├── .gitignore ├── LICENSE ├── MANIFEST.in ├── README.md ├── example ├── README.md ├── classification.py ├── demo.py ├── detection.py ├── pointdet.py └── sam_segmentation.py ├── image ├── classification.gif ├── deer.jpg ├── demo.gif ├── detection.gif ├── dog.jpg ├── framingo.jpg ├── penguin.jpg ├── pointdet.gif └── sam_segmentation.gif ├── requirements.txt ├── setup.py └── streamlit_image_annotation ├── Classification ├── __init__.py └── frontend │ ├── .env │ ├── .prettierrc │ ├── package.json │ ├── public │ ├── bootstrap.min.css │ └── index.html │ ├── src │ ├── Classification.tsx │ ├── ThemeSwitcher.tsx │ ├── index.tsx │ └── react-app-env.d.ts │ ├── tsconfig.json │ └── yarn.lock ├── Detection ├── __init__.py └── frontend │ ├── .env │ ├── .prettierrc │ ├── package.json │ ├── public │ ├── bootstrap.min.css │ └── index.html │ ├── src │ ├── BBox.tsx │ ├── BBoxCanvas.tsx │ ├── Detection.tsx │ ├── ThemeSwitcher.tsx │ ├── index.tsx │ └── react-app-env.d.ts │ ├── tsconfig.json │ └── yarn.lock ├── Point ├── __init__.py └── frontend │ ├── .env │ ├── .prettierrc │ ├── package.json │ ├── public │ ├── bootstrap.min.css │ └── index.html │ ├── src │ ├── Point.tsx │ ├── PointCanvas.tsx │ ├── PointDet.tsx │ ├── ThemeSwitcher.tsx │ ├── index.tsx │ └── react-app-env.d.ts │ ├── tsconfig.json │ └── yarn.lock └── __init__.py /.gitignore: -------------------------------------------------------------------------------- 1 | ######################################################################## 2 | # Python - https://github.com/github/gitignore/blob/master/Python.gitignore 3 | ######################################################################## 4 | # Byte-compiled / optimized / DLL files 5 | __pycache__/ 6 | *.py[cod] 7 | *$py.class 8 | 9 | # Distribution / packaging 10 | build/ 11 | dist/ 12 | eggs/ 13 | .eggs/ 14 | *.egg-info/ 15 | *.egg 16 | 17 | # Unit test / coverage reports 18 | .coverage 19 | .coverage\.* 20 | .pytest_cache/ 21 | .mypy_cache/ 22 | test-reports 23 | 24 | # Test fixtures 25 | cffi_bin 26 | 27 | # Pyenv Stuff 28 | .python-version 29 | 30 | ######################################################################## 31 | # OSX - https://github.com/github/gitignore/blob/master/Global/macOS.gitignore 32 | ######################################################################## 33 | .DS_Store 34 | .DocumentRevisions-V100 35 | .fseventsd 36 | .Spotlight-V100 37 | .TemporaryItems 38 | .Trashes 39 | .VolumeIcon.icns 40 | .com.apple.timemachine.donotpresent 41 | 42 | ######################################################################## 43 | # node - https://github.com/github/gitignore/blob/master/Node.gitignore 44 | ######################################################################## 45 | # Logs 46 | npm-debug.log* 47 | yarn-debug.log* 48 | yarn-error.log* 49 | 50 | # Dependency directories 51 | node_modules/ 52 | 53 | # Coverage directory used by tools like istanbul 54 | coverage/ 55 | 56 | # Lockfiles 57 | #yarn.lock 58 | package-lock.json 59 | 60 | ######################################################################## 61 | # JetBrains 62 | ######################################################################## 63 | .idea 64 | 65 | ######################################################################## 66 | # VSCode 67 | ######################################################################## 68 | .vscode/ 69 | 70 | ######################################################################## 71 | # Other 72 | ######################################################################## 73 | .venv/ 74 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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Assign a key so the component is not remount every time the script is rerun. 63 | 64 | - **Component Value**: {'label': label_name} 65 | 66 | Example: [example code](example/classification.py) 67 | 68 | ```python 69 | detection( 70 | image_path: str, 71 | label_list: List[str], 72 | bboxes: Optional[List[List[int, int, int, int]]] = None, 73 | labels: Optional[List[int]] = None, 74 | height: int = 512, 75 | width: int = 512, 76 | line_width: int = 5, 77 | use_space: bool = False, 78 | key: Optional[str] = None 79 | ) 80 | ``` 81 | 82 | - **image_path**: Image path. 83 | - **label_list**: List of label candidates. 84 | - **bboxes**: Initial list of bounding boxes, where each bbox is in the format [x, y, w, h]. 85 | - **labels**: List of label for each initial bbox. 86 | - **height**: The maximum height of the displayed image. 87 | - **width**: The maximum width of the displayed image. 88 | - **line_width**: The stroke width of the bbox. 89 | - **use_space**: Enable Space key for complete. 90 | - **key**: An optional string to use as the unique key for the widget. Assign a key so the component is not remount every time the script is rerun. 91 | 92 | - **Component Value**: \[{'bbox':[x,y,width, height], 'label_id': label_id, 'label': label_name},...\] 93 | 94 | Example: [example code](example/detection.py) 95 | 96 | ```python 97 | pointdet( 98 | image_path: str, 99 | label_list: List[str], 100 | points: Optional[List[List[int, int]]] = None, 101 | labels: Optional[List[int]] = None, 102 | height: int = 512, 103 | width: int = 512, 104 | point_width: int =3, 105 | use_space: bool = False, 106 | key: Optional[str] = None 107 | ) 108 | ``` 109 | 110 | - **image_path**: Image path. 111 | - **label_list**: List of label candidates. 112 | - **points**: Initial list of points, where each point is in the format [x, y]. 113 | - **labels**: List of label for each initial bbox. 114 | - **height**: The maximum height of the displayed image. 115 | - **width**: The maximum width of the displayed image. 116 | - **point_width**: The stroke width of the bbox. 117 | - **use_space**: Enable Space key for complete. 118 | - **key**: An optional string to use as the unique key for the widget. Assign a key so the component is not remount every time the script is rerun. 119 | 120 | - **Component Value**: \[{'bbox':[x,y], 'label_id': label_id, 'label': label_name},...\] 121 | 122 | Example: [example code](example/pointdet.py) 123 | 124 | # Future Work 125 | * Addition of component for segmentation task. 126 | 127 | # Development 128 | ## setup 129 | ```bash 130 | cd Streamlit-Image-Annotation/ 131 | export PYTHONPATH=$PWD 132 | ``` 133 | and set `IS_RELEASE = False` in `Streamlit-Image-Annotation/__init__.py`. 134 | 135 | 136 | ## start frontend 137 | ```bash 138 | git clone https://github.com/hirune924/Streamlit-Image-Annotation.git 139 | cd Streamlit-Image-Annotation/streamlit_image_annotation/Detection/frontend 140 | yarn 141 | yarn start 142 | ``` 143 | 144 | ## start streamlit 145 | ```bash 146 | cd Streamlit-Image-Annotation/ 147 | streamlit run streamlit_image_annotation/Detection/__init__.py 148 | ``` 149 | 150 | ## build 151 | ```bash 152 | cd Streamlit-Image-Annotation/Classification/frontend 153 | yarn build 154 | cd Streamlit-Image-Annotation/Detection/frontend 155 | yarn build 156 | cd Streamlit-Image-Annotation/Point/frontend 157 | yarn build 158 | ``` 159 | and set `IS_RELEASE = True` in `Streamlit-Image-Annotation/__init__.py`. 160 | 161 | ## make wheel 162 | ```bash 163 | python setup.py sdist bdist_wheel 164 | ``` 165 | ## upload 166 | ```bash 167 | python3 -m twine upload --repository testpypi dist/* 168 | python -m pip install --index-url https://test.pypi.org/simple/ --no-deps streamlit-image-annotation 169 | ``` 170 | ```bash 171 | twine upload dist/* 172 | ``` 173 | -------------------------------------------------------------------------------- /example/README.md: -------------------------------------------------------------------------------- 1 | # Examples 2 | ## Classification 3 | ``` 4 | streamlit run classification.py 5 | ``` 6 | ![](../image/classification.gif) 7 | ## Detection 8 | ``` 9 | streamlit run detection.py 10 | ``` 11 | ![](../image/detection.gif) 12 | 13 | ## Point Detection 14 | ``` 15 | streamlit run pointdet.py 16 | ``` 17 | ![](../image/pointdet.gif) 18 | 19 | ## Segmentation via SAM 20 | ``` 21 | streamlit run sam_segmentation.py 22 | ``` 23 | ![](../image/sam_segmentation.gif) 24 | -------------------------------------------------------------------------------- /example/classification.py: -------------------------------------------------------------------------------- 1 | from glob import glob 2 | import pandas as pd 3 | import streamlit as st 4 | from streamlit_image_annotation import classification 5 | 6 | label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear'] 7 | image_path_list = glob('image/*.jpg') 8 | if 'result_df' not in st.session_state: 9 | st.session_state['result_df'] = pd.DataFrame.from_dict({'image': image_path_list, 'label': [0]*len(image_path_list)}).copy() 10 | 11 | num_page = st.slider('page', 0, len(image_path_list)-1, 0) 12 | 13 | label = classification(image_path_list[num_page], 14 | label_list=label_list, 15 | default_label_index=int(st.session_state['result_df'].loc[num_page, 'label'])) 16 | 17 | if label is not None and label['label'] != st.session_state['result_df'].loc[num_page, 'label']: 18 | st.session_state['result_df'].loc[num_page, 'label'] = label_list.index(label['label']) 19 | st.table(st.session_state['result_df']) -------------------------------------------------------------------------------- /example/demo.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from glob import glob 3 | import pandas as pd 4 | from streamlit_image_annotation import detection, classification, pointdet 5 | 6 | mode = st.tabs(["Detection", "Classification", "Point"]) 7 | label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear'] 8 | image_path_list = glob('image/*.jpg') 9 | 10 | with mode[0]: 11 | if 'result_dict_det' not in st.session_state: 12 | result_dict = {} 13 | for img in image_path_list: 14 | result_dict[img] = {'bboxes': [[0,0,100,100],[10,20,50,150]],'labels':[0,3]} 15 | st.session_state['result_dict_det'] = result_dict.copy() 16 | 17 | num_page = st.slider('page', 0, len(image_path_list)-1, 0, key='slider_det') 18 | target_image_path = image_path_list[num_page] 19 | 20 | new_labels = detection(image_path=target_image_path, 21 | bboxes=st.session_state['result_dict_det'][target_image_path]['bboxes'], 22 | labels=st.session_state['result_dict_det'][target_image_path]['labels'], 23 | label_list=label_list, use_space=True, key=target_image_path+'_det') 24 | if new_labels is not None: 25 | st.session_state['result_dict_det'][target_image_path]['bboxes'] = [v['bbox'] for v in new_labels] 26 | st.session_state['result_dict_det'][target_image_path]['labels'] = [v['label_id'] for v in new_labels] 27 | st.json(st.session_state['result_dict_det']) 28 | 29 | with mode[1]: 30 | 31 | if 'result_df_cls' not in st.session_state: 32 | st.session_state['result_df_cls'] = pd.DataFrame.from_dict({'image': image_path_list, 'label': [0]*len(image_path_list)}).copy() 33 | 34 | num_page = st.slider('page', 0, len(image_path_list)-1, 0, key='slider_cls') 35 | 36 | label = classification(image_path_list[num_page], 37 | label_list=label_list, 38 | default_label_index=int(st.session_state['result_df_cls'].loc[num_page, 'label'])) 39 | 40 | if label is not None and label['label'] != st.session_state['result_df_cls'].loc[num_page, 'label']: 41 | st.session_state['result_df_cls'].loc[num_page, 'label'] = label_list.index(label['label']) 42 | st.table(st.session_state['result_df_cls']) 43 | 44 | with mode[2]: 45 | 46 | if 'result_dict_point' not in st.session_state: 47 | result_dict = {} 48 | for img in image_path_list: 49 | result_dict[img] = {'points': [[0,0],[50,150], [200,200]],'labels':[0,3,4]} 50 | st.session_state['result_dict_point'] = result_dict.copy() 51 | 52 | num_page = st.slider('page', 0, len(image_path_list)-1, 0, key='slider_point') 53 | target_image_path = image_path_list[num_page] 54 | 55 | new_labels = pointdet(image_path=target_image_path, 56 | label_list=label_list, 57 | points=st.session_state['result_dict_point'][target_image_path]['points'], 58 | labels=st.session_state['result_dict_point'][target_image_path]['labels'], 59 | use_space=True, key=target_image_path+'_point') 60 | if new_labels is not None: 61 | st.session_state['result_dict_point'][target_image_path]['points'] = [v['point'] for v in new_labels] 62 | st.session_state['result_dict_point'][target_image_path]['labels'] = [v['label_id'] for v in new_labels] 63 | st.json(st.session_state['result_dict_point']) -------------------------------------------------------------------------------- /example/detection.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from glob import glob 3 | from streamlit_image_annotation import detection 4 | 5 | label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear'] 6 | image_path_list = glob('image/*.jpg') 7 | if 'result_dict' not in st.session_state: 8 | result_dict = {} 9 | for img in image_path_list: 10 | result_dict[img] = {'bboxes': [[0,0,100,100],[10,20,50,150]],'labels':[0,3]} 11 | st.session_state['result_dict'] = result_dict.copy() 12 | 13 | num_page = st.slider('page', 0, len(image_path_list)-1, 0, key='slider') 14 | target_image_path = image_path_list[num_page] 15 | 16 | new_labels = detection(image_path=target_image_path, 17 | bboxes=st.session_state['result_dict'][target_image_path]['bboxes'], 18 | labels=st.session_state['result_dict'][target_image_path]['labels'], 19 | label_list=label_list, use_space=True, key=target_image_path) 20 | if new_labels is not None: 21 | st.session_state['result_dict'][target_image_path]['bboxes'] = [v['bbox'] for v in new_labels] 22 | st.session_state['result_dict'][target_image_path]['labels'] = [v['label_id'] for v in new_labels] 23 | st.json(st.session_state['result_dict']) -------------------------------------------------------------------------------- /example/pointdet.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from glob import glob 3 | from streamlit_image_annotation import pointdet 4 | 5 | label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear'] 6 | image_path_list = glob('image/*.jpg') 7 | if 'result_dict' not in st.session_state: 8 | result_dict = {} 9 | for img in image_path_list: 10 | result_dict[img] = {'points': [[0,0],[50,150], [200,200]],'labels':[0,3,4]} 11 | st.session_state['result_dict'] = result_dict.copy() 12 | 13 | num_page = st.slider('page', 0, len(image_path_list)-1, 0, key='slider') 14 | target_image_path = image_path_list[num_page] 15 | 16 | new_labels = pointdet(image_path=target_image_path, 17 | label_list=label_list, 18 | points=st.session_state['result_dict'][target_image_path]['points'], 19 | labels=st.session_state['result_dict'][target_image_path]['labels'], 20 | use_space=True, key=target_image_path) 21 | if new_labels is not None: 22 | st.session_state['result_dict'][target_image_path]['points'] = [v['point'] for v in new_labels] 23 | st.session_state['result_dict'][target_image_path]['labels'] = [v['label_id'] for v in new_labels] 24 | st.json(st.session_state['result_dict']) -------------------------------------------------------------------------------- /example/sam_segmentation.py: -------------------------------------------------------------------------------- 1 | import streamlit as st 2 | from glob import glob 3 | from streamlit_image_annotation import detection 4 | import cv2 5 | import numpy as np 6 | import urllib.request 7 | import torch 8 | import os 9 | import matplotlib.pyplot as plt 10 | 11 | try: 12 | from segment_anything import SamPredictor, sam_model_registry 13 | except: 14 | import subprocess 15 | subprocess.call(["pip","install","git+https://github.com/facebookresearch/segment-anything.git"]) 16 | subprocess.call(["mkdir",".sam"]) 17 | from segment_anything import SamPredictor, sam_model_registry 18 | 19 | def get_colormap(label_names, colormap_name='gist_rainbow'): 20 | colormap = [] 21 | cmap = plt.get_cmap(colormap_name) 22 | for idx, l in enumerate(label_names): 23 | rgb = [int(d) for d in np.array(cmap(float(idx)/len(label_names)))*255][:3] 24 | #colormap[l] = ('#%02x%02x%02x' % tuple(rgb)) 25 | colormap.append(rgb) 26 | return colormap 27 | 28 | class SAMPredictor: 29 | def __init__(self, model_type, device, ckpt_dir='.sam'): 30 | os.makedirs(ckpt_dir, exist_ok=True) 31 | url = 'https://dl.fbaipublicfiles.com/segment_anything/' 32 | ckpt_dict = {'vit_b': 'sam_vit_b_01ec64.pth', 33 | 'vit_l': 'sam_vit_l_0b3195.pth', 34 | 'vit_h': 'sam_vit_h_4b8939.pth'} 35 | @st.cache_resource() 36 | def get_sam_predictor(ckpt, model_type, device): 37 | sam = sam_model_registry[model_type](checkpoint=ckpt) 38 | sam.to(device=device) 39 | predictor = SamPredictor(sam) 40 | return predictor 41 | with st.spinner('Downloading SAM model...'): 42 | if not os.path.exists(os.path.join(ckpt_dir, ckpt_dict[model_type])): 43 | urllib.request.urlretrieve(url+ckpt_dict[model_type], os.path.join(ckpt_dir, ckpt_dict[model_type])) 44 | 45 | self.model_type = model_type 46 | self.device = device 47 | self.predictor = get_sam_predictor(os.path.join(ckpt_dir, ckpt_dict[model_type]), model_type, device) 48 | 49 | def set_image(self, image): 50 | self.image = image 51 | self.predictor.set_image(image) 52 | 53 | def predict(self, bboxes): 54 | ''' 55 | bboxes: [[x,y,x,y], [x,y,x,y]] 56 | ''' 57 | input_boxes = torch.tensor(bboxes).to(self.device) 58 | transformed_boxes = self.predictor.transform.apply_boxes_torch(input_boxes, self.image.shape[:2]) 59 | masks, _, _ = self.predictor.predict_torch( 60 | point_coords=None, 61 | point_labels=None, 62 | boxes=transformed_boxes, 63 | multimask_output=False, 64 | ) 65 | masks = masks.cpu().numpy() 66 | return masks 67 | 68 | def draw_masks(self, masks, colormap, labels): 69 | result = self.image.copy() 70 | for idx, m in enumerate(masks): 71 | color = np.array(colormap[labels[idx]]) 72 | h, w = m.shape[-2:] 73 | mask_image = m.reshape(h, w, 1) * color.reshape(1, 1, -1) 74 | result = np.where(np.tile(m.reshape(h, w, 1),(1,1,3)),(result * 0.5 + mask_image * 0.5).astype(np.uint8), result) 75 | return result 76 | 77 | sam_predictor = SAMPredictor(model_type='vit_b', device='cpu') 78 | label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear'] 79 | colormap = get_colormap(label_list, colormap_name='gist_rainbow') 80 | image_path_list = glob('image/*.jpg') 81 | if 'result_dict' not in st.session_state: 82 | result_dict = {} 83 | for img in image_path_list: 84 | result_dict[img] = {'bboxes': [],'labels':[]} 85 | st.session_state['result_dict'] = result_dict.copy() 86 | 87 | num_page = st.slider('page', 0, len(image_path_list)-1, 0, key='slider') 88 | target_image_path = image_path_list[num_page] 89 | 90 | new_labels = detection(image_path=target_image_path, 91 | bboxes=st.session_state['result_dict'][target_image_path]['bboxes'], 92 | labels=st.session_state['result_dict'][target_image_path]['labels'], 93 | label_list=label_list, key=target_image_path) 94 | if new_labels is not None: 95 | st.session_state['result_dict'][target_image_path]['bboxes'] = [v['bbox'] for v in new_labels] 96 | st.session_state['result_dict'][target_image_path]['labels'] = [v['label_id'] for v in new_labels] 97 | 98 | image = cv2.imread(target_image_path) 99 | image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) 100 | sam_predictor.set_image(image) 101 | if len(st.session_state['result_dict'][target_image_path]['bboxes'])!=0: 102 | input_boxes = [[b[0], b[1], b[2]+b[0], b[3]+b[1]] for b in st.session_state['result_dict'][target_image_path]['bboxes']] 103 | masks = sam_predictor.predict(input_boxes) 104 | draw_image = sam_predictor.draw_masks(masks, colormap, [l for l in st.session_state['result_dict'][target_image_path]['labels']]) 105 | st.image(draw_image) 106 | else: 107 | st.image(image) 108 | -------------------------------------------------------------------------------- /image/classification.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hirune924/Streamlit-Image-Annotation/abd8f7e7c060fa04e1b47bd138d5306328d4cbf1/image/classification.gif -------------------------------------------------------------------------------- /image/deer.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hirune924/Streamlit-Image-Annotation/abd8f7e7c060fa04e1b47bd138d5306328d4cbf1/image/deer.jpg -------------------------------------------------------------------------------- /image/demo.gif: 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/hirune924/Streamlit-Image-Annotation/abd8f7e7c060fa04e1b47bd138d5306328d4cbf1/image/sam_segmentation.gif -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | streamlit 2 | Pillow 3 | streamlit-image-annotation 4 | matplotlib 5 | pandas 6 | numpy 7 | opencv-python-headless 8 | torch 9 | git+https://github.com/facebookresearch/segment-anything.git 10 | torchvision 11 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | with open("README.md", "r") as fh: 4 | long_description = fh.read() 5 | 6 | setuptools.setup( 7 | name="streamlit_image_annotation", 8 | version="0.4.0", 9 | author="hirune924", 10 | description="streamlit components for image annotation", 11 | long_description=long_description, 12 | long_description_content_type="text/markdown", 13 | url="https://github.com/hirune924/Streamlit-Image-Annotation", 14 | packages=setuptools.find_packages(), 15 | include_package_data=True, 16 | classifiers=[], 17 | keywords=['Python', 'Streamlit', 'React', 'JavaScript'], 18 | python_requires=">=3.6", 19 | install_requires=[ 20 | "streamlit >= 0.63", 21 | ], 22 | ) -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/__init__.py: -------------------------------------------------------------------------------- 1 | import os 2 | import streamlit.components.v1 as components 3 | from streamlit.components.v1.components import CustomComponent 4 | 5 | import streamlit as st 6 | try: 7 | from streamlit.elements.image import image_to_url 8 | except: 9 | from streamlit.elements.lib.image_utils import image_to_url 10 | from PIL import Image 11 | 12 | from hashlib import md5 13 | from streamlit_image_annotation import IS_RELEASE 14 | 15 | if IS_RELEASE: 16 | absolute_path = os.path.dirname(os.path.abspath(__file__)) 17 | build_path = os.path.join(absolute_path, "frontend/build") 18 | _component_func = components.declare_component("st_classification", path=build_path) 19 | else: 20 | _component_func = components.declare_component("st_classification", url="http://localhost:3000") 21 | 22 | def classification(image_path, label_list, default_label_index=None, height=512, width=512, key=None) -> CustomComponent: 23 | image = Image.open(image_path) 24 | image.thumbnail(size=(width, height)) 25 | image_url = image_to_url(image, image.size[0], True, "RGB", "PNG", f"classification-{md5(image.tobytes()).hexdigest()}-{key}") 26 | if image_url.startswith('/'): 27 | image_url = image_url[1:] 28 | component_value = _component_func(image_url=image_url, image_size=image.size, label_list=label_list, default_label_idx=default_label_index, key=key) 29 | return component_value 30 | 31 | if not IS_RELEASE: 32 | from glob import glob 33 | import pandas as pd 34 | label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear'] 35 | image_path_list = glob('image/*.jpg') 36 | if 'result_df' not in st.session_state: 37 | st.session_state['result_df'] = pd.DataFrame.from_dict({'image': image_path_list, 'label': [0]*len(image_path_list)}).copy() 38 | 39 | num_page = st.slider('page', 0, len(image_path_list)-1, 0, key="slider") 40 | label = classification(image_path_list[num_page], 41 | label_list=label_list, 42 | default_label_index=int(st.session_state['result_df'].loc[num_page, 'label']), 43 | key=image_path_list[num_page]) 44 | 45 | if label is not None and label['label'] != st.session_state['result_df'].loc[num_page, 'label']: 46 | st.session_state['result_df'].loc[num_page, 'label'] = label_list.index(label['label']) 47 | st.table(st.session_state['result_df']) -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/frontend/.env: -------------------------------------------------------------------------------- 1 | # Run the component's dev server on :3001 2 | # (The Streamlit dev server already runs on :3000) 3 | PORT=3000 4 | 5 | # Don't automatically open the web browser on `npm run start`. 6 | BROWSER=none 7 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/frontend/.prettierrc: -------------------------------------------------------------------------------- 1 | { 2 | "endOfLine": "lf", 3 | "semi": false, 4 | "trailingComma": "es5" 5 | } 6 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/frontend/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "st-classification", 3 | "version": "0.1.0", 4 | "private": true, 5 | "homepage": ".", 6 | "dependencies": { 7 | "@chakra-ui/react": "^2.5.5", 8 | "@emotion/react": "^11", 9 | "@emotion/styled": "^11", 10 | "@testing-library/jest-dom": "^5.14.1", 11 | "@testing-library/react": "^13.0.0", 12 | "@testing-library/user-event": "^13.2.1", 13 | "@types/jest": "^27.0.1", 14 | "@types/node": "^16.7.13", 15 | "@types/react": "^18.0.0", 16 | "@types/react-dom": "^18.0.0", 17 | "framer-motion": "^6", 18 | "konva": "^8.4.3", 19 | "react": "^18.2.0", 20 | "react-dom": "^18.2.0", 21 | "react-konva": "^18.2.5", 22 | "react-scripts": "5.0.1", 23 | "streamlit-component-lib": "^1.4.0", 24 | "typescript": "^4.4.2", 25 | "use-image": "^1.1.0", 26 | "web-vitals": "^2.1.0" 27 | }, 28 | "resolutions": { 29 | "streamlit-component-lib/apache-arrow": "11.0.0" 30 | }, 31 | "scripts": { 32 | "start": "react-scripts start", 33 | "build": "react-scripts build", 34 | "test": "react-scripts test", 35 | "eject": "react-scripts eject" 36 | }, 37 | "eslintConfig": { 38 | "extends": [ 39 | "react-app", 40 | "react-app/jest" 41 | ] 42 | }, 43 | "browserslist": { 44 | "production": [ 45 | ">0.2%", 46 | "not dead", 47 | "not op_mini all" 48 | ], 49 | "development": [ 50 | "last 1 chrome version", 51 | "last 1 firefox version", 52 | "last 1 safari version" 53 | ] 54 | } 55 | } 56 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/frontend/public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Streamlit Component 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
14 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/frontend/src/Classification.tsx: -------------------------------------------------------------------------------- 1 | import { 2 | Streamlit, 3 | withStreamlitConnection, 4 | ComponentProps 5 | } from "streamlit-component-lib" 6 | import React, { useEffect, useState } from "react" 7 | import { ChakraProvider, Select, Box, Spacer, HStack, Center, Text } from '@chakra-ui/react' 8 | 9 | import { Stage, Image, Layer } from 'react-konva'; 10 | import useImage from 'use-image'; 11 | import ThemeSwitcher from './ThemeSwitcher' 12 | 13 | export interface PythonArgs { 14 | image_url: string, 15 | image_size: number[], 16 | label_list: string[], 17 | default_label_idx: number 18 | } 19 | /** 20 | * This is a React-based component template. The `render()` function is called 21 | * automatically when your component should be re-rendered. 22 | */ 23 | const Classification = ({ args, theme }: ComponentProps) => { 24 | const { 25 | image_url, 26 | image_size, 27 | label_list, 28 | default_label_idx 29 | }: PythonArgs = args 30 | 31 | const params = new URLSearchParams(window.location.search); 32 | const baseUrl = params.get('streamlitUrl') 33 | const [image] = useImage(baseUrl + image_url) 34 | const [label, setLabel] = useState(label_list[default_label_idx]) 35 | const [scale, setScale] = useState(1.0) 36 | const handleChange = (event: React.ChangeEvent) => { 37 | setLabel(event.target.value) 38 | Streamlit.setComponentValue({ 'label': event.target.value }) 39 | } 40 | 41 | useEffect(() => { 42 | const resizeCanvas = () => { 43 | const scale_ratio = window.innerWidth * 0.8 / image_size[0] 44 | setScale(Math.min(scale_ratio, 1.0)) 45 | Streamlit.setFrameHeight(image_size[1] * Math.min(scale_ratio, 1.0)) 46 | } 47 | window.addEventListener('resize', resizeCanvas); 48 | resizeCanvas() 49 | }, [image_size]) 50 | 51 | return ( 52 | 53 | 54 |
55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | Class 66 | 73 | 74 | 75 |
76 | 77 | 78 | ) 79 | 80 | } 81 | 82 | 83 | export default withStreamlitConnection(Classification) 84 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/frontend/src/ThemeSwitcher.tsx: -------------------------------------------------------------------------------- 1 | import React, { ReactNode } from "react" 2 | import { DarkMode, LightMode, Box } from '@chakra-ui/react' 3 | import { Theme } from 'streamlit-component-lib' 4 | 5 | 6 | type ContainerProps = { 7 | children: ReactNode; 8 | theme: Theme | undefined; 9 | } 10 | 11 | const ThemeSwitcher = ({ children, theme }: ContainerProps) => { 12 | console.log(theme) 13 | if (theme?.base !== 'light') { 14 | return ( 15 | 16 | 17 | {children} 18 | 19 | 20 | ); 21 | } else { 22 | return ( 23 | 24 | 25 | {children} 26 | 27 | 28 | ); 29 | } 30 | } 31 | 32 | export default ThemeSwitcher; -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/frontend/src/index.tsx: -------------------------------------------------------------------------------- 1 | import React from "react" 2 | import ReactDOM from "react-dom" 3 | import Classification from "./Classification" 4 | 5 | ReactDOM.render( 6 | 7 | 8 | , 9 | document.getElementById("root") 10 | ) 11 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/frontend/src/react-app-env.d.ts: -------------------------------------------------------------------------------- 1 | /// 2 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Classification/frontend/tsconfig.json: -------------------------------------------------------------------------------- 1 | { 2 | "compilerOptions": { 3 | "target": "es2020", 4 | "allowJs": true, 5 | "skipLibCheck": true, 6 | "esModuleInterop": true, 7 | "allowSyntheticDefaultImports": true, 8 | "strict": true, 9 | "forceConsistentCasingInFileNames": true, 10 | "moduleResolution": "node", 11 | "resolveJsonModule": true, 12 | "isolatedModules": true, 13 | "jsx": "react", 14 | "lib": [ 15 | "dom", 16 | "dom.iterable", 17 | "esnext" 18 | ], 19 | "module": "esnext", 20 | "noEmit": true 21 | }, 22 | "include": [ 23 | "src" 24 | ] 25 | } 26 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/__init__.py: -------------------------------------------------------------------------------- 1 | import os 2 | import streamlit.components.v1 as components 3 | from streamlit.components.v1.components import CustomComponent 4 | from typing import List 5 | 6 | import streamlit as st 7 | try: 8 | from streamlit.elements.image import image_to_url 9 | except: 10 | from streamlit.elements.lib.image_utils import image_to_url 11 | from PIL import Image 12 | import numpy as np 13 | import matplotlib.pyplot as plt 14 | from hashlib import md5 15 | from streamlit_image_annotation import IS_RELEASE 16 | 17 | if IS_RELEASE: 18 | absolute_path = os.path.dirname(os.path.abspath(__file__)) 19 | build_path = os.path.join(absolute_path, "frontend/build") 20 | _component_func = components.declare_component("st-detection", path=build_path) 21 | else: 22 | _component_func = components.declare_component("st-detection", url="http://localhost:3000") 23 | 24 | def get_colormap(label_names, colormap_name='gist_rainbow'): 25 | colormap = {} 26 | cmap = plt.get_cmap(colormap_name) 27 | for idx, l in enumerate(label_names): 28 | rgb = [int(d) for d in np.array(cmap(float(idx)/len(label_names)))*255][:3] 29 | colormap[l] = ('#%02x%02x%02x' % tuple(rgb)) 30 | return colormap 31 | 32 | #''' 33 | #bboxes: 34 | #[[x,y,w,h],[x,y,w,h]] 35 | #labels: 36 | #[0,3] 37 | #''' 38 | def detection(image_path, label_list, bboxes=None, labels=None, height=512, width=512, line_width=5.0, use_space=False, key=None) -> CustomComponent: 39 | image = Image.open(image_path) 40 | original_image_size = image.size 41 | image.thumbnail(size=(width, height)) 42 | resized_image_size = image.size 43 | scale = original_image_size[0]/resized_image_size[0] 44 | 45 | image_url = image_to_url(image, image.size[0], True, "RGB", "PNG", f"detection-{md5(image.tobytes()).hexdigest()}-{key}") 46 | if image_url.startswith('/'): 47 | image_url = image_url[1:] 48 | 49 | color_map = get_colormap(label_list, colormap_name='gist_rainbow') 50 | bbox_info = [{'bbox':[b/scale for b in item[0]], 'label_id': item[1], 'label': label_list[item[1]]} for item in zip(bboxes, labels)] 51 | component_value = _component_func(image_url=image_url, image_size=image.size, label_list=label_list, bbox_info=bbox_info, color_map=color_map, line_width=line_width, use_space=use_space, key=key) 52 | if component_value is not None: 53 | component_value = [{'bbox':[b*scale for b in item['bbox']], 'label_id': item['label_id'], 'label': item['label']}for item in component_value] 54 | return component_value 55 | 56 | if not IS_RELEASE: 57 | from glob import glob 58 | import pandas as pd 59 | label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear'] 60 | image_path_list = glob('image/*.jpg') 61 | if 'result_dict' not in st.session_state: 62 | result_dict = {} 63 | for img in image_path_list: 64 | result_dict[img] = {'bboxes': [[0,0,100,100],[10,20,50,150]],'labels':[0,3]} 65 | st.session_state['result_dict'] = result_dict.copy() 66 | 67 | num_page = st.slider('page', 0, len(image_path_list)-1, 0, key='slider') 68 | target_image_path = image_path_list[num_page] 69 | 70 | new_labels = detection(image_path=target_image_path, 71 | bboxes=st.session_state['result_dict'][target_image_path]['bboxes'], 72 | labels=st.session_state['result_dict'][target_image_path]['labels'], 73 | label_list=label_list, line_width=5, use_space=True, key=target_image_path) 74 | if new_labels is not None: 75 | st.session_state['result_dict'][target_image_path]['bboxes'] = [v['bbox'] for v in new_labels] 76 | st.session_state['result_dict'][target_image_path]['labels'] = [v['label_id'] for v in new_labels] 77 | st.json(st.session_state['result_dict']) -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/.env: -------------------------------------------------------------------------------- 1 | # Run the component's dev server on :3001 2 | # (The Streamlit dev server already runs on :3000) 3 | PORT=3000 4 | 5 | # Don't automatically open the web browser on `npm run start`. 6 | BROWSER=none 7 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/.prettierrc: -------------------------------------------------------------------------------- 1 | { 2 | "endOfLine": "lf", 3 | "semi": false, 4 | "trailingComma": "es5" 5 | } 6 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "st-detection", 3 | "version": "0.1.0", 4 | "private": true, 5 | "homepage": ".", 6 | "dependencies": { 7 | "@chakra-ui/react": "^2.5.5", 8 | "@emotion/react": "^11", 9 | "@emotion/styled": "^11", 10 | "@testing-library/jest-dom": "^5.14.1", 11 | "@testing-library/react": "^13.0.0", 12 | "@testing-library/user-event": "^13.2.1", 13 | "@types/jest": "^27.0.1", 14 | "@types/node": "^16.7.13", 15 | "@types/react": "^18.0.0", 16 | "@types/react-dom": "^18.0.0", 17 | "framer-motion": "^6", 18 | "konva": "^8.4.3", 19 | "react": "^18.2.0", 20 | "react-dom": "^18.2.0", 21 | "react-konva": "^18.2.5", 22 | "react-scripts": "5.0.1", 23 | "streamlit-component-lib": "^1.4.0", 24 | "typescript": "^4.4.2", 25 | "use-image": "^1.1.0", 26 | "web-vitals": "^2.1.0" 27 | }, 28 | "resolutions": { 29 | "streamlit-component-lib/apache-arrow": "11.0.0" 30 | }, 31 | "scripts": { 32 | "start": "react-scripts start", 33 | "build": "react-scripts build", 34 | "test": "react-scripts test", 35 | "eject": "react-scripts eject" 36 | }, 37 | "eslintConfig": { 38 | "extends": [ 39 | "react-app", 40 | "react-app/jest" 41 | ] 42 | }, 43 | "browserslist": { 44 | "production": [ 45 | ">0.2%", 46 | "not dead", 47 | "not op_mini all" 48 | ], 49 | "development": [ 50 | "last 1 chrome version", 51 | "last 1 firefox version", 52 | "last 1 safari version" 53 | ] 54 | } 55 | } 56 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Streamlit Component 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
14 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/src/BBox.tsx: -------------------------------------------------------------------------------- 1 | import React, { useState } from "react" 2 | import { Rect, Text, Transformer } from 'react-konva'; 3 | 4 | export interface RectProps { 5 | x: number, 6 | y: number, 7 | width: number, 8 | height: number, 9 | id: string, 10 | stroke: string, 11 | label: string, 12 | } 13 | export interface BBoxProps { 14 | rectProps: RectProps, 15 | onChange: any, 16 | isSelected: boolean, 17 | onClick: any, 18 | scale: number, 19 | strokeWidth: number 20 | } 21 | const BBox = (props: BBoxProps) => { 22 | const shapeRef = React.useRef(); 23 | const trRef = React.useRef(); 24 | const { 25 | rectProps, onChange, isSelected, onClick, scale, strokeWidth 26 | }: BBoxProps = props 27 | const [moving, setMoving] = useState(false); 28 | 29 | React.useEffect(() => { 30 | //if(isSelected){ 31 | trRef.current?.nodes([shapeRef.current]); 32 | trRef.current?.getLayer().batchDraw(); 33 | //} 34 | }, [isSelected]); 35 | 36 | 37 | return ( 38 | 39 | {moving || } 40 | { 54 | setMoving(true) 55 | }} 56 | onDragEnd={(e) => { 57 | onChange({ 58 | ...rectProps, 59 | x: e.target.x() / scale, 60 | y: e.target.y() / scale, 61 | }); 62 | setMoving(false) 63 | }} 64 | onTransformStart={(e) => { 65 | setMoving(true) 66 | }} 67 | onTransformEnd={(e) => { 68 | // transformer is changing scale of the node 69 | // and NOT its width or height 70 | // but in the store we have only width and height 71 | // to match the data better we will reset scale on transform end 72 | const node = shapeRef.current; 73 | const scaleX = node.scaleX(); 74 | const scaleY = node.scaleY(); 75 | 76 | setMoving(false) 77 | 78 | // we will reset it back 79 | node.scaleX(1); 80 | node.scaleY(1); 81 | onChange({ 82 | ...rectProps, 83 | x: node.x() / scale, 84 | y: node.y() / scale, 85 | // set minimal value 86 | width: Math.max(5, node.width() * scaleX / scale), 87 | height: Math.max(5, node.height() * scaleY / scale), 88 | }); 89 | }} 90 | /> 91 | { 99 | // limit resize 100 | if (newBox.width < 5 || newBox.height < 5) { 101 | return oldBox; 102 | } 103 | return newBox; 104 | }} 105 | /> 106 | 107 | ); 108 | }; 109 | 110 | 111 | export default BBox; -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/src/BBoxCanvas.tsx: -------------------------------------------------------------------------------- 1 | import React, { useState, useEffect } from "react" 2 | import { Layer, Rect, Stage, Image } from 'react-konva'; 3 | import BBox from './BBox' 4 | import Konva from 'konva'; 5 | 6 | export interface BBoxCanvasLayerProps { 7 | rectangles: any[], 8 | mode: string, 9 | selectedId: string | null, 10 | setSelectedId: any, 11 | setRectangles: any, 12 | setLabel: any, 13 | color_map: any, 14 | scale: number, 15 | label: string, 16 | image_size: number[], 17 | image: any, 18 | strokeWidth: number 19 | } 20 | const BBoxCanvas = (props: BBoxCanvasLayerProps) => { 21 | const { 22 | rectangles, 23 | mode, 24 | selectedId, 25 | setSelectedId, 26 | setRectangles, 27 | setLabel, 28 | color_map, 29 | scale, 30 | label, 31 | image_size, 32 | image, 33 | strokeWidth 34 | }: BBoxCanvasLayerProps = props 35 | const [adding, setAdding] = useState(null) 36 | const checkDeselect = (e: any) => { 37 | console.log('DOWN') 38 | if (!(e.target instanceof Konva.Rect)) { 39 | if (selectedId === null) { 40 | if (mode === 'Transform') { 41 | const pointer = e.target.getStage().getPointerPosition() 42 | setAdding([pointer.x, pointer.y, pointer.x, pointer.y]) 43 | } 44 | } else { 45 | setSelectedId(null); 46 | } 47 | } 48 | }; 49 | 50 | useEffect(() => { 51 | const rects = rectangles.slice(); 52 | for (let i = 0; i < rects.length; i++) { 53 | if (rects[i].width < 0) { 54 | rects[i].width = rects[i].width * -1 55 | rects[i].x = rects[i].x - rects[i].width 56 | setRectangles(rects) 57 | } 58 | if (rects[i].height < 0) { 59 | rects[i].height = rects[i].height * -1 60 | rects[i].y = rects[i].y - rects[i].height 61 | setRectangles(rects) 62 | } 63 | if (rects[i].x < 0 || rects[i].y < 0) { 64 | rects[i].width = rects[i].width + Math.min(0, rects[i].x) 65 | rects[i].x = Math.max(0, rects[i].x) 66 | rects[i].height = rects[i].height + Math.min(0, rects[i].y) 67 | rects[i].y = Math.max(0, rects[i].y) 68 | setRectangles(rects) 69 | } 70 | if (rects[i].x + rects[i].width > image_size[0] || rects[i].y + rects[i].height > image_size[1]) { 71 | rects[i].width = Math.min(rects[i].width, image_size[0] - rects[i].x) 72 | rects[i].height = Math.min(rects[i].height, image_size[1] - rects[i].y) 73 | setRectangles(rects) 74 | } 75 | if (rects[i].width < 5 || rects[i].height < 5) { 76 | rects[i].width = 5 77 | rects[i].height = 5 78 | } 79 | } 80 | console.log(rects) 81 | }, [rectangles, image_size]) 82 | return ( 83 | { 86 | if (!(adding === null)) { 87 | const pointer = e.target.getStage().getPointerPosition() 88 | setAdding([adding[0], adding[1], pointer.x, pointer.y]) 89 | } 90 | }} 91 | onMouseLeave={(e: any) => { 92 | setAdding(null) 93 | }} 94 | onMouseUp={(e: any) => { 95 | if (!(adding === null)) { 96 | const rects = rectangles.slice(); 97 | const new_id = Date.now().toString() 98 | rects.push({ 99 | x: adding[0] / scale, 100 | y: adding[1] / scale, 101 | width: (adding[2] - adding[0]) / scale, 102 | height: (adding[3] - adding[1]) / scale, 103 | label: label, 104 | stroke: color_map[label], 105 | id: new_id 106 | }) 107 | setRectangles(rects); 108 | setSelectedId(new_id); 109 | setAdding(null) 110 | } 111 | }}> 112 | 113 | 114 | 115 | 116 | {rectangles.map((rect, i) => { 117 | return ( 118 | { 125 | if (mode === 'Transform') { 126 | setSelectedId(rect.id); 127 | const rects = rectangles.slice(); 128 | const lastIndex = rects.length - 1; 129 | const lastItem = rects[lastIndex]; 130 | rects[lastIndex] = rects[i]; 131 | rects[i] = lastItem; 132 | setRectangles(rects); 133 | setLabel(rect.label) 134 | } else if (mode === 'Del') { 135 | const rects = rectangles.slice(); 136 | setRectangles(rects.filter((element) => element.id !== rect.id)); 137 | } 138 | }} 139 | onChange={(newAttrs: any) => { 140 | const rects = rectangles.slice(); 141 | rects[i] = newAttrs; 142 | setRectangles(rects); 143 | }} 144 | /> 145 | ); 146 | })} 147 | {adding !== null && } 148 | 149 | ); 150 | }; 151 | 152 | 153 | export default BBoxCanvas; -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/src/Detection.tsx: -------------------------------------------------------------------------------- 1 | import { 2 | Streamlit, 3 | withStreamlitConnection, 4 | ComponentProps 5 | } from "streamlit-component-lib" 6 | import React, { useEffect, useState } from "react" 7 | import { ChakraProvider, Select, Box, Spacer, HStack, Center, Button, Text } from '@chakra-ui/react' 8 | 9 | import useImage from 'use-image'; 10 | 11 | import ThemeSwitcher from './ThemeSwitcher' 12 | 13 | import BBoxCanvas from "./BBoxCanvas"; 14 | 15 | export interface PythonArgs { 16 | image_url: string, 17 | image_size: number[], 18 | label_list: string[], 19 | bbox_info: any[], 20 | color_map: any, 21 | line_width: number, 22 | use_space: boolean 23 | } 24 | 25 | const Detection = ({ args, theme }: ComponentProps) => { 26 | const { 27 | image_url, 28 | image_size, 29 | label_list, 30 | bbox_info, 31 | color_map, 32 | line_width, 33 | use_space 34 | }: PythonArgs = args 35 | 36 | const params = new URLSearchParams(window.location.search); 37 | const baseUrl = params.get('streamlitUrl') 38 | const [image] = useImage(baseUrl + image_url) 39 | 40 | const [rectangles, setRectangles] = React.useState( 41 | bbox_info.map((bb, i) => { 42 | return { 43 | x: bb.bbox[0], 44 | y: bb.bbox[1], 45 | width: bb.bbox[2], 46 | height: bb.bbox[3], 47 | label: bb.label, 48 | stroke: color_map[bb.label], 49 | id: 'bbox-' + i 50 | } 51 | })); 52 | const [selectedId, setSelectedId] = React.useState(null); 53 | const [label, setLabel] = useState(label_list[0]) 54 | const [mode, setMode] = React.useState('Transform'); 55 | 56 | const handleClassSelectorChange = (event: React.ChangeEvent) => { 57 | setLabel(event.target.value) 58 | console.log(selectedId) 59 | if (!(selectedId === null)) { 60 | const rects = rectangles.slice(); 61 | for (let i = 0; i < rects.length; i++) { 62 | if (rects[i].id === selectedId) { 63 | rects[i].label = event.target.value; 64 | rects[i].stroke = color_map[rects[i].label] 65 | } 66 | } 67 | setRectangles(rects) 68 | } 69 | } 70 | const [scale, setScale] = useState(1.0) 71 | useEffect(() => { 72 | const resizeCanvas = () => { 73 | const scale_ratio = window.innerWidth * 0.8 / image_size[0] 74 | setScale(Math.min(scale_ratio, 1.0)) 75 | Streamlit.setFrameHeight(image_size[1] * Math.min(scale_ratio, 1.0)) 76 | } 77 | window.addEventListener('resize', resizeCanvas); 78 | resizeCanvas() 79 | }, [image_size]) 80 | 81 | useEffect(() => { 82 | const handleKeyPress = (event: KeyboardEvent) => { 83 | if (use_space && event.key === ' ') { // 32 is the key code for Space key 84 | const currentBboxValue = rectangles.map((rect, i) => { 85 | return { 86 | bbox: [rect.x, rect.y, rect.width, rect.height], 87 | label_id: label_list.indexOf(rect.label), 88 | label: rect.label 89 | } 90 | }) 91 | Streamlit.setComponentValue(currentBboxValue) 92 | } 93 | }; 94 | window.addEventListener('keydown', handleKeyPress); 95 | return () => { 96 | window.removeEventListener('keydown', handleKeyPress); 97 | }; 98 | }, [rectangles]); 99 | 100 | return ( 101 | 102 | 103 |
104 | 105 | 106 | 120 | 121 | 122 | 123 | Mode 124 | 130 | Class 131 | 138 | 139 | 149 | 150 | 151 |
152 | 153 | 154 | ) 155 | 156 | } 157 | 158 | 159 | export default withStreamlitConnection(Detection) 160 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/src/ThemeSwitcher.tsx: -------------------------------------------------------------------------------- 1 | import React, { ReactNode } from "react" 2 | import { DarkMode, LightMode, Box } from '@chakra-ui/react' 3 | import { Theme } from 'streamlit-component-lib' 4 | 5 | 6 | type ContainerProps = { 7 | children: ReactNode; 8 | theme: Theme | undefined; 9 | } 10 | 11 | const ThemeSwitcher = ({ children, theme }: ContainerProps) => { 12 | console.log(theme) 13 | if (theme?.base !== 'light') { 14 | return ( 15 | 16 | 17 | {children} 18 | 19 | 20 | ); 21 | } else { 22 | return ( 23 | 24 | 25 | {children} 26 | 27 | 28 | ); 29 | } 30 | } 31 | 32 | export default ThemeSwitcher; -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/src/index.tsx: -------------------------------------------------------------------------------- 1 | import React from "react" 2 | import ReactDOM from "react-dom" 3 | import Detection from "./Detection" 4 | 5 | ReactDOM.render( 6 | 7 | 8 | , 9 | document.getElementById("root") 10 | ) 11 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/src/react-app-env.d.ts: -------------------------------------------------------------------------------- 1 | /// 2 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Detection/frontend/tsconfig.json: -------------------------------------------------------------------------------- 1 | { 2 | "compilerOptions": { 3 | "target": "es2020", 4 | "allowJs": true, 5 | "skipLibCheck": true, 6 | "esModuleInterop": true, 7 | "allowSyntheticDefaultImports": true, 8 | "strict": true, 9 | "forceConsistentCasingInFileNames": true, 10 | "moduleResolution": "node", 11 | "resolveJsonModule": true, 12 | "isolatedModules": true, 13 | "jsx": "react", 14 | "lib": [ 15 | "dom", 16 | "dom.iterable", 17 | "esnext" 18 | ], 19 | "module": "esnext", 20 | "noEmit": true 21 | }, 22 | "include": [ 23 | "src" 24 | ] 25 | } 26 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/__init__.py: -------------------------------------------------------------------------------- 1 | import os 2 | import streamlit.components.v1 as components 3 | from streamlit.components.v1.components import CustomComponent 4 | 5 | import streamlit as st 6 | try: 7 | from streamlit.elements.image import image_to_url 8 | except: 9 | from streamlit.elements.lib.image_utils import image_to_url 10 | from PIL import Image 11 | import numpy as np 12 | import matplotlib.pyplot as plt 13 | from hashlib import md5 14 | from streamlit_image_annotation import IS_RELEASE 15 | 16 | if IS_RELEASE: 17 | absolute_path = os.path.dirname(os.path.abspath(__file__)) 18 | build_path = os.path.join(absolute_path, "frontend/build") 19 | _component_func = components.declare_component("st_point", path=build_path) 20 | else: 21 | _component_func = components.declare_component("st_point", url="http://localhost:3000") 22 | 23 | def get_colormap(label_names, colormap_name='gist_rainbow'): 24 | colormap = {} 25 | cmap = plt.get_cmap(colormap_name) 26 | for idx, l in enumerate(label_names): 27 | rgb = [int(d) for d in np.array(cmap(float(idx)/len(label_names)))*255][:3] 28 | colormap[l] = ('#%02x%02x%02x' % tuple(rgb)) 29 | return colormap 30 | 31 | def pointdet(image_path, label_list, points=None, labels=None, height=512, width=512, point_width=3, use_space=False, key=None) -> CustomComponent: 32 | image = Image.open(image_path) 33 | original_image_size = image.size 34 | image.thumbnail(size=(width, height)) 35 | resized_image_size = image.size 36 | scale = original_image_size[0]/resized_image_size[0] 37 | 38 | image_url = image_to_url(image, image.size[0], True, "RGB", "PNG", f"point-{md5(image.tobytes()).hexdigest()}-{key}") 39 | if image_url.startswith('/'): 40 | image_url = image_url[1:] 41 | 42 | color_map = get_colormap(label_list, colormap_name='gist_rainbow') 43 | points_info = [{'point':[b/scale for b in item[0]], 'label_id': item[1], 'label': label_list[item[1]]} for item in zip(points, labels)] 44 | component_value = _component_func(image_url=image_url, image_size=image.size, label_list=label_list, points_info=points_info, color_map=color_map, point_width=point_width, use_space=use_space, key=key) 45 | if component_value is not None: 46 | component_value = [{'point':[b*scale for b in item['point']], 'label_id': item['label_id'], 'label': item['label']}for item in component_value] 47 | return component_value 48 | 49 | if not IS_RELEASE: 50 | from glob import glob 51 | import pandas as pd 52 | label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear'] 53 | image_path_list = glob('image/*.jpg') 54 | if 'result_dict' not in st.session_state: 55 | result_dict = {} 56 | for img in image_path_list: 57 | result_dict[img] = {'points': [[0,0],[50,150], [200,200]],'labels':[0,3,4]} 58 | st.session_state['result_dict'] = result_dict.copy() 59 | 60 | 61 | num_page = st.slider('page', 0, len(image_path_list)-1, 0) 62 | target_image_path = image_path_list[num_page] 63 | new_labels = pointdet(image_path=target_image_path, 64 | label_list=label_list, 65 | points=st.session_state['result_dict'][target_image_path]['points'], 66 | labels=st.session_state['result_dict'][target_image_path]['labels'], 67 | point_width=3, use_space=True, key=target_image_path) 68 | 69 | if new_labels is not None: 70 | st.session_state['result_dict'][target_image_path]['points'] = [v['point'] for v in new_labels] 71 | st.session_state['result_dict'][target_image_path]['labels'] = [v['label_id'] for v in new_labels] 72 | st.json(st.session_state['result_dict']) -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/.env: -------------------------------------------------------------------------------- 1 | # Run the component's dev server on :3001 2 | # (The Streamlit dev server already runs on :3000) 3 | PORT=3000 4 | 5 | # Don't automatically open the web browser on `npm run start`. 6 | BROWSER=none 7 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/.prettierrc: -------------------------------------------------------------------------------- 1 | { 2 | "endOfLine": "lf", 3 | "semi": false, 4 | "trailingComma": "es5" 5 | } 6 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "st_point", 3 | "version": "0.1.0", 4 | "private": true, 5 | "homepage": ".", 6 | "dependencies": { 7 | "@chakra-ui/react": "^2.5.5", 8 | "@emotion/react": "^11", 9 | "@emotion/styled": "^11", 10 | "@testing-library/jest-dom": "^5.14.1", 11 | "@testing-library/react": "^13.0.0", 12 | "@testing-library/user-event": "^13.2.1", 13 | "@types/jest": "^27.0.1", 14 | "@types/node": "^16.7.13", 15 | "@types/react": "^18.0.0", 16 | "@types/react-dom": "^18.0.0", 17 | "framer-motion": "^6", 18 | "konva": "^8.4.3", 19 | "react": "^18.2.0", 20 | "react-dom": "^18.2.0", 21 | "react-konva": "^18.2.5", 22 | "react-scripts": "5.0.1", 23 | "streamlit-component-lib": "^1.4.0", 24 | "typescript": "^4.4.2", 25 | "use-image": "^1.1.0", 26 | "web-vitals": "^2.1.0" 27 | }, 28 | "resolutions": { 29 | "streamlit-component-lib/apache-arrow": "11.0.0" 30 | }, 31 | "scripts": { 32 | "start": "react-scripts start", 33 | "build": "react-scripts build", 34 | "test": "react-scripts test", 35 | "eject": "react-scripts eject" 36 | }, 37 | "eslintConfig": { 38 | "extends": [ 39 | "react-app", 40 | "react-app/jest" 41 | ] 42 | }, 43 | "browserslist": { 44 | "production": [ 45 | ">0.2%", 46 | "not dead", 47 | "not op_mini all" 48 | ], 49 | "development": [ 50 | "last 1 chrome version", 51 | "last 1 firefox version", 52 | "last 1 safari version" 53 | ] 54 | } 55 | } 56 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Streamlit Component 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
14 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/src/Point.tsx: -------------------------------------------------------------------------------- 1 | import React, { useState, useEffect } from "react" 2 | import { Circle, Rect, Text, Transformer } from 'react-konva'; 3 | 4 | export interface RectProps { 5 | x: number, 6 | y: number, 7 | width: number, 8 | height: number, 9 | id: string, 10 | stroke: string, 11 | label: string, 12 | } 13 | export interface BBoxProps { 14 | rectProps: RectProps, 15 | onChange: any, 16 | isSelected: boolean, 17 | onClick: any, 18 | scale: number, 19 | strokeWidth: number 20 | } 21 | const Point = (props: BBoxProps)=>{ 22 | const { 23 | rectProps, onChange, isSelected, onClick, scale, strokeWidth 24 | }: BBoxProps = props 25 | 26 | return ( 27 | 28 | { 38 | onChange({ 39 | ...rectProps, 40 | x: e.target.x() / scale, 41 | y: e.target.y() / scale, 42 | }); 43 | }} 44 | /> 45 | 46 | ); 47 | }; 48 | 49 | 50 | export default Point; -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/src/PointCanvas.tsx: -------------------------------------------------------------------------------- 1 | import React, { useEffect } from "react" 2 | import { Layer, Stage, Image } from 'react-konva'; 3 | import Point from './Point' 4 | import Konva from 'konva'; 5 | 6 | export interface PointCanvasProps { 7 | pointsInfo: any[], 8 | mode: string, 9 | selectedId: string | null, 10 | setSelectedId: any, 11 | setPointsInfo: any, 12 | setLabel: any, 13 | color_map: any, 14 | scale: number, 15 | label: string, 16 | image_size: number[], 17 | image: any, 18 | strokeWidth: number 19 | } 20 | const PointCanvas = (props: PointCanvasProps) => { 21 | const { 22 | pointsInfo, 23 | mode, 24 | selectedId, 25 | setSelectedId, 26 | setPointsInfo, 27 | setLabel, 28 | color_map, 29 | scale, 30 | label, 31 | image_size, 32 | image, 33 | strokeWidth 34 | }: PointCanvasProps = props 35 | const checkDeselect = (e: any) => { 36 | console.log('DOWN') 37 | if (!(e.target instanceof Konva.Circle)) { 38 | if (selectedId === null) { 39 | if (mode === 'Transform') { 40 | const pointer = e.target.getStage().getPointerPosition() 41 | const points = pointsInfo.slice(); 42 | const new_id = Date.now().toString() 43 | points.push({ 44 | x: pointer.x / scale, 45 | y: pointer.y / scale, 46 | label: label, 47 | stroke: color_map[label], 48 | id: new_id 49 | }) 50 | setPointsInfo(points); 51 | setSelectedId(new_id); 52 | } 53 | } else { 54 | setSelectedId(null); 55 | } 56 | } 57 | }; 58 | 59 | useEffect(() => { 60 | const points = pointsInfo.slice(); 61 | for (let i = 0; i < points.length; i++) { 62 | if (points[i].x < 0 || points[i].y < 0) { 63 | points[i].x = Math.max(0, points[i].x) 64 | points[i].y = Math.max(0, points[i].y) 65 | setPointsInfo(points) 66 | } 67 | if (points[i].x > image_size[0] || points[i].y > image_size[1]) { 68 | points[i].x = Math.min(points[i].x, image_size[0]) 69 | points[i].y = Math.min(points[i].y, image_size[1]) 70 | setPointsInfo(points) 71 | } 72 | } 73 | console.log(pointsInfo) 74 | }, [pointsInfo, image_size]) 75 | return ( 76 | 78 | 79 | 80 | 81 | 82 | {pointsInfo.map((point, i) => { 83 | return ( 84 | { 91 | if (mode === 'Transform') { 92 | setSelectedId(point.id); 93 | const points = pointsInfo.slice(); 94 | const lastIndex = points.length - 1; 95 | const lastItem = points[lastIndex]; 96 | points[lastIndex] = points[i]; 97 | points[i] = lastItem; 98 | setPointsInfo(points); 99 | setLabel(point.label) 100 | } else if (mode === 'Del') { 101 | const points = pointsInfo.slice(); 102 | setPointsInfo(points.filter((element) => element.id !== point.id)); 103 | } 104 | }} 105 | onChange={(newAttrs: any) => { 106 | const points = pointsInfo.slice(); 107 | points[i] = newAttrs; 108 | setPointsInfo(points); 109 | }} 110 | /> 111 | ); 112 | })} 113 | 114 | ); 115 | }; 116 | 117 | 118 | export default PointCanvas; -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/src/PointDet.tsx: -------------------------------------------------------------------------------- 1 | import { 2 | Streamlit, 3 | withStreamlitConnection, 4 | ComponentProps 5 | } from "streamlit-component-lib" 6 | import React, { useEffect, useState } from "react" 7 | import { ChakraProvider, Select, Box, Spacer, HStack, Center, Text, Button } from '@chakra-ui/react' 8 | 9 | import useImage from 'use-image'; 10 | import ThemeSwitcher from './ThemeSwitcher' 11 | import PointCanvas from "./PointCanvas"; 12 | 13 | export interface PythonArgs { 14 | image_url: string, 15 | image_size: number[], 16 | label_list: string[], 17 | points_info: any[], 18 | color_map: any, 19 | point_width: number, 20 | use_space: boolean, 21 | } 22 | 23 | /** 24 | * This is a React-based component template. The `render()` function is called 25 | * automatically when your component should be re-rendered. 26 | */ 27 | const PointDet = ({ args, theme }: ComponentProps) => { 28 | const { 29 | image_url, 30 | image_size, 31 | label_list, 32 | points_info, 33 | color_map, 34 | point_width, 35 | use_space 36 | }: PythonArgs = args 37 | 38 | const params = new URLSearchParams(window.location.search); 39 | const baseUrl = params.get('streamlitUrl') 40 | const [image] = useImage(baseUrl + image_url) 41 | const [pointsInfo, setPointsInfo] = React.useState( 42 | points_info.map((p, i) => { 43 | return { 44 | x: p.point[0], 45 | y: p.point[1], 46 | label: p.label, 47 | stroke: color_map[p.label], 48 | id: 'point-' + i 49 | } 50 | })); 51 | const [selectedId, setSelectedId] = React.useState(null); 52 | const [label, setLabel] = useState(label_list[0]) 53 | const [mode, setMode] = React.useState('Transform'); 54 | 55 | const handleClassSelectorChange = (event: React.ChangeEvent) => { 56 | setLabel(event.target.value) 57 | console.log(selectedId) 58 | if (!(selectedId === null)) { 59 | const points = pointsInfo.slice(); 60 | for (let i = 0; i < points.length; i++) { 61 | if (points[i].id === selectedId) { 62 | points[i].label = event.target.value; 63 | points[i].stroke = color_map[points[i].label] 64 | } 65 | } 66 | setPointsInfo(points) 67 | } 68 | } 69 | const [scale, setScale] = useState(1.0) 70 | useEffect(() => { 71 | const resizeCanvas = () => { 72 | const scale_ratio = window.innerWidth * 0.8 / image_size[0] 73 | setScale(Math.min(scale_ratio, 1.0)) 74 | Streamlit.setFrameHeight(image_size[1] * Math.min(scale_ratio, 1.0)) 75 | } 76 | window.addEventListener('resize', resizeCanvas); 77 | resizeCanvas() 78 | }, [image_size]) 79 | 80 | useEffect(() => { 81 | const handleKeyPress = (event: KeyboardEvent) => { 82 | if (use_space && event.key === ' ') { // 32 is the key code for Space key 83 | const currentPointsValue = pointsInfo.map((point, i) => { 84 | return { 85 | point: [point.x, point.y], 86 | label_id: label_list.indexOf(point.label), 87 | label: point.label 88 | } 89 | }) 90 | Streamlit.setComponentValue(currentPointsValue) 91 | } 92 | }; 93 | window.addEventListener('keydown', handleKeyPress); 94 | return () => { 95 | window.removeEventListener('keydown', handleKeyPress); 96 | }; 97 | }, [pointsInfo]); 98 | 99 | return ( 100 | 101 | 102 |
103 | 104 | 105 | 119 | 120 | 121 | 122 | Mode 123 | 129 | Class 130 | 137 | 138 | 148 | 149 | 150 |
151 | 152 | 153 | ) 154 | 155 | } 156 | 157 | 158 | export default withStreamlitConnection(PointDet) 159 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/src/ThemeSwitcher.tsx: -------------------------------------------------------------------------------- 1 | import React, { ReactNode } from "react" 2 | import { DarkMode, LightMode, Box } from '@chakra-ui/react' 3 | import { Theme } from 'streamlit-component-lib' 4 | 5 | 6 | type ContainerProps = { 7 | children: ReactNode; 8 | theme: Theme | undefined; 9 | } 10 | 11 | const ThemeSwitcher = ({ children, theme }: ContainerProps) => { 12 | console.log(theme) 13 | if (theme?.base !== 'light') { 14 | return ( 15 | 16 | 17 | {children} 18 | 19 | 20 | ); 21 | } else { 22 | return ( 23 | 24 | 25 | {children} 26 | 27 | 28 | ); 29 | } 30 | } 31 | 32 | export default ThemeSwitcher; -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/src/index.tsx: -------------------------------------------------------------------------------- 1 | import React from "react" 2 | import ReactDOM from "react-dom" 3 | import PointDet from "./PointDet" 4 | 5 | ReactDOM.render( 6 | 7 | 8 | , 9 | document.getElementById("root") 10 | ) 11 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/src/react-app-env.d.ts: -------------------------------------------------------------------------------- 1 | /// 2 | -------------------------------------------------------------------------------- /streamlit_image_annotation/Point/frontend/tsconfig.json: -------------------------------------------------------------------------------- 1 | { 2 | "compilerOptions": { 3 | "target": "es2020", 4 | "allowJs": true, 5 | "skipLibCheck": true, 6 | "esModuleInterop": true, 7 | "allowSyntheticDefaultImports": true, 8 | "strict": true, 9 | "forceConsistentCasingInFileNames": true, 10 | "moduleResolution": "node", 11 | "resolveJsonModule": true, 12 | "isolatedModules": true, 13 | "jsx": "react", 14 | "lib": [ 15 | "dom", 16 | "dom.iterable", 17 | "esnext" 18 | ], 19 | "module": "esnext", 20 | "noEmit": true 21 | }, 22 | "include": [ 23 | "src" 24 | ] 25 | } 26 | -------------------------------------------------------------------------------- /streamlit_image_annotation/__init__.py: -------------------------------------------------------------------------------- 1 | IS_RELEASE = True 2 | 3 | from .Classification import classification 4 | from .Detection import detection 5 | from .Point import pointdet --------------------------------------------------------------------------------