├── .gitignore ├── LICENSE ├── README.md ├── class_names.txt ├── images ├── bus.jpg └── zidane.jpg ├── requirements.txt ├── utils.py ├── yolov5_tflite_folder_of_images_inference.py ├── yolov5_tflite_image_inference.py ├── yolov5_tflite_inference.py ├── yolov5_tflite_video_inference.py ├── yolov5_tflite_webcam_inference.py └── yolov5s-fp16.tflite /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Yolov5 export to CPU 2 | It is a two step process: 3 | 4 | 1. Convert model weights to tflite 5 | 2. Run the inference on CPU 6 | 7 | 8 | 9 | ### Convert Model Weights to tflite 10 | 11 | 12 | 13 | **If you don't want to install anything on your system then use this [Google Colab](https://colab.research.google.com/drive/1oZN9azdFyrlbzeVcGaqdddJ_YVatVTJJ?usp=sharing) (*Recommended*).** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1oZN9azdFyrlbzeVcGaqdddJ_YVatVTJJ?usp=sharing) 14 | 15 | 16 | 17 | ##### And if you want to perform the conversion on your system then follow bellow instructions: 18 | 19 | I recommend create a new conda environment for this as we need python==3.7.0 for this: 20 | 21 | ``` 22 | conda create -n yolov5_env python==3.7.0 23 | conda activate yolov5_env 24 | ``` 25 | 26 | Then run below commands and replace **yolov5s.pt** with your own model path and also change **yolov5s.yaml** accordingly. 27 | 28 | ```bash 29 | git clone https://github.com/karanjakhar/yolov5.git 30 | cd yolov5 31 | pip3 install tensorflow==2.3.1 32 | pip install -r requirements.txt 33 | python3 models/tf.py --weight yolov5s.pt --cfg models/yolov5s.yaml --img 416 34 | ``` 35 | 36 | 37 | 38 | ### Run the inference on CPU 39 | 40 | If you have created conda environment in conversion step then activate it ( `conda activate yolov5_env` ) and follow below steps. Otherwise I recommend creating a conda environment: 41 | 42 | ``` 43 | conda create -n yolov5_env python==3.7.0 44 | conda activate yolov5_env 45 | ``` 46 | 47 | then follow below steps: 48 | 49 | ```bash 50 | git clone https://github.com/karanjakhar/yolov5-export-to-cpu.git 51 | cd yolov5-export-to-cpu 52 | pip3 install -r requirements.txt 53 | python3 yolov5_tflite_folder_of_images_inference.py --weights yolov5s-fp16.tflite --folder_path images/ 54 | ``` 55 | 56 | 57 | 58 | 59 | 60 | **yolov5_tflite_inference.py** this file contains main inference code which you can use with your own project. 61 | 62 | Other files show examples how to use it. I have placed a tflite file and some sample images to run a quick test. 63 | 64 | 65 | 66 | 67 | 68 | **To Run Examples:** 69 | 70 | For webcam: 71 | 72 | `python3 yolov5_tflite_webcam_inference.py -w yolov5s-fp16.tflite ` 73 | 74 | For image: 75 | 76 | `python3 yolov5_tflite_image_inference.py -w yolov5s-fp16.tflite -i images/bus.jpg` 77 | 78 | For video: 79 | 80 | `python3 yolov5_tflite_video_inference.py -w yolov5s-fp16.tflite -v ` 81 | 82 | For folder of images: 83 | 84 | `python3 yolov5_tflite_folder_of_images_inference.py -w yolov5s-fp16.tflite -f images/` 85 | 86 | -------------------------------------------------------------------------------- /class_names.txt: -------------------------------------------------------------------------------- 1 | person 2 | bicycle 3 | car 4 | motorcycle 5 | airplane 6 | bus 7 | train 8 | truck 9 | boat 10 | traffic light 11 | fire hydrant 12 | stop sign 13 | parking meter 14 | bench 15 | bird 16 | cat 17 | dog 18 | horse 19 | sheep 20 | cow 21 | elephant 22 | bear 23 | zebra 24 | giraffe 25 | backpack 26 | umbrella 27 | handbag 28 | tie 29 | suitcase 30 | frisbee 31 | skis 32 | snowboard 33 | sports ball 34 | kite 35 | baseball bat 36 | baseball glove 37 | skateboard 38 | surfboard 39 | tennis racket 40 | bottle 41 | wine glass 42 | cup 43 | fork 44 | knife 45 | spoon 46 | bowl 47 | banana 48 | apple 49 | sandwich 50 | orange 51 | broccoli 52 | carrot 53 | hot dog 54 | pizza 55 | donut 56 | cake 57 | chair 58 | couch 59 | potted plant 60 | bed 61 | dining table 62 | toilet 63 | tv 64 | laptop 65 | mouse 66 | remote 67 | keyboard 68 | cell phone 69 | microwave 70 | oven 71 | toaster 72 | sink 73 | refrigerator 74 | book 75 | clock 76 | vase 77 | scissors 78 | teddy bear 79 | hair drier 80 | toothbrush -------------------------------------------------------------------------------- /images/bus.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karanjakhar/yolov5-export-to-cpu/10b24d6cf9f4446570b3262daf3fa7c622745e2c/images/bus.jpg -------------------------------------------------------------------------------- /images/zidane.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karanjakhar/yolov5-export-to-cpu/10b24d6cf9f4446570b3262daf3fa7c622745e2c/images/zidane.jpg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | tensorflow==2.3.1 2 | pillow 3 | argparse 4 | opencv-python 5 | numpy 6 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | 3 | 4 | def letterbox_image(image, size): 5 | iw, ih = image.size 6 | w, h = size 7 | scale = min(w / iw, h / ih) 8 | nw = int(iw * scale) 9 | nh = int(ih * scale) 10 | 11 | image = image.resize((nw, nh), Image.BICUBIC) 12 | new_image = Image.new('RGB', size, (128, 128, 128)) 13 | new_image.paste(image, ((w - nw) // 2, (h - nh) // 2)) 14 | return new_image 15 | 16 | def clip_coords(boxes, img_shape): 17 | # Clip bounding xyxy bounding boxes to image shape (height, width) 18 | 19 | boxes[:, 0].clip(0, img_shape[1], out=boxes[:, 0]) # x1 20 | boxes[:, 1].clip(0, img_shape[0], out=boxes[:, 1]) # y1 21 | boxes[:, 2].clip(0, img_shape[1], out=boxes[:, 2]) # x2 22 | boxes[:, 3].clip(0, img_shape[0], out=boxes[:, 3]) # y2 23 | 24 | 25 | def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): 26 | # Rescale coords (xyxy) from img1_shape to img0_shape 27 | if ratio_pad is None: # calculate from img0_shape 28 | gain = max(img1_shape) / max(img0_shape) # gain = old / new 29 | pad = (img1_shape[1] - img0_shape[1] * gain) / \ 30 | 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding 31 | else: 32 | gain = ratio_pad[0][0] 33 | pad = ratio_pad[1] 34 | 35 | coords[:, [0, 2]] -= pad[0] # x padding 36 | coords[:, [1, 3]] -= pad[1] # y padding 37 | coords[:, :4] /= gain 38 | clip_coords(coords, img0_shape) 39 | return coords 40 | -------------------------------------------------------------------------------- /yolov5_tflite_folder_of_images_inference.py: -------------------------------------------------------------------------------- 1 | from yolov5_tflite_image_inference import detect_image 2 | import argparse 3 | from glob import glob 4 | import os 5 | 6 | 7 | def detect_from_folder_of_images(weights,folder_path,img_size,conf_thres,iou_thres): 8 | 9 | for file in glob(os.path.join(folder_path,'*')): 10 | print('Processing ',file,' now ...') 11 | 12 | detect_image(weights,file,img_size,conf_thres,iou_thres) 13 | 14 | 15 | 16 | if __name__ == '__main__': 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('-w','--weights', type=str, default='yolov5s-fp16.tflite', help='model.tflite path(s)') 19 | parser.add_argument('-f','--folder_path', type=str,required=True, help='folder path') # file/folder, 0 for webcam 20 | parser.add_argument('--img_size', type=int, default=416, help='image size') # height, width 21 | parser.add_argument('--conf_thres', type=float, default=0.25, help='object confidence threshold') 22 | parser.add_argument('--iou_thres', type=float, default=0.45, help='IOU threshold for NMS') 23 | 24 | 25 | opt = parser.parse_args() 26 | 27 | print(opt) 28 | detect_from_folder_of_images(opt.weights,opt.folder_path,opt.img_size,opt.conf_thres,opt.iou_thres) -------------------------------------------------------------------------------- /yolov5_tflite_image_inference.py: -------------------------------------------------------------------------------- 1 | from yolov5_tflite_inference import yolov5_tflite 2 | import argparse 3 | import cv2 4 | import time 5 | from PIL import Image, ImageOps 6 | import numpy as np 7 | from utils import letterbox_image, scale_coords 8 | 9 | 10 | 11 | def detect_image(weights,image_url,img_size,conf_thres,iou_thres): 12 | 13 | 14 | 15 | start_time = time.time() 16 | 17 | 18 | #image = cv2.imread(image_url) 19 | image = Image.open(image_url) 20 | original_size = image.size[:2] 21 | size = (img_size,img_size) 22 | image_resized = letterbox_image(image,size) 23 | img = np.asarray(image) 24 | 25 | #image = ImageOps.fit(image, size, Image.ANTIALIAS) 26 | image_array = np.asarray(image_resized) 27 | 28 | normalized_image_array = image_array.astype(np.float32) / 255.0 29 | 30 | 31 | 32 | yolov5_tflite_obj = yolov5_tflite(weights,img_size,conf_thres,iou_thres) 33 | 34 | result_boxes, result_scores, result_class_names = yolov5_tflite_obj.detect(normalized_image_array) 35 | 36 | 37 | if len(result_boxes) > 0: 38 | result_boxes = scale_coords(size,np.array(result_boxes),(original_size[1],original_size[0])) 39 | font = cv2.FONT_HERSHEY_SIMPLEX 40 | 41 | # org 42 | org = (20, 40) 43 | 44 | # fontScale 45 | fontScale = 0.5 46 | 47 | # Blue color in BGR 48 | color = (0, 255, 0) 49 | 50 | # Line thickness of 1 px 51 | thickness = 1 52 | 53 | for i,r in enumerate(result_boxes): 54 | 55 | org = (int(r[0]),int(r[1])) 56 | cv2.rectangle(img, (int(r[0]),int(r[1])), (int(r[2]),int(r[3])), (255,0,0), 1) 57 | cv2.putText(img, str(int(100*result_scores[i])) + '% ' + str(result_class_names[i]), org, font, 58 | fontScale, color, thickness, cv2.LINE_AA) 59 | 60 | save_result_filepath = image_url.split('/')[-1].split('.')[0] + 'yolov5_output.jpg' 61 | cv2.imwrite(save_result_filepath,img[:,:,::-1]) 62 | 63 | end_time = time.time() 64 | 65 | print('FPS:',1/(end_time-start_time)) 66 | print('Total Time Taken:',end_time-start_time) 67 | 68 | 69 | 70 | if __name__ == '__main__': 71 | parser = argparse.ArgumentParser() 72 | parser.add_argument('-w','--weights', type=str, default='yolov5s-fp16.tflite', help='model.tflite path(s)') 73 | parser.add_argument('-i','--img_path', type=str, required=True, help='image path') 74 | parser.add_argument('--img_size', type=int, default=416, help='image size') 75 | parser.add_argument('--conf_thres', type=float, default=0.25, help='object confidence threshold') 76 | parser.add_argument('--iou_thres', type=float, default=0.45, help='IOU threshold for NMS') 77 | 78 | 79 | opt = parser.parse_args() 80 | 81 | print(opt) 82 | detect_image(opt.weights,opt.img_path,opt.img_size,opt.conf_thres,opt.iou_thres) 83 | 84 | 85 | -------------------------------------------------------------------------------- /yolov5_tflite_inference.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import tensorflow as tf 3 | 4 | 5 | 6 | class yolov5_tflite: 7 | 8 | def __init__(self,weights = 'yolov5s-fp16.tflite',image_size = 416,conf_thres=0.25,iou_thres=0.45): 9 | 10 | self.weights = weights 11 | self.image_size = image_size 12 | self.conf_thres = conf_thres 13 | self.iou_thres = iou_thres 14 | self.interpreter = tf.lite.Interpreter(self.weights) 15 | self.interpreter.allocate_tensors() 16 | self.input_details = self.interpreter.get_input_details() 17 | self.output_details = self.interpreter.get_output_details() 18 | 19 | with open('class_names.txt') as f: 20 | self.names = [line.rstrip() for line in f] 21 | 22 | 23 | 24 | def xywh2xyxy(self,x): 25 | # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right 26 | y = x.copy() 27 | y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x 28 | y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y 29 | y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x 30 | y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y 31 | return y 32 | 33 | 34 | def non_max_suppression(self,boxes, scores, threshold): 35 | assert boxes.shape[0] == scores.shape[0] 36 | # bottom-left origin 37 | ys1 = boxes[:, 0] 38 | xs1 = boxes[:, 1] 39 | # top-right target 40 | ys2 = boxes[:, 2] 41 | xs2 = boxes[:, 3] 42 | # box coordinate ranges are inclusive-inclusive 43 | areas = (ys2 - ys1) * (xs2 - xs1) 44 | scores_indexes = scores.argsort().tolist() 45 | boxes_keep_index = [] 46 | while len(scores_indexes): 47 | index = scores_indexes.pop() 48 | boxes_keep_index.append(index) 49 | if not len(scores_indexes): 50 | break 51 | ious = self.compute_iou(boxes[index], boxes[scores_indexes], areas[index], 52 | areas[scores_indexes]) 53 | filtered_indexes = set((ious > threshold).nonzero()[0]) 54 | # if there are no more scores_index 55 | # then we should pop it 56 | scores_indexes = [ 57 | v for (i, v) in enumerate(scores_indexes) 58 | if i not in filtered_indexes 59 | ] 60 | return np.array(boxes_keep_index) 61 | 62 | 63 | def compute_iou(self,box, boxes, box_area, boxes_area): 64 | # this is the iou of the box against all other boxes 65 | assert boxes.shape[0] == boxes_area.shape[0] 66 | # get all the origin-ys 67 | # push up all the lower origin-xs, while keeping the higher origin-xs 68 | ys1 = np.maximum(box[0], boxes[:, 0]) 69 | # get all the origin-xs 70 | # push right all the lower origin-xs, while keeping higher origin-xs 71 | xs1 = np.maximum(box[1], boxes[:, 1]) 72 | # get all the target-ys 73 | # pull down all the higher target-ys, while keeping lower origin-ys 74 | ys2 = np.minimum(box[2], boxes[:, 2]) 75 | # get all the target-xs 76 | # pull left all the higher target-xs, while keeping lower target-xs 77 | xs2 = np.minimum(box[3], boxes[:, 3]) 78 | # each intersection area is calculated by the 79 | # pulled target-x minus the pushed origin-x 80 | # multiplying 81 | # pulled target-y minus the pushed origin-y 82 | # we ignore areas where the intersection side would be negative 83 | # this is done by using maxing the side length by 0 84 | intersections = np.maximum(ys2 - ys1, 0) * np.maximum(xs2 - xs1, 0) 85 | # each union is then the box area 86 | # added to each other box area minusing their intersection calculated above 87 | unions = box_area + boxes_area - intersections 88 | # element wise division 89 | # if the intersection is 0, then their ratio is 0 90 | ious = intersections / unions 91 | return ious 92 | 93 | 94 | 95 | 96 | def nms(self,prediction): 97 | 98 | 99 | 100 | 101 | prediction = prediction[prediction[...,4] > self.conf_thres] 102 | 103 | # Box (center x, center y, width, height) to (x1, y1, x2, y2) 104 | boxes = self.xywh2xyxy(prediction[:, :4]) 105 | 106 | res = self.non_max_suppression(boxes,prediction[:,4],self.iou_thres) 107 | 108 | result_boxes = [] 109 | result_scores = [] 110 | result_class_names = [] 111 | for r in res: 112 | result_boxes.append(boxes[r]) 113 | result_scores.append(prediction[r,4]) 114 | result_class_names.append(self.names[np.argmax(prediction[r,5:])]) 115 | 116 | 117 | return result_boxes, result_scores, result_class_names 118 | 119 | 120 | 121 | def detect(self,image): 122 | original_size = image.shape[:2] 123 | input_data = np.ndarray(shape=(1, self.image_size, self.image_size, 3), dtype=np.float32) 124 | #image = cv2.resize(image,(self.image_size,self.image_size)) 125 | #input_data[0] = image.astype(np.float32)/255.0 126 | input_data[0] = image 127 | 128 | #self.interpreter.allocate_tensors() 129 | 130 | # Get input and output tensors 131 | #input_details = self.interpreter.get_input_details() 132 | #output_details = self.interpreter.get_output_details() 133 | 134 | 135 | self.interpreter.set_tensor(self.input_details[0]['index'], input_data) 136 | self.interpreter.invoke() 137 | pred = self.interpreter.get_tensor(self.output_details[0]['index']) 138 | 139 | # Denormalize xywh 140 | pred[..., 0] *= original_size[1] # x 141 | pred[..., 1] *= original_size[0] # y 142 | pred[..., 2] *= original_size[1] # w 143 | pred[..., 3] *= original_size[0] # h 144 | 145 | 146 | 147 | result_boxes, result_scores, result_class_names = self.nms(pred) 148 | 149 | return result_boxes,result_scores, result_class_names 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | -------------------------------------------------------------------------------- /yolov5_tflite_video_inference.py: -------------------------------------------------------------------------------- 1 | from yolov5_tflite_inference import yolov5_tflite 2 | import argparse 3 | import cv2 4 | from PIL import Image 5 | from utils import letterbox_image, scale_coords 6 | import numpy as np 7 | import time 8 | 9 | def detect_video(weights,video_filepath,img_size,conf_thres,iou_thres): 10 | 11 | start_time = time.time() 12 | 13 | fourcc = cv2.VideoWriter_fourcc(*'mp4v') 14 | video = cv2.VideoCapture(video_filepath) 15 | fps = video.get(cv2.CAP_PROP_FPS) 16 | h = int(video.get(3)) 17 | w = int(video.get(4)) 18 | print(w,h) 19 | #h = 1280 20 | #w = 720 21 | result_video_filepath = video_filepath.split('/')[-1].split('.')[0] + 'yolov5_output.mp4' 22 | out = cv2.VideoWriter(result_video_filepath,fourcc,int(fps),(h,w)) 23 | 24 | yolov5_tflite_obj = yolov5_tflite(weights,img_size,conf_thres,iou_thres) 25 | 26 | size = (img_size,img_size) 27 | no_of_frames = 0 28 | 29 | while True: 30 | 31 | check, frame = video.read() 32 | 33 | if not check: 34 | break 35 | #frame = cv2.resize(frame,(h,w)) 36 | no_of_frames += 1 37 | image_resized = letterbox_image(Image.fromarray(frame),size) 38 | image_array = np.asarray(image_resized) 39 | 40 | normalized_image_array = image_array.astype(np.float32) / 255.0 41 | result_boxes, result_scores, result_class_names = yolov5_tflite_obj.detect(normalized_image_array) 42 | 43 | if len(result_boxes) > 0: 44 | result_boxes = scale_coords(size,np.array(result_boxes),(w,h)) 45 | font = cv2.FONT_HERSHEY_SIMPLEX 46 | 47 | # org 48 | org = (20, 40) 49 | 50 | # fontScale 51 | fontScale = 0.5 52 | 53 | # Blue color in BGR 54 | color = (0, 255, 0) 55 | 56 | # Line thickness of 1 px 57 | thickness = 1 58 | 59 | for i,r in enumerate(result_boxes): 60 | 61 | org = (int(r[0]),int(r[1])) 62 | cv2.rectangle(frame, (int(r[0]),int(r[1])), (int(r[2]),int(r[3])), (255,0,0), 1) 63 | cv2.putText(frame, str(int(100*result_scores[i])) + '% ' + str(result_class_names[i]), org, font, 64 | fontScale, color, thickness, cv2.LINE_AA) 65 | 66 | 67 | out.write(frame) 68 | end_time = time.time() 69 | print('FPS:',1/(end_time-start_time)) 70 | start_time = end_time 71 | out.release() 72 | 73 | 74 | 75 | if __name__ == '__main__': 76 | parser = argparse.ArgumentParser() 77 | parser.add_argument('-w','--weights', type=str, default='yolov5s-fp16.tflite', help='model.tflite path(s)') 78 | parser.add_argument('-v','--video_path', type=str, required=True, help='video path') 79 | parser.add_argument('--img_size', type=int, default=416, help='image size') # height, width 80 | parser.add_argument('--conf_thres', type=float, default=0.25, help='object confidence threshold') 81 | parser.add_argument('--iou_thres', type=float, default=0.45, help='IOU threshold for NMS') 82 | 83 | 84 | opt = parser.parse_args() 85 | 86 | print(opt) 87 | detect_video(opt.weights,opt.video_path,opt.img_size,opt.conf_thres,opt.iou_thres) -------------------------------------------------------------------------------- /yolov5_tflite_webcam_inference.py: -------------------------------------------------------------------------------- 1 | from yolov5_tflite_inference import yolov5_tflite 2 | import argparse 3 | import cv2 4 | from PIL import Image 5 | from utils import letterbox_image, scale_coords 6 | import numpy as np 7 | import time 8 | 9 | def detect_video(weights,webcam,img_size,conf_thres,iou_thres): 10 | 11 | start_time = time.time() 12 | 13 | fourcc = cv2.VideoWriter_fourcc(*'mp4v') 14 | video = cv2.VideoCapture(webcam) 15 | fps = video.get(cv2.CAP_PROP_FPS) 16 | #print(fps) 17 | h = int(video.get(3)) 18 | w = int(video.get(4)) 19 | print(w,h) 20 | #h = 1280 21 | #w = 720 22 | result_video_filepath = 'webcam_yolov5_output.mp4' 23 | out = cv2.VideoWriter(result_video_filepath,fourcc,int(fps),(h,w)) 24 | 25 | yolov5_tflite_obj = yolov5_tflite(weights,img_size,conf_thres,iou_thres) 26 | 27 | size = (img_size,img_size) 28 | no_of_frames = 0 29 | try: 30 | while True: 31 | 32 | check, frame = video.read() 33 | 34 | if not check: 35 | break 36 | #frame = cv2.resize(frame,(h,w)) 37 | #no_of_frames += 1 38 | image_resized = letterbox_image(Image.fromarray(frame),size) 39 | image_array = np.asarray(image_resized) 40 | 41 | normalized_image_array = image_array.astype(np.float32) / 255.0 42 | result_boxes, result_scores, result_class_names = yolov5_tflite_obj.detect(normalized_image_array) 43 | 44 | if len(result_boxes) > 0: 45 | result_boxes = scale_coords(size,np.array(result_boxes),(w,h)) 46 | font = cv2.FONT_HERSHEY_SIMPLEX 47 | 48 | # org 49 | org = (20, 40) 50 | 51 | # fontScale 52 | fontScale = 0.5 53 | 54 | # Blue color in BGR 55 | color = (0, 255, 0) 56 | 57 | # Line thickness of 1 px 58 | thickness = 1 59 | 60 | for i,r in enumerate(result_boxes): 61 | 62 | org = (int(r[0]),int(r[1])) 63 | cv2.rectangle(frame, (int(r[0]),int(r[1])), (int(r[2]),int(r[3])), (255,0,0), 1) 64 | cv2.putText(frame, str(int(100*result_scores[i])) + '% ' + str(result_class_names[i]), org, font, 65 | fontScale, color, thickness, cv2.LINE_AA) 66 | 67 | 68 | out.write(frame) 69 | 70 | cv2.imshow('output',frame) 71 | if cv2.waitKey(1) & 0xFF == ord('q'): 72 | break 73 | end_time = time.time() 74 | print('FPS:',1/(end_time-start_time)) 75 | start_time = end_time 76 | out.release() 77 | except: 78 | out.release() 79 | 80 | 81 | 82 | if __name__ == '__main__': 83 | parser = argparse.ArgumentParser() 84 | parser.add_argument('-w','--weights', type=str, default='yolov5s-fp16.tflite', help='model.tflite path(s)') 85 | parser.add_argument('-wc','--webcam', type=int, default=0, help='webcam number 0,1,2 etc.') 86 | parser.add_argument('--img_size', type=int, default=416, help='image size') # height, width 87 | parser.add_argument('--conf_thres', type=float, default=0.25, help='object confidence threshold') 88 | parser.add_argument('--iou_thres', type=float, default=0.45, help='IOU threshold for NMS') 89 | 90 | 91 | opt = parser.parse_args() 92 | 93 | print(opt) 94 | detect_video(opt.weights,opt.webcam,opt.img_size,opt.conf_thres,opt.iou_thres) -------------------------------------------------------------------------------- /yolov5s-fp16.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karanjakhar/yolov5-export-to-cpu/10b24d6cf9f4446570b3262daf3fa7c622745e2c/yolov5s-fp16.tflite --------------------------------------------------------------------------------