├── .gitignore ├── LICENSE ├── README.md ├── classes.txt ├── main.py ├── requirements.txt ├── src ├── bottle.jpg ├── cup.jpg ├── keyboard.jpg ├── mobile.jpg ├── person.jpg ├── person1.jpg └── scissors.jpg ├── yolov4-tiny.cfg └── yolov4-tiny.weights /.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 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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2 |

Real-Time Object Detection and Distance Measurement

3 | 4 |
5 | 6 | ### AccelATHON 3.0 7 | Team Name: Cipher 8 | 9 | ### Problem Statement 10 | 11 | Real World Distance measurement by detecting and identifying the Object using WebCam. A prototype that can help blind people navigate smoothly. 12 | 13 | Algorithm used: YoloV4:Yolo i.e., You only look once. 14 | Dataset uses: COCO i.e., Common Objects in Context 15 | 16 | Future Goal: Implementing the project on Hardware kit, i.e., ESP32 CAM. 17 | 18 | ### Requirements 19 | 20 | ```py 21 | pip install -r requirements.txt 22 | 23 | ``` 24 | 25 | ## Code Flow 26 | 27 | - Import OpenCV Module and create a function to detect objects. 28 | - Create two more functions to calculate Distance and Focal length 29 | - Create an instance of DetectionModel and pass reference images to the detected object. 30 | - Using cv2, capture the real-time Video using WebCam and measure distance in inches. 31 | ## Screenshots 32 | 33 | ![outcome](https://user-images.githubusercontent.com/66197713/188265636-a2e529ba-800f-4a49-9643-fc3e4460ccd8.png) 34 | 35 | 36 | ## Presentation 37 | 38 | [Canva Presentation- Team Cipher](https://www.canva.com/design/DAFLKeN0WFE/0tIHlfOiTvCe71TXL7bPWw/view?utm_content=DAFLKeN0WFE&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton) 39 | 40 | ## Future Aspects- Applications 41 | 42 | - Self Driving Cars 43 | - Vision to Blind 44 | - Argumented Reality 45 | 46 | ## Conclusion 47 | 48 | - In the given problem statement, Objects were detected successfully. 49 | - To detect the Objects successfully YOLO dataset came in handy. 50 | - Implemented a Prototype for the problem statement i.e., to find the distance. 51 | - In future this problem statement code will be dumped on Raspberry PI or ESP-32 52 | ## Authors 53 | 54 | - [@TRJ_0751](https://www.twitter.com/TRJ_0751) 55 | -------------------------------------------------------------------------------- /classes.txt: -------------------------------------------------------------------------------- 1 | person 2 | bicycle 3 | car 4 | motorbike 5 | aeroplane 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 | sofa 59 | pottedplant 60 | bed 61 | diningtable 62 | toilet 63 | tvmonitor 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 -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import warnings 3 | import os 4 | warnings.filterwarnings("ignore") 5 | import termcolor 6 | 7 | KNOWN_DISTANCE = 48 8 | PERSON_WIDTH = 15 9 | CUP_WIDTH = 3 10 | KEYBOARD_WIDTH = 4 11 | MOBILE_WIDTH = 3 12 | SCISSOR_WIDTH = 3 13 | 14 | FONTS = cv2.FONT_HERSHEY_TRIPLEX 15 | 16 | def detect_object(object): 17 | classes, scores, boxes = model.detect(object,0.4,0.3) 18 | data_list =[] 19 | for (classid, score, box) in zip(classes, scores, boxes): 20 | cv2.rectangle(object, box,(0,0,255), 2) 21 | cv2.putText(object,"{}:{}".format(class_names[classid],format(score,'.2f')), (box[0], box[1]-14), FONTS,0.6,(0,255,0), 3) 22 | 23 | if classid ==0: #person 24 | data_list.append([class_names[classid], box[2], (box[0], box[1]-2)]) 25 | elif classid == 41: #cup 26 | data_list.append([class_names[classid], box[2], (box[0], box[1]-2)]) 27 | elif classid ==66: #keyboard 28 | data_list.append([class_names[classid], box[2], (box[0], box[1]-2)]) 29 | elif classid ==67: #cell phone 30 | data_list.append([class_names[classid], box[2], (box[0], box[1]-2)]) 31 | elif classid ==76: #scissors 32 | data_list.append([class_names[classid], box[2], (box[0], box[1]-2)]) 33 | return data_list 34 | 35 | def cal_distance(f,W,w): 36 | return (w * f) / W 37 | 38 | def cal_focalLength(d, W, w): 39 | return (W * d) / w*2 40 | 41 | class_names = [] 42 | with open("classes.txt", "r") as objects_file: 43 | class_names = [e_g.strip() for e_g in objects_file.readlines()] 44 | 45 | yoloNet = cv2.dnn.readNet('yolov4-tiny.weights', 'yolov4-tiny.cfg') 46 | 47 | model = cv2.dnn_DetectionModel(yoloNet) 48 | model.setInputParams(size=(416, 416), scale=1/255, swapRB=True) 49 | 50 | person_image_path = os.path.join("src","person.jpg") 51 | cup_image_path = os.path.join("src","cup.jpg") 52 | kb_image_path = os.path.join("src","keyboard.jpg") 53 | moblie_image_path = os.path.join("src","mobile.jpg") 54 | scissors_image_path = os.path.join("src","scissors.jpg") 55 | 56 | 57 | person_data = detect_object(cv2.imread(person_image_path)) 58 | person_width_in_rf = person_data[0][1] 59 | 60 | """keyboard_data = detect_object(cv2.imread(kb_image_path)) 61 | #print(keyboard_data) 62 | keyboard_width_in_rf = keyboard_data[1][1]""" 63 | 64 | mobile_data = detect_object(cv2.imread(moblie_image_path)) 65 | #print(mobile_data) 66 | mobile_width_in_rf = mobile_data[0][1] 67 | 68 | scissor_data = detect_object(cv2.imread(scissors_image_path)) 69 | #print(scissor_data) 70 | scissor_width_in_rf = scissor_data[0][1] 71 | 72 | cup_data = detect_object(cv2.imread(cup_image_path)) 73 | #print(cup_data) 74 | cup_width_in_rf = cup_data[1][1] 75 | 76 | 77 | focal_person = cal_focalLength(KNOWN_DISTANCE, PERSON_WIDTH, person_width_in_rf) 78 | focal_cup = cal_focalLength(KNOWN_DISTANCE, CUP_WIDTH, cup_width_in_rf) 79 | #focal_kb = cal_focalLength(KNOWN_DISTANCE, KEYBOARD_WIDTH, keyboard_width_in_rf) 80 | focal_mobile = cal_focalLength(KNOWN_DISTANCE, MOBILE_WIDTH, mobile_width_in_rf) 81 | focal_scissor = cal_focalLength(KNOWN_DISTANCE, SCISSOR_WIDTH, scissor_width_in_rf) 82 | 83 | try: 84 | capture = cv2.VideoCapture(0) 85 | while True: 86 | _,frame = capture.read() 87 | 88 | data = detect_object(frame) 89 | for d in data: 90 | print(d) 91 | if d[0] =='person': 92 | distance = cal_distance(focal_person, PERSON_WIDTH, d[1]) 93 | x,y = d[2] 94 | elif d[0] =='cup': 95 | distance = cal_distance(focal_cup, CUP_WIDTH, d[1]) 96 | x, y = d[2] 97 | elif d[0] =='cell phone': 98 | distance = cal_distance(focal_mobile, MOBILE_WIDTH, d[1]) 99 | x, y = d[2] 100 | elif d[0] =='scissors': 101 | distance = cal_distance(focal_scissor, SCISSOR_WIDTH, d[1]) 102 | x, y = d[2] 103 | 104 | cv2.rectangle(frame, (x,y-3), (x+150, y+23),(255,255,255),-1) 105 | cv2.putText(frame,f"Distance:{format(distance,'.2f')}inchs", (x+5,y+13), FONTS, 0.45,(255,0,0), 2) 106 | 107 | print("Distance of {} is {} inchs".format(d[0],distance)) 108 | 109 | cv2.imshow('frame',frame) 110 | exit_key_press = cv2.waitKey(1) 111 | 112 | if exit_key_press == ord('q'): 113 | break 114 | 115 | capture.release() 116 | cv2.waitKey(0) 117 | cv2.destroyAllWindows() 118 | except cv2.error: 119 | termcolor.cprint("Select the WebCam or Camera index properly, in my case it is 2","red") 120 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | cv2 2 | termcolor -------------------------------------------------------------------------------- /src/bottle.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lucifertrj/Real-Time-Object-Distance-Measurement/7b89deded4783f8cdc0c9bd9e8ce9677e8b4d682/src/bottle.jpg -------------------------------------------------------------------------------- /src/cup.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lucifertrj/Real-Time-Object-Distance-Measurement/7b89deded4783f8cdc0c9bd9e8ce9677e8b4d682/src/cup.jpg -------------------------------------------------------------------------------- /src/keyboard.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lucifertrj/Real-Time-Object-Distance-Measurement/7b89deded4783f8cdc0c9bd9e8ce9677e8b4d682/src/keyboard.jpg -------------------------------------------------------------------------------- /src/mobile.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lucifertrj/Real-Time-Object-Distance-Measurement/7b89deded4783f8cdc0c9bd9e8ce9677e8b4d682/src/mobile.jpg -------------------------------------------------------------------------------- /src/person.jpg: 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-------------------------------------------------------------------------------- 1 | [net] 2 | # Testing 3 | #batch=1 4 | #subdivisions=1 5 | # Training 6 | batch=64 7 | subdivisions=1 8 | width=416 9 | height=416 10 | channels=3 11 | momentum=0.9 12 | decay=0.0005 13 | angle=0 14 | saturation = 1.5 15 | exposure = 1.5 16 | hue=.1 17 | 18 | learning_rate=0.00261 19 | burn_in=1000 20 | 21 | max_batches = 2000200 22 | policy=steps 23 | steps=1600000,1800000 24 | scales=.1,.1 25 | 26 | 27 | #weights_reject_freq=1001 28 | #ema_alpha=0.9998 29 | #equidistant_point=1000 30 | #num_sigmas_reject_badlabels=3 31 | #badlabels_rejection_percentage=0.2 32 | 33 | 34 | [convolutional] 35 | batch_normalize=1 36 | filters=32 37 | size=3 38 | stride=2 39 | pad=1 40 | activation=leaky 41 | 42 | [convolutional] 43 | batch_normalize=1 44 | filters=64 45 | size=3 46 | stride=2 47 | pad=1 48 | activation=leaky 49 | 50 | [convolutional] 51 | batch_normalize=1 52 | filters=64 53 | size=3 54 | stride=1 55 | pad=1 56 | activation=leaky 57 | 58 | [route] 59 | layers=-1 60 | groups=2 61 | group_id=1 62 | 63 | [convolutional] 64 | batch_normalize=1 65 | filters=32 66 | size=3 67 | stride=1 68 | pad=1 69 | activation=leaky 70 | 71 | [convolutional] 72 | batch_normalize=1 73 | filters=32 74 | size=3 75 | stride=1 76 | pad=1 77 | activation=leaky 78 | 79 | [route] 80 | layers = -1,-2 81 | 82 | [convolutional] 83 | batch_normalize=1 84 | filters=64 85 | size=1 86 | stride=1 87 | pad=1 88 | activation=leaky 89 | 90 | [route] 91 | layers = -6,-1 92 | 93 | [maxpool] 94 | size=2 95 | stride=2 96 | 97 | [convolutional] 98 | batch_normalize=1 99 | filters=128 100 | size=3 101 | stride=1 102 | pad=1 103 | activation=leaky 104 | 105 | [route] 106 | layers=-1 107 | groups=2 108 | group_id=1 109 | 110 | [convolutional] 111 | batch_normalize=1 112 | filters=64 113 | size=3 114 | stride=1 115 | pad=1 116 | activation=leaky 117 | 118 | [convolutional] 119 | batch_normalize=1 120 | filters=64 121 | size=3 122 | stride=1 123 | pad=1 124 | activation=leaky 125 | 126 | [route] 127 | layers = -1,-2 128 | 129 | [convolutional] 130 | batch_normalize=1 131 | filters=128 132 | size=1 133 | stride=1 134 | pad=1 135 | activation=leaky 136 | 137 | [route] 138 | layers = -6,-1 139 | 140 | [maxpool] 141 | size=2 142 | stride=2 143 | 144 | [convolutional] 145 | batch_normalize=1 146 | filters=256 147 | size=3 148 | stride=1 149 | pad=1 150 | activation=leaky 151 | 152 | [route] 153 | layers=-1 154 | groups=2 155 | group_id=1 156 | 157 | [convolutional] 158 | batch_normalize=1 159 | filters=128 160 | size=3 161 | stride=1 162 | pad=1 163 | activation=leaky 164 | 165 | [convolutional] 166 | batch_normalize=1 167 | filters=128 168 | size=3 169 | stride=1 170 | pad=1 171 | activation=leaky 172 | 173 | [route] 174 | layers = -1,-2 175 | 176 | [convolutional] 177 | batch_normalize=1 178 | filters=256 179 | size=1 180 | stride=1 181 | pad=1 182 | activation=leaky 183 | 184 | [route] 185 | layers = -6,-1 186 | 187 | [maxpool] 188 | size=2 189 | stride=2 190 | 191 | [convolutional] 192 | batch_normalize=1 193 | filters=512 194 | size=3 195 | stride=1 196 | pad=1 197 | activation=leaky 198 | 199 | ################################## 200 | 201 | [convolutional] 202 | batch_normalize=1 203 | filters=256 204 | size=1 205 | stride=1 206 | pad=1 207 | activation=leaky 208 | 209 | [convolutional] 210 | batch_normalize=1 211 | filters=512 212 | size=3 213 | stride=1 214 | pad=1 215 | activation=leaky 216 | 217 | [convolutional] 218 | size=1 219 | stride=1 220 | pad=1 221 | filters=255 222 | activation=linear 223 | 224 | 225 | 226 | [yolo] 227 | mask = 3,4,5 228 | anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 229 | classes=80 230 | num=6 231 | jitter=.3 232 | scale_x_y = 1.05 233 | cls_normalizer=1.0 234 | iou_normalizer=0.07 235 | iou_loss=ciou 236 | ignore_thresh = .7 237 | truth_thresh = 1 238 | random=0 239 | resize=1.5 240 | nms_kind=greedynms 241 | beta_nms=0.6 242 | #new_coords=1 243 | #scale_x_y = 2.0 244 | 245 | [route] 246 | layers = -4 247 | 248 | [convolutional] 249 | batch_normalize=1 250 | filters=128 251 | size=1 252 | stride=1 253 | pad=1 254 | activation=leaky 255 | 256 | [upsample] 257 | stride=2 258 | 259 | [route] 260 | layers = -1, 23 261 | 262 | [convolutional] 263 | batch_normalize=1 264 | filters=256 265 | size=3 266 | stride=1 267 | pad=1 268 | activation=leaky 269 | 270 | [convolutional] 271 | size=1 272 | stride=1 273 | pad=1 274 | filters=255 275 | activation=linear 276 | 277 | [yolo] 278 | mask = 1,2,3 279 | anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 280 | classes=80 281 | num=6 282 | jitter=.3 283 | scale_x_y = 1.05 284 | cls_normalizer=1.0 285 | iou_normalizer=0.07 286 | iou_loss=ciou 287 | ignore_thresh = .7 288 | truth_thresh = 1 289 | random=0 290 | resize=1.5 291 | nms_kind=greedynms 292 | beta_nms=0.6 293 | #new_coords=1 294 | #scale_x_y = 2.0 -------------------------------------------------------------------------------- /yolov4-tiny.weights: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lucifertrj/Real-Time-Object-Distance-Measurement/7b89deded4783f8cdc0c9bd9e8ce9677e8b4d682/yolov4-tiny.weights --------------------------------------------------------------------------------