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- # USAGE
- # 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())
- # initialize the list of class labels MobileNet SSD was trained to
- # detect, then generate a set of bounding box colors for each class
- 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"])
- # initialize the video stream, allow the cammera sensor to warmup,
- # and initialize the FPS counter
- 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:
- # grab the frame from the threaded video stream and resize it
- # to have a maximum width of 400 pixels
- frame = vs.read()
- frame = imutils.resize(frame, width=400)
- # grab the frame dimensions and convert it to a blob
- (h, w) = frame.shape[:2]
- blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
- 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]
- # filter out weak detections by ensuring the `confidence` is
- # greater than the minimum confidence
- if confidence > args["confidence"]:
- # extract the index of the class label from the
- # `detections`, then compute the (x, y)-coordinates of
- # the bounding box for the object
- 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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