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Open Source Computer Vision Library
https://opencv.org/
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132 lines
6.7 KiB
132 lines
6.7 KiB
# This script is used to demonstrate MobileNet-SSD network using OpenCV deep learning module. |
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# |
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# It works with model taken from https://github.com/chuanqi305/MobileNet-SSD/ that |
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# was trained in Caffe-SSD framework, https://github.com/weiliu89/caffe/tree/ssd. |
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# Model detects objects from 20 classes. |
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# |
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# Also TensorFlow model from TensorFlow object detection model zoo may be used to |
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# detect objects from 90 classes: |
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# http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz |
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# Text graph definition must be taken from opencv_extra: |
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# https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/ssd_mobilenet_v1_coco.pbtxt |
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import numpy as np |
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import argparse |
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try: |
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import cv2 as cv |
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except ImportError: |
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raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, ' |
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'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)') |
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inWidth = 300 |
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inHeight = 300 |
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WHRatio = inWidth / float(inHeight) |
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inScaleFactor = 0.007843 |
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meanVal = 127.5 |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser( |
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description='Script to run MobileNet-SSD object detection network ' |
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'trained either in Caffe or TensorFlow frameworks.') |
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parser.add_argument("--video", help="path to video file. If empty, camera's stream will be used") |
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parser.add_argument("--prototxt", default="MobileNetSSD_deploy.prototxt", |
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help='Path to text network file: ' |
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'MobileNetSSD_deploy.prototxt for Caffe model or ' |
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'ssd_mobilenet_v1_coco.pbtxt from opencv_extra for TensorFlow model') |
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parser.add_argument("--weights", default="MobileNetSSD_deploy.caffemodel", |
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help='Path to weights: ' |
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'MobileNetSSD_deploy.caffemodel for Caffe model or ' |
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'frozen_inference_graph.pb from TensorFlow.') |
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parser.add_argument("--num_classes", default=20, type=int, |
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help="Number of classes. It's 20 for Caffe model from " |
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"https://github.com/chuanqi305/MobileNet-SSD/ and 90 for " |
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"TensorFlow model from http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz") |
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parser.add_argument("--thr", default=0.2, type=float, help="confidence threshold to filter out weak detections") |
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args = parser.parse_args() |
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if args.num_classes == 20: |
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net = cv.dnn.readNetFromCaffe(args.prototxt, args.weights) |
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swapRB = False |
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classNames = { 0: 'background', |
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1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat', |
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5: 'bottle', 6: 'bus', 7: 'car', 8: 'cat', 9: 'chair', |
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10: 'cow', 11: 'diningtable', 12: 'dog', 13: 'horse', |
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14: 'motorbike', 15: 'person', 16: 'pottedplant', |
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17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor' } |
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else: |
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assert(args.num_classes == 90) |
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net = cv.dnn.readNetFromTensorflow(args.weights, args.prototxt) |
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swapRB = True |
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classNames = { 0: 'background', |
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1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus', |
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7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant', |
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13: 'stop sign', 14: 'parking meter', 15: 'bench', 16: 'bird', 17: 'cat', |
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18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear', |
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24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag', |
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32: 'tie', 33: 'suitcase', 34: 'frisbee', 35: 'skis', 36: 'snowboard', |
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37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove', |
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41: 'skateboard', 42: 'surfboard', 43: 'tennis racket', 44: 'bottle', |
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46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon', |
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51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange', |
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56: 'broccoli', 57: 'carrot', 58: 'hot dog', 59: 'pizza', 60: 'donut', |
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61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed', |
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67: 'dining table', 70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse', |
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75: 'remote', 76: 'keyboard', 77: 'cell phone', 78: 'microwave', 79: 'oven', |
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80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock', |
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86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush' } |
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if args.video: |
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cap = cv.VideoCapture(args.video) |
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else: |
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cap = cv.VideoCapture(0) |
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while True: |
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# Capture frame-by-frame |
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ret, frame = cap.read() |
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blob = cv.dnn.blobFromImage(frame, inScaleFactor, (inWidth, inHeight), (meanVal, meanVal, meanVal), swapRB) |
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net.setInput(blob) |
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detections = net.forward() |
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cols = frame.shape[1] |
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rows = frame.shape[0] |
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if cols / float(rows) > WHRatio: |
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cropSize = (int(rows * WHRatio), rows) |
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else: |
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cropSize = (cols, int(cols / WHRatio)) |
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y1 = int((rows - cropSize[1]) / 2) |
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y2 = y1 + cropSize[1] |
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x1 = int((cols - cropSize[0]) / 2) |
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x2 = x1 + cropSize[0] |
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frame = frame[y1:y2, x1:x2] |
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cols = frame.shape[1] |
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rows = frame.shape[0] |
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for i in range(detections.shape[2]): |
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confidence = detections[0, 0, i, 2] |
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if confidence > args.thr: |
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class_id = int(detections[0, 0, i, 1]) |
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xLeftBottom = int(detections[0, 0, i, 3] * cols) |
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yLeftBottom = int(detections[0, 0, i, 4] * rows) |
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xRightTop = int(detections[0, 0, i, 5] * cols) |
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yRightTop = int(detections[0, 0, i, 6] * rows) |
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cv.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop), |
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(0, 255, 0)) |
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if class_id in classNames: |
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label = classNames[class_id] + ": " + str(confidence) |
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labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1) |
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yLeftBottom = max(yLeftBottom, labelSize[1]) |
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cv.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]), |
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(xLeftBottom + labelSize[0], yLeftBottom + baseLine), |
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(255, 255, 255), cv.FILLED) |
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cv.putText(frame, label, (xLeftBottom, yLeftBottom), |
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cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0)) |
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cv.imshow("detections", frame) |
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if cv.waitKey(1) >= 0: |
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break
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