ],
2736 |     "instruction": "open pycharm",
2737 |     "data_type": "text",
2738 |     "data_source": "macos"
2739 |   },
2740 |   {
2741 |     "img_filename": "pc_35c339d2-810d-4bbe-93db-0c1db024638f.png",
2742 |     "bbox": [
2743 |       473,
2744 |       701,
2745 |       299,
2746 |       286
2747 |     ],
2748 |     "instruction": "open anaconda",
2749 |     "data_type": "text",
2750 |     "data_source": "macos"
2751 |   },
2752 |   {
2753 |     "img_filename": "pc_75eae2f4-193e-4365-a5f1-b9b5340d02ff.png",
2754 |     "bbox": [
2755 |       1284,
2756 |       0,
2757 |       90,
2758 |       106
2759 |     ],
2760 |     "instruction": "delete this email",
2761 |     "data_type": "icon",
2762 |     "data_source": "macos"
2763 |   },
2764 |   {
2765 |     "img_filename": "pc_75eae2f4-193e-4365-a5f1-b9b5340d02ff.png",
2766 |     "bbox": [
2767 |       1667,
2768 |       0,
2769 |       82,
2770 |       103
2771 |     ],
2772 |     "instruction": "forward email",
2773 |     "data_type": "icon",
2774 |     "data_source": "macos"
2775 |   },
2776 |   {
2777 |     "img_filename": "pc_1e16b18c-5d91-4155-9e49-4ad5d9453fc7.png",
2778 |     "bbox": [
2779 |       872,
2780 |       0,
2781 |       98,
2782 |       106
2783 |     ],
2784 |     "instruction": "search in emails",
2785 |     "data_type": "icon",
2786 |     "data_source": "macos"
2787 |   },
2788 |   {
2789 |     "img_filename": "pc_1e16b18c-5d91-4155-9e49-4ad5d9453fc7.png",
2790 |     "bbox": [
2791 |       561,
2792 |       693,
2793 |       90,
2794 |       52
2795 |     ],
2796 |     "instruction": "view attachment",
2797 |     "data_type": "icon",
2798 |     "data_source": "macos"
2799 |   },
2800 |   {
2801 |     "img_filename": "pc_63a274e4-38e1-4a50-a102-33b31aee3a62.png",
2802 |     "bbox": [
2803 |       1013,
2804 |       19,
2805 |       100,
2806 |       72
2807 |     ],
2808 |     "instruction": "add images",
2809 |     "data_type": "icon",
2810 |     "data_source": "macos"
2811 |   },
2812 |   {
2813 |     "img_filename": "pc_63a274e4-38e1-4a50-a102-33b31aee3a62.png",
2814 |     "bbox": [
2815 |       1025,
2816 |       106,
2817 |       78,
2818 |       56
2819 |     ],
2820 |     "instruction": "add a recipient",
2821 |     "data_type": "icon",
2822 |     "data_source": "macos"
2823 |   },
2824 |   {
2825 |     "img_filename": "pc_63a274e4-38e1-4a50-a102-33b31aee3a62.png",
2826 |     "bbox": [
2827 |       808,
2828 |       19,
2829 |       84,
2830 |       72
2831 |     ],
2832 |     "instruction": "adjust font size",
2833 |     "data_type": "icon",
2834 |     "data_source": "macos"
2835 |   },
2836 |   {
2837 |     "img_filename": "pc_5392df82-37d6-41f3-838d-2afa1062dcc8.png",
2838 |     "bbox": [
2839 |       2,
2840 |       917,
2841 |       374,
2842 |       63
2843 |     ],
2844 |     "instruction": "check junk emails",
2845 |     "data_type": "text",
2846 |     "data_source": "macos"
2847 |   },
2848 |   {
2849 |     "img_filename": "pc_5392df82-37d6-41f3-838d-2afa1062dcc8.png",
2850 |     "bbox": [
2851 |       376,
2852 |       103,
2853 |       563,
2854 |       157
2855 |     ],
2856 |     "instruction": "open the first email from researchgate",
2857 |     "data_type": "text",
2858 |     "data_source": "macos"
2859 |   },
2860 |   {
2861 |     "img_filename": "pc_fe30b885-865a-442e-a8f5-0e9924f256fd.png",
2862 |     "bbox": [
2863 |       42,
2864 |       567,
2865 |       1183,
2866 |       736
2867 |     ],
2868 |     "instruction": "open the image in memo",
2869 |     "data_type": "icon",
2870 |     "data_source": "macos"
2871 |   },
2872 |   {
2873 |     "img_filename": "pc_fe30b885-865a-442e-a8f5-0e9924f256fd.png",
2874 |     "bbox": [
2875 |       918,
2876 |       9,
2877 |       95,
2878 |       81
2879 |     ],
2880 |     "instruction": "lock the memo",
2881 |     "data_type": "icon",
2882 |     "data_source": "macos"
2883 |   },
2884 |   {
2885 |     "img_filename": "pc_9237faf6-9f95-4157-9d0b-aafa67f7b675.png",
2886 |     "bbox": [
2887 |       1103,
2888 |       87,
2889 |       230,
2890 |       58
2891 |     ],
2892 |     "instruction": "share throught airdrop",
2893 |     "data_type": "text",
2894 |     "data_source": "macos"
2895 |   },
2896 |   {
2897 |     "img_filename": "pc_1d6fb8dc-8ba5-4c0d-a532-188e5d579686.png",
2898 |     "bbox": [
2899 |       18,
2900 |       104,
2901 |       405,
2902 |       57
2903 |     ],
2904 |     "instruction": "search in app store",
2905 |     "data_type": "text",
2906 |     "data_source": "macos"
2907 |   },
2908 |   {
2909 |     "img_filename": "pc_1d6fb8dc-8ba5-4c0d-a532-188e5d579686.png",
2910 |     "bbox": [
2911 |       1917,
2912 |       877,
2913 |       79,
2914 |       143
2915 |     ],
2916 |     "instruction": "next page",
2917 |     "data_type": "icon",
2918 |     "data_source": "macos"
2919 |   },
2920 |   {
2921 |     "img_filename": "pc_6d0b3e39-872b-48ec-b894-0490106eb629.png",
2922 |     "bbox": [
2923 |       1880,
2924 |       10,
2925 |       81,
2926 |       84
2927 |     ],
2928 |     "instruction": "share this page",
2929 |     "data_type": "icon",
2930 |     "data_source": "macos"
2931 |   },
2932 |   {
2933 |     "img_filename": "pc_6d0b3e39-872b-48ec-b894-0490106eb629.png",
2934 |     "bbox": [
2935 |       172,
2936 |       15,
2937 |       70,
2938 |       75
2939 |     ],
2940 |     "instruction": "back to previous page",
2941 |     "data_type": "icon",
2942 |     "data_source": "macos"
2943 |   },
2944 |   {
2945 |     "img_filename": "pc_8be15b4a-65cc-4e89-94a2-3b6b571f41e2.png",
2946 |     "bbox": [
2947 |       1054,
2948 |       211,
2949 |       156,
2950 |       81
2951 |     ],
2952 |     "instruction": "get the bear markdown notes app",
2953 |     "data_type": "icon",
2954 |     "data_source": "macos"
2955 |   },
2956 |   {
2957 |     "img_filename": "pc_8be15b4a-65cc-4e89-94a2-3b6b571f41e2.png",
2958 |     "bbox": [
2959 |       517,
2960 |       323,
2961 |       699,
2962 |       143
2963 |     ],
2964 |     "instruction": "view the pixelmator app",
2965 |     "data_type": "text",
2966 |     "data_source": "macos"
2967 |   },
2968 |   {
2969 |     "img_filename": "pc_4a9b8c51-5db6-4325-a6e0-8a0f9801e25d.png",
2970 |     "bbox": [
2971 |       504,
2972 |       250,
2973 |       459,
2974 |       96
2975 |     ],
2976 |     "instruction": "open business categories",
2977 |     "data_type": "text",
2978 |     "data_source": "macos"
2979 |   },
2980 |   {
2981 |     "img_filename": "pc_4a9b8c51-5db6-4325-a6e0-8a0f9801e25d.png",
2982 |     "bbox": [
2983 |       1795,
2984 |       1229,
2985 |       102,
2986 |       99
2987 |     ],
2988 |     "instruction": "download fantastical from icloud",
2989 |     "data_type": "icon",
2990 |     "data_source": "macos"
2991 |   },
2992 |   {
2993 |     "img_filename": "pc_a2300b43-a1e0-4da9-aed4-3543b507d9a3.png",
2994 |     "bbox": [
2995 |       518,
2996 |       231,
2997 |       685,
2998 |       641
2999 |     ],
3000 |     "instruction": "view the 2023 app store awards",
3001 |     "data_type": "text",
3002 |     "data_source": "macos"
3003 |   },
3004 |   {
3005 |     "img_filename": "pc_01625ab1-9757-4dc4-8da0-a9c946d3b76a.png",
3006 |     "bbox": [
3007 |       355,
3008 |       39,
3009 |       60,
3010 |       45
3011 |     ],
3012 |     "instruction": "back to previous folder",
3013 |     "data_type": "icon",
3014 |     "data_source": "macos"
3015 |   },
3016 |   {
3017 |     "img_filename": "pc_01625ab1-9757-4dc4-8da0-a9c946d3b76a.png",
3018 |     "bbox": [
3019 |       886,
3020 |       175,
3021 |       147,
3022 |       209
3023 |     ],
3024 |     "instruction": "open d1_one",
3025 |     "data_type": "text",
3026 |     "data_source": "macos"
3027 |   },
3028 |   {
3029 |     "img_filename": "pc_09fff6da-5dd8-4288-a0e7-f503273c1908.png",
3030 |     "bbox": [
3031 |       1411,
3032 |       804,
3033 |       152,
3034 |       65
3035 |     ],
3036 |     "instruction": "open the file",
3037 |     "data_type": "text",
3038 |     "data_source": "macos"
3039 |   },
3040 |   {
3041 |     "img_filename": "pc_09fff6da-5dd8-4288-a0e7-f503273c1908.png",
3042 |     "bbox": [
3043 |       354,
3044 |       809,
3045 |       241,
3046 |       49
3047 |     ],
3048 |     "instruction": "create a new document",
3049 |     "data_type": "text",
3050 |     "data_source": "macos"
3051 |   },
3052 |   {
3053 |     "img_filename": "pc_f01bc231-56c4-4aba-9dd4-fd6271be0f8e.png",
3054 |     "bbox": [
3055 |       475,
3056 |       248,
3057 |       247,
3058 |       48
3059 |     ],
3060 |     "instruction": "show the files in list",
3061 |     "data_type": "text",
3062 |     "data_source": "macos"
3063 |   },
3064 |   {
3065 |     "img_filename": "pc_bf63137d-d442-4bb9-a145-e07cb48abb64.png",
3066 |     "bbox": [
3067 |       307,
3068 |       192,
3069 |       226,
3070 |       340
3071 |     ],
3072 |     "instruction": "open a blank document",
3073 |     "data_type": "text",
3074 |     "data_source": "macos"
3075 |   },
3076 |   {
3077 |     "img_filename": "pc_bf63137d-d442-4bb9-a145-e07cb48abb64.png",
3078 |     "bbox": [
3079 |       29,
3080 |       109,
3081 |       118,
3082 |       127
3083 |     ],
3084 |     "instruction": "my account",
3085 |     "data_type": "icon",
3086 |     "data_source": "macos"
3087 |   },
3088 |   {
3089 |     "img_filename": "pc_e73c6749-043f-4a24-8e56-6c829f78255d.png",
3090 |     "bbox": [
3091 |       513,
3092 |       134,
3093 |       63,
3094 |       57
3095 |     ],
3096 |     "instruction": "enlarge font size",
3097 |     "data_type": "icon",
3098 |     "data_source": "macos"
3099 |   },
3100 |   {
3101 |     "img_filename": "pc_e73c6749-043f-4a24-8e56-6c829f78255d.png",
3102 |     "bbox": [
3103 |       438,
3104 |       2,
3105 |       61,
3106 |       61
3107 |     ],
3108 |     "instruction": "save file",
3109 |     "data_type": "icon",
3110 |     "data_source": "macos"
3111 |   },
3112 |   {
3113 |     "img_filename": "pc_8fc1a1fb-00b9-4fe6-8fd3-4784ab2274e8.png",
3114 |     "bbox": [
3115 |       645,
3116 |       127,
3117 |       94,
3118 |       133
3119 |     ],
3120 |     "instruction": "add a new pen",
3121 |     "data_type": "text",
3122 |     "data_source": "macos"
3123 |   },
3124 |   {
3125 |     "img_filename": "pc_8fc1a1fb-00b9-4fe6-8fd3-4784ab2274e8.png",
3126 |     "bbox": [
3127 |       352,
3128 |       133,
3129 |       85,
3130 |       111
3131 |     ],
3132 |     "instruction": "choose the red pen",
3133 |     "data_type": "icon",
3134 |     "data_source": "macos"
3135 |   },
3136 |   {
3137 |     "img_filename": "pc_0205fe68-a787-4651-8cfa-495802d6beb6.png",
3138 |     "bbox": [
3139 |       221,
3140 |       131,
3141 |       126,
3142 |       120
3143 |     ],
3144 |     "instruction": "adjust orientation",
3145 |     "data_type": "text",
3146 |     "data_source": "macos"
3147 |   },
3148 |   {
3149 |     "img_filename": "pc_0205fe68-a787-4651-8cfa-495802d6beb6.png",
3150 |     "bbox": [
3151 |       884,
3152 |       175,
3153 |       161,
3154 |       44
3155 |     ],
3156 |     "instruction": "adjust the left indent",
3157 |     "data_type": "icon",
3158 |     "data_source": "macos"
3159 |   },
3160 |   {
3161 |     "img_filename": "pc_00b0c9f6-b24f-4bff-b492-12117b50e63b.png",
3162 |     "bbox": [
3163 |       95,
3164 |       256,
3165 |       107,
3166 |       114
3167 |     ],
3168 |     "instruction": "insert a shape",
3169 |     "data_type": "text",
3170 |     "data_source": "macos"
3171 |   },
3172 |   {
3173 |     "img_filename": "pc_00b0c9f6-b24f-4bff-b492-12117b50e63b.png",
3174 |     "bbox": [
3175 |       580,
3176 |       258,
3177 |       123,
3178 |       112
3179 |     ],
3180 |     "instruction": "add a screenshot",
3181 |     "data_type": "text",
3182 |     "data_source": "macos"
3183 |   },
3184 |   {
3185 |     "img_filename": "pc_3f805781-f2fe-45dc-8bc5-e62d12f1c359.png",
3186 |     "bbox": [
3187 |       879,
3188 |       797,
3189 |       212,
3190 |       223
3191 |     ],
3192 |     "instruction": "add a sunburst chart",
3193 |     "data_type": "icon",
3194 |     "data_source": "macos"
3195 |   },
3196 |   {
3197 |     "img_filename": "pc_3f805781-f2fe-45dc-8bc5-e62d12f1c359.png",
3198 |     "bbox": [
3199 |       283,
3200 |       0,
3201 |       98,
3202 |       61
3203 |     ],
3204 |     "instruction": "turn on auto save",
3205 |     "data_type": "icon",
3206 |     "data_source": "macos"
3207 |   },
3208 |   {
3209 |     "img_filename": "pc_41147eb1-3e27-4aa0-b9f9-71363b9be5c9.png",
3210 |     "bbox": [
3211 |       407,
3212 |       771,
3213 |       127,
3214 |       41
3215 |     ],
3216 |     "instruction": "switch to 20 size font",
3217 |     "data_type": "text",
3218 |     "data_source": "macos"
3219 |   },
3220 |   {
3221 |     "img_filename": "pc_41147eb1-3e27-4aa0-b9f9-71363b9be5c9.png",
3222 |     "bbox": [
3223 |       1554,
3224 |       66,
3225 |       142,
3226 |       57
3227 |     ],
3228 |     "instruction": "share the word file",
3229 |     "data_type": "text",
3230 |     "data_source": "macos"
3231 |   },
3232 |   {
3233 |     "img_filename": "pc_c2ffa4d3-4cf7-4813-986d-d741f1260c4c.png",
3234 |     "bbox": [
3235 |       205,
3236 |       420,
3237 |       812,
3238 |       55
3239 |     ],
3240 |     "instruction": "choose ms pgothic for heading",
3241 |     "data_type": "text",
3242 |     "data_source": "macos"
3243 |   },
3244 |   {
3245 |     "img_filename": "pc_c2ffa4d3-4cf7-4813-986d-d741f1260c4c.png",
3246 |     "bbox": [
3247 |       205,
3248 |       1342,
3249 |       814,
3250 |       66
3251 |     ],
3252 |     "instruction": "choose apple chancery",
3253 |     "data_type": "text",
3254 |     "data_source": "macos"
3255 |   },
3256 |   {
3257 |     "img_filename": "pc_71e8f8bd-7ff0-4846-ada6-ce337a0f5ebc.png",
3258 |     "bbox": [
3259 |       358,
3260 |       65,
3261 |       127,
3262 |       61
3263 |     ],
3264 |     "instruction": "switch to design",
3265 |     "data_type": "text",
3266 |     "data_source": "macos"
3267 |   },
3268 |   {
3269 |     "img_filename": "pc_9eebe928-6d8a-4de5-8212-039d0282b082.png",
3270 |     "bbox": [
3271 |       1316,
3272 |       553,
3273 |       69,
3274 |       60
3275 |     ],
3276 |     "instruction": "rotate title box",
3277 |     "data_type": "icon",
3278 |     "data_source": "macos"
3279 |   },
3280 |   {
3281 |     "img_filename": "pc_b113f651-ec99-4a84-8c15-ff0fc1aee615.png",
3282 |     "bbox": [
3283 |       1197,
3284 |       572,
3285 |       901,
3286 |       64
3287 |     ],
3288 |     "instruction": "bullets and numbering settings",
3289 |     "data_type": "text",
3290 |     "data_source": "macos"
3291 |   },
3292 |   {
3293 |     "img_filename": "pc_38236247-66f3-4ad3-aeb0-bc4783335a46.png",
3294 |     "bbox": [
3295 |       393,
3296 |       1087,
3297 |       373,
3298 |       61
3299 |     ],
3300 |     "instruction": "sign up for free",
3301 |     "data_type": "text",
3302 |     "data_source": "macos"
3303 |   },
3304 |   {
3305 |     "img_filename": "pc_38236247-66f3-4ad3-aeb0-bc4783335a46.png",
3306 |     "bbox": [
3307 |       393,
3308 |       1173,
3309 |       378,
3310 |       61
3311 |     ],
3312 |     "instruction": "sign in",
3313 |     "data_type": "text",
3314 |     "data_source": "macos"
3315 |   },
3316 |   {
3317 |     "img_filename": "pc_18a939a5-8f77-4519-9c7a-d62b11e7b878.png",
3318 |     "bbox": [
3319 |       185,
3320 |       253,
3321 |       783,
3322 |       84
3323 |     ],
3324 |     "instruction": "input the email address",
3325 |     "data_type": "icon",
3326 |     "data_source": "macos"
3327 |   },
3328 |   {
3329 |     "img_filename": "pc_ac718327-53b2-49c1-8707-d12220913fc1.png",
3330 |     "bbox": [
3331 |       1511,
3332 |       55,
3333 |       58,
3334 |       58
3335 |     ],
3336 |     "instruction": "debug",
3337 |     "data_type": "icon",
3338 |     "data_source": "macos"
3339 |   },
3340 |   {
3341 |     "img_filename": "pc_45480159-6b1b-4528-9109-dab1905c62b7.png",
3342 |     "bbox": [
3343 |       520,
3344 |       349,
3345 |       184,
3346 |       50
3347 |     ],
3348 |     "instruction": "check file colors",
3349 |     "data_type": "text",
3350 |     "data_source": "macos"
3351 |   },
3352 |   {
3353 |     "img_filename": "pc_7656e3c5-00b1-4be7-9103-9890b837342f.png",
3354 |     "bbox": [
3355 |       0,
3356 |       424,
3357 |       467,
3358 |       48
3359 |     ],
3360 |     "instruction": "check python interpreter",
3361 |     "data_type": "text",
3362 |     "data_source": "macos"
3363 |   },
3364 |   {
3365 |     "img_filename": "pc_7656e3c5-00b1-4be7-9103-9890b837342f.png",
3366 |     "bbox": [
3367 |       0,
3368 |       282,
3369 |       467,
3370 |       45
3371 |     ],
3372 |     "instruction": "view plugins",
3373 |     "data_type": "text",
3374 |     "data_source": "macos"
3375 |   },
3376 |   {
3377 |     "img_filename": "pc_8c77197f-8be9-485a-bf32-989a8a66fbae.png",
3378 |     "bbox": [
3379 |       748,
3380 |       158,
3381 |       957,
3382 |       51
3383 |     ],
3384 |     "instruction": "select python interpreter",
3385 |     "data_type": "icon",
3386 |     "data_source": "macos"
3387 |   },
3388 |   {
3389 |     "img_filename": "pc_8c77197f-8be9-485a-bf32-989a8a66fbae.png",
3390 |     "bbox": [
3391 |       509,
3392 |       256,
3393 |       50,
3394 |       51
3395 |     ],
3396 |     "instruction": "add a new package",
3397 |     "data_type": "icon",
3398 |     "data_source": "macos"
3399 |   },
3400 |   {
3401 |     "img_filename": "pc_155db9b3-fe88-41cb-81d1-d51f37ba5970.png",
3402 |     "bbox": [
3403 |       519,
3404 |       139,
3405 |       400,
3406 |       68
3407 |     ],
3408 |     "instruction": "cancel showing plots in tool windows",
3409 |     "data_type": "text",
3410 |     "data_source": "macos"
3411 |   },
3412 |   {
3413 |     "img_filename": "pc_668258b4-f214-4a57-a500-d03aa94b0e65.png",
3414 |     "bbox": [
3415 |       38,
3416 |       1009,
3417 |       60,
3418 |       49
3419 |     ],
3420 |     "instruction": "pause the debugger",
3421 |     "data_type": "icon",
3422 |     "data_source": "macos"
3423 |   },
3424 |   {
3425 |     "img_filename": "pc_668258b4-f214-4a57-a500-d03aa94b0e65.png",
3426 |     "bbox": [
3427 |       2192,
3428 |       779,
3429 |       66,
3430 |       55
3431 |     ],
3432 |     "instruction": "minimize the debug panel",
3433 |     "data_type": "icon",
3434 |     "data_source": "macos"
3435 |   },
3436 |   {
3437 |     "img_filename": "pc_f656ce46-13af-4cd1-9126-f3c45a4e696c.png",
3438 |     "bbox": [
3439 |       78,
3440 |       603,
3441 |       1046,
3442 |       36
3443 |     ],
3444 |     "instruction": "select the gzip file format",
3445 |     "data_type": "text",
3446 |     "data_source": "macos"
3447 |   },
3448 |   {
3449 |     "img_filename": "pc_e9abfe29-8665-47e1-ba30-f79a0089278a.png",
3450 |     "bbox": [
3451 |       473,
3452 |       190,
3453 |       252,
3454 |       52
3455 |     ],
3456 |     "instruction": "hide menu icon",
3457 |     "data_type": "text",
3458 |     "data_source": "macos"
3459 |   },
3460 |   {
3461 |     "img_filename": "pc_e9abfe29-8665-47e1-ba30-f79a0089278a.png",
3462 |     "bbox": [
3463 |       685,
3464 |       55,
3465 |       111,
3466 |       95
3467 |     ],
3468 |     "instruction": "switch to display",
3469 |     "data_type": "text",
3470 |     "data_source": "macos"
3471 |   },
3472 |   {
3473 |     "img_filename": "pc_ec085abc-591d-4dcc-830c-defddc958f47.png",
3474 |     "bbox": [
3475 |       351,
3476 |       571,
3477 |       363,
3478 |       44
3479 |     ],
3480 |     "instruction": "buy betterdisplay pro",
3481 |     "data_type": "text",
3482 |     "data_source": "macos"
3483 |   },
3484 |   {
3485 |     "img_filename": "pc_c69e46cd-d2a9-4dbf-8d0a-4532b750a691.png",
3486 |     "bbox": [
3487 |       185,
3488 |       1132,
3489 |       122,
3490 |       112
3491 |     ],
3492 |     "instruction": "start record",
3493 |     "data_type": "icon",
3494 |     "data_source": "macos"
3495 |   },
3496 |   {
3497 |     "img_filename": "pc_141ec1f5-ccf8-42f0-8b0b-8aba284ce81a.png",
3498 |     "bbox": [
3499 |       1381,
3500 |       17,
3501 |       86,
3502 |       77
3503 |     ],
3504 |     "instruction": "search in activity monitor",
3505 |     "data_type": "icon",
3506 |     "data_source": "macos"
3507 |   },
3508 |   {
3509 |     "img_filename": "pc_141ec1f5-ccf8-42f0-8b0b-8aba284ce81a.png",
3510 |     "bbox": [
3511 |       1,
3512 |       360,
3513 |       1475,
3514 |       51
3515 |     ],
3516 |     "instruction": "check the google chrome app",
3517 |     "data_type": "icon",
3518 |     "data_source": "macos"
3519 |   },
3520 |   {
3521 |     "img_filename": "pc_857c0a8b-5f0f-4d6a-966f-789b594622c9.png",
3522 |     "bbox": [
3523 |       555,
3524 |       18,
3525 |       79,
3526 |       67
3527 |     ],
3528 |     "instruction": "terminate this process",
3529 |     "data_type": "icon",
3530 |     "data_source": "macos"
3531 |   },
3532 |   {
3533 |     "img_filename": "pc_857c0a8b-5f0f-4d6a-966f-789b594622c9.png",
3534 |     "bbox": [
3535 |       640,
3536 |       17,
3537 |       70,
3538 |       70
3539 |     ],
3540 |     "instruction": "detail information",
3541 |     "data_type": "icon",
3542 |     "data_source": "macos"
3543 |   },
3544 |   {
3545 |     "img_filename": "pc_dc199113-7c34-4cc2-85cb-e9b552b87b05.png",
3546 |     "bbox": [
3547 |       1024,
3548 |       20,
3549 |       70,
3550 |       69
3551 |     ],
3552 |     "instruction": "add a shortcut",
3553 |     "data_type": "icon",
3554 |     "data_source": "macos"
3555 |   },
3556 |   {
3557 |     "img_filename": "pc_dc199113-7c34-4cc2-85cb-e9b552b87b05.png",
3558 |     "bbox": [
3559 |       1,
3560 |       100,
3561 |       450,
3562 |       64
3563 |     ],
3564 |     "instruction": "open gallery",
3565 |     "data_type": "text",
3566 |     "data_source": "macos"
3567 |   },
3568 |   {
3569 |     "img_filename": "pc_2c2b6cbd-39cf-4bd2-80aa-d953890c21e5.png",
3570 |     "bbox": [
3571 |       1496,
3572 |       204,
3573 |       257,
3574 |       44
3575 |     ],
3576 |     "instruction": "switch to apps",
3577 |     "data_type": "text",
3578 |     "data_source": "macos"
3579 |   },
3580 |   {
3581 |     "img_filename": "pc_2c2b6cbd-39cf-4bd2-80aa-d953890c21e5.png",
3582 |     "bbox": [
3583 |       1254,
3584 |       628,
3585 |       491,
3586 |       58
3587 |     ],
3588 |     "instruction": "add a send message action",
3589 |     "data_type": "text",
3590 |     "data_source": "macos"
3591 |   },
3592 |   {
3593 |     "img_filename": "pc_64c386c3-9d87-4c91-b712-c1d9ab9216ae.png",
3594 |     "bbox": [
3595 |       1144,
3596 |       16,
3597 |       66,
3598 |       71
3599 |     ],
3600 |     "instruction": "run the shortcut",
3601 |     "data_type": "icon",
3602 |     "data_source": "macos"
3603 |   },
3604 |   {
3605 |     "img_filename": "pc_64c386c3-9d87-4c91-b712-c1d9ab9216ae.png",
3606 |     "bbox": [
3607 |       1037,
3608 |       13,
3609 |       81,
3610 |       79
3611 |     ],
3612 |     "instruction": "share the shortcut",
3613 |     "data_type": "icon",
3614 |     "data_source": "macos"
3615 |   },
3616 |   {
3617 |     "img_filename": "pc_b313a50d-ac5e-4189-beb9-3f5ceb67e536.png",
3618 |     "bbox": [
3619 |       501,
3620 |       124,
3621 |       212,
3622 |       246
3623 |     ],
3624 |     "instruction": "open paper named flamingo",
3625 |     "data_type": "text",
3626 |     "data_source": "macos"
3627 |   },
3628 |   {
3629 |     "img_filename": "pc_b313a50d-ac5e-4189-beb9-3f5ceb67e536.png",
3630 |     "bbox": [
3631 |       1426,
3632 |       14,
3633 |       80,
3634 |       69
3635 |     ],
3636 |     "instruction": "search in files",
3637 |     "data_type": "icon",
3638 |     "data_source": "macos"
3639 |   },
3640 |   {
3641 |     "img_filename": "pc_97ce9c11-f20b-4294-982a-f01241c81576.png",
3642 |     "bbox": [
3643 |       746,
3644 |       19,
3645 |       62,
3646 |       65
3647 |     ],
3648 |     "instruction": "enlarge the font",
3649 |     "data_type": "icon",
3650 |     "data_source": "macos"
3651 |   },
3652 |   {
3653 |     "img_filename": "pc_97ce9c11-f20b-4294-982a-f01241c81576.png",
3654 |     "bbox": [
3655 |       836,
3656 |       19,
3657 |       523,
3658 |       66
3659 |     ],
3660 |     "instruction": "search in dictionary",
3661 |     "data_type": "text",
3662 |     "data_source": "macos"
3663 |   },
3664 |   {
3665 |     "img_filename": "pc_1inyhi.png",
3666 |     "bbox": [
3667 |       12,
3668 |       433,
3669 |       67,
3670 |       64
3671 |     ],
3672 |     "instruction": "open vscode",
3673 |     "data_type": "icon",
3674 |     "data_source": "shop"
3675 |   },
3676 |   {
3677 |     "img_filename": "pc_8g2jf6.png",
3678 |     "bbox": [
3679 |       797,
3680 |       128,
3681 |       50,
3682 |       37
3683 |     ],
3684 |     "instruction": "adjust font color",
3685 |     "data_type": "icon",
3686 |     "data_source": "shop"
3687 |   },
3688 |   {
3689 |     "img_filename": "pc_b5q1tt.png",
3690 |     "bbox": [1547, 358, 442, 45],
3691 |     "instruction": "view battery usage of last 10 days",
3692 |     "data_type": "text",
3693 |     "data_source": "shop"
3694 |   },
3695 |   {
3696 |     "img_filename": "pc_drcjo3.png",
3697 |     "bbox": [
3698 |       2691, 
3699 |       1569, 
3700 |       81, 
3701 |       66
3702 |     ],
3703 |     "instruction": "download microsoft word from cloud",
3704 |     "data_type": "icon",
3705 |     "data_source": "shop"
3706 |   },
3707 |   {
3708 |     "img_filename": "pc_dyxzuj.png",
3709 |     "bbox": [
3710 |       1337,
3711 |       275,
3712 |       306,
3713 |       48
3714 |     ],
3715 |     "instruction": "adjust mouse speed",
3716 |     "data_type": "icon",
3717 |     "data_source": "shop"
3718 |   },
3719 |   {
3720 |     "img_filename": "pc_dzpr8m.png",
3721 |     "bbox": [
3722 |       1475,
3723 |       473,
3724 |       96,
3725 |       35
3726 |     ],
3727 |     "instruction": "empty the trash",
3728 |     "data_type": "icon",
3729 |     "data_source": "shop"
3730 |   },
3731 |   {
3732 |     "img_filename": "pc_f6eng1.png",
3733 |     "bbox": [702, 857, 337, 335],
3734 |     "instruction": "play dance pop station music",
3735 |     "data_type": "icon",
3736 |     "data_source": "shop"
3737 |   },
3738 |   {
3739 |     "img_filename": "pc_gtda7d.png",
3740 |     "bbox": [2425, 403, 84, 79],
3741 |     "instruction": "open google drive",
3742 |     "data_type": "text",
3743 |     "data_source": "shop"
3744 |   },
3745 |   {
3746 |     "img_filename": "pc_j6tmln.png",
3747 |     "bbox": [
3748 |       906,
3749 |       181,
3750 |       109,
3751 |       34
3752 |     ],
3753 |     "instruction": "install LXD app",
3754 |     "data_type": "text",
3755 |     "data_source": "shop"
3756 |   },
3757 |   {
3758 |     "img_filename": "pc_js1st2.png",
3759 |     "bbox":[2279, 397, 267, 31],
3760 |     "instruction": "click to restart the computer",
3761 |     "data_type": "text",
3762 |     "data_source": "shop"
3763 |   },
3764 |   {
3765 |     "img_filename": "pc_krzgko.png",
3766 |     "bbox": [
3767 |       2262,
3768 |       78,
3769 |       30,
3770 |       28
3771 |     ],
3772 |     "instruction": "add current webpage to favourites",
3773 |     "data_type": "icon",
3774 |     "data_source": "shop"
3775 |   },
3776 |   {
3777 |     "img_filename": "pc_nuyjna.png",
3778 |     "bbox": [
3779 |       1554,
3780 |       391,
3781 |       48,
3782 |       30
3783 |     ],
3784 |     "instruction": "Add a wired connection device",
3785 |     "data_type": "icon",
3786 |     "data_source": "shop"
3787 |   },
3788 |   {
3789 |     "img_filename": "pc_o4nd16.png",
3790 |     "bbox": [707, 644, 189, 30],
3791 |     "instruction": "select all folders",
3792 |     "data_type": "text",
3793 |     "data_source": "shop"
3794 |   },
3795 |   {
3796 |     "img_filename": "pc_odcu20.png",
3797 |     "bbox": [2207, 76, 647, 46],
3798 |     "instruction": "search in this folder",
3799 |     "data_type": "text",
3800 |     "data_source": "shop"
3801 |   },
3802 |   {
3803 |     "img_filename": "pc_oyo43s.png",
3804 |     "bbox": [
3805 |       927,
3806 |       130,
3807 |       40,
3808 |       32
3809 |     ],
3810 |     "instruction": "center the selected text",
3811 |     "data_type": "icon",
3812 |     "data_source": "shop"
3813 |   },
3814 |   {
3815 |     "img_filename": "pc_p4xo8z.png",
3816 |     "bbox": [
3817 |       1132,
3818 |       403,
3819 |       592,
3820 |       54
3821 |     ],
3822 |     "instruction": "add a new input method",
3823 |     "data_type": "icon",
3824 |     "data_source": "shop"
3825 |   },
3826 |   {
3827 |     "img_filename": "pc_pl3ry1.png",
3828 |     "bbox": [2632, 143, 170, 59],
3829 |     "instruction": "update all available apps",
3830 |     "data_type": "text",
3831 |     "data_source": "shop"
3832 |   },
3833 |   {
3834 |     "img_filename": "pc_qao1zc.png",
3835 |     "bbox": [
3836 |       1610,
3837 |       800,
3838 |       163,
3839 |       163
3840 |     ],
3841 |     "instruction": "open nac client ",
3842 |     "data_type": "text",
3843 |     "data_source": "shop"
3844 |   },
3845 |   {
3846 |     "img_filename": "pc_rvnx5q.png",
3847 |     "bbox": [1137, 355, 598, 58],
3848 |     "instruction": "select a new time format",
3849 |     "data_type": "icon",
3850 |     "data_source": "shop"
3851 |   },
3852 |   {
3853 |     "img_filename": "pc_st4s1n.png",
3854 |     "bbox": [
3855 |       772,
3856 |       755,
3857 |       305,
3858 |       75
3859 |     ],
3860 |     "instruction": "view get more help",
3861 |     "data_type": "text",
3862 |     "data_source": "shop"
3863 |   },
3864 |   {
3865 |     "img_filename": "pc_suf989.png",
3866 |     "bbox": [
3867 |       996,
3868 |       755,
3869 |       211,
3870 |       50
3871 |     ],
3872 |     "instruction": "do not save this file",
3873 |     "data_type": "text",
3874 |     "data_source": "shop"
3875 |   },
3876 |   {
3877 |     "img_filename": "pc_u77zz1.png",
3878 |     "bbox": [553, 1688, 88, 81],
3879 |     "instruction": "open the map app",
3880 |     "data_type": "icon",
3881 |     "data_source": "shop"
3882 |   },
3883 |   {
3884 |     "img_filename": "pc_umvv32.png",
3885 |     "bbox": [962, 740, 515, 33],
3886 |     "instruction": "switch to NAT network connections",
3887 |     "data_type": "icon",
3888 |     "data_source": "shop"
3889 |   },
3890 |   {
3891 |     "img_filename": "pc_uuhwlx.png",
3892 |     "bbox": [
3893 |       1309,
3894 |       526,
3895 |       114,
3896 |       132
3897 |     ],
3898 |     "instruction": "open the calculator",
3899 |     "data_type": "text",
3900 |     "data_source": "shop"
3901 |   },
3902 |   {
3903 |     "img_filename": "pc_vcud9k.png",
3904 |     "bbox": [269, 127, 113, 132],
3905 |     "instruction": "insert a new slide",
3906 |     "data_type": "text",
3907 |     "data_source": "shop"
3908 |   },
3909 |   {
3910 |     "img_filename": "pc_z7ydq8.png",
3911 |     "bbox": [
3912 |       1466,
3913 |       644,
3914 |       147,
3915 |       136
3916 |     ],
3917 |     "instruction": "view more information about bomber",
3918 |     "data_type": "text",
3919 |     "data_source": "shop"
3920 |   },
3921 |   {
3922 |     "img_filename": "pc_zuyj00.png",
3923 |     "bbox": [1744, 281, 258, 53],
3924 |     "instruction": "adjust the options of  \"show previous\"",
3925 |     "data_type": "icon",
3926 |     "data_source": "shop"
3927 |   },
3928 |   {
3929 |     "img_filename": "pc_aaaaaa.png",
3930 |     "bbox": [
3931 |       701,
3932 |       321,
3933 |       417,
3934 |       34
3935 |     ],
3936 |     "instruction": "enter my full name",
3937 |     "data_type": "text",
3938 |     "data_source": "shop"
3939 |   }
3940 | ]


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