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# python real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel # import the necessary packages from imutils.video import VideoStream from imutils.video import FPS import numpy as np import argparse import imutils import time import cv2 # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-p", "--prototxt", required=True, help="path to Caffe 'deploy' prototxt file") ap.add_argument("-m", "--model", required=True, help="path to Caffe pre-trained model") ap.add_argument("-c", "--confidence", type=float, default=0.2, help="minimum probability to filter weak detections") args = vars(ap.parse_args()) CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3)) # load our serialized model from disk print("[INFO] loading model...") net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"]) print("[INFO] starting video stream...") vs = VideoStream(src=0).start() time.sleep(2.0) fps = FPS().start() # loop over the frames from the video stream while True: frame = vs.read() frame = imutils.resize(frame, width=400) (h, w) = frame.shape[:2] blob = cv2.dnn.blobFromImage(cv2.resize(frame, (600, 600)), 0.007843, (300, 300), 127.5) # pass the blob through the network and obtain the detections and # predictions net.setInput(blob) detections = net.forward() # loop over the detections for i in np.arange(0, detections.shape[2]): # extract the confidence (i.e., probability) associated with # the prediction confidence = detections[0, 0, i, 2] if confidence > args["confidence"]: idx = int(detections[0, 0, i, 1]) box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # draw the prediction on the frame label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100) cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2) y = startY - 15 if startY - 15 > 15 else startY + 15 cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2) # show the output frame cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF # if the `q` key was pressed, break from the loop if key == ord("q"): break # update the FPS counter fps.update() # stop the timer and display FPS information fps.stop() print("[INFO] elapsed time: {:.2f}".format(fps.elapsed())) print("[INFO] approx. FPS: {:.2f}".format(fps.fps())) # do a bit of cleanup cv2.destroyAllWindows() vs.stop()
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