mirror of
https://github.com/opencv/opencv.git
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88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
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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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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_300x300.prototxt",
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help="path to caffe prototxt")
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parser.add_argument("-c", "--caffemodel", help="path to caffemodel file, download it here: "
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"https://github.com/chuanqi305/MobileNet-SSD/blob/master/MobileNetSSD_train.caffemodel")
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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 = dnn.readNetFromCaffe(args.prototxt, args.caffemodel)
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if len(args.video):
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cap = cv2.VideoCapture(args.video)
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else:
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cap = cv2.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 = 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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cv2.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 = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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cv2.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]),
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(xLeftBottom + labelSize[0], yLeftBottom + baseLine),
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(255, 255, 255), cv2.FILLED)
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cv2.putText(frame, label, (xLeftBottom, yLeftBottom),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
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cv2.imshow("detections", frame)
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if cv2.waitKey(1) >= 0:
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break
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