mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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88 lines
3.4 KiB
88 lines
3.4 KiB
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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classNames = ('background', |
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'aeroplane', 'bicycle', 'bird', 'boat', |
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'bottle', 'bus', 'car', 'cat', 'chair', |
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'cow', 'diningtable', 'dog', 'horse', |
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'motorbike', 'person', 'pottedplant', |
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'sheep', 'sofa', 'train', 'tvmonitor') |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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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 caffe prototxt") |
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parser.add_argument("-c", "--caffemodel", default="MobileNetSSD_deploy.caffemodel", |
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help="path to caffemodel file, download it here: " |
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"https://github.com/chuanqi305/MobileNet-SSD/") |
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parser.add_argument("--thr", default=0.2, help="confidence threshold to filter out weak detections") |
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args = parser.parse_args() |
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net = cv.dnn.readNetFromCaffe(args.prototxt, args.caffemodel) |
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if len(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) |
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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 = (rows - cropSize[1]) / 2 |
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y2 = y1 + cropSize[1] |
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x1 = (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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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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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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