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167 lines
5.0 KiB
167 lines
5.0 KiB
"""USAGE |
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python cv_detector.py \ |
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--prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt \ |
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--model mobilenet_ssd/MobileNetSSD_deploy.caffemodel --video ~/Desktop/5min.mp4 |
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""" |
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# import the necessary packages |
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from imutils.video import FPS |
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import numpy as np |
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import argparse |
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import imutils |
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import dlib |
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import cv2 |
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from PIL import Image |
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# construct the argument parser and parse the arguments |
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ap = argparse.ArgumentParser() |
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ap.add_argument("-p", "--prototxt", required=True, |
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help="path to Caffe 'deploy' prototxt file") |
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ap.add_argument("-m", "--model", required=True, |
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help="path to Caffe pre-trained model") |
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ap.add_argument("-v", "--video", required=True, |
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help="path to input video file") |
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ap.add_argument("-o", "--output", type=str, |
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help="path to optional output video file") |
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ap.add_argument("-c", "--confidence", type=float, default=0.2, |
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help="minimum probability to filter weak detections") |
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args = vars(ap.parse_args()) |
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# initialize the list of class labels MobileNet SSD was trained to |
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# detect |
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CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", |
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"bottle", "bus", "car", "cat", "chair", "cow", "diningtable", |
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"dog", "horse", "motorbike", "person", "pottedplant", "sheep", |
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"sofa", "train", "tvmonitor"] |
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# load our serialized model from disk |
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print("[INFO] loading model...") |
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net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"]) |
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# initialize the video stream and output video writer |
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print("[INFO] starting video stream...") |
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vs = cv2.VideoCapture(args["video"]) |
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_fps = vs.get(cv2.CAP_PROP_FPS) |
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writer = None |
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# initialize the list of object trackers and corresponding class |
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# labels |
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# trackers = [] |
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labels = [] |
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# start the frames per second throughput estimator |
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fps = FPS().start() |
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frame_count = 0 |
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# loop over frames from the video file stream |
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while True: |
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# grab the next frame from the video file |
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(grabbed, frame) = vs.read() |
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frame_count += 1 |
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_duration = frame_count / _fps |
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# check to see if we have reached the end of the video file |
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if frame is None: |
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break |
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""" |
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ENTRANCE_1: from hospital |
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""" |
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# 45. 325 == 164, 509 |
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cropped_frame = frame[325:509, 45:164] |
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# cropped_frame.save("test.jpg") |
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cv2.imwrite('test.jpg', cropped_frame) |
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frame = cropped_frame |
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# resize the frame for faster processing and then convert the |
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# frame from BGR to RGB ordering (dlib needs RGB ordering) |
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# frame = imutils.resize(frame, width=600) |
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# rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
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# grab the frame dimensions and convert the frame to a blob |
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(h, w) = frame.shape[:2] |
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blob = cv2.dnn.blobFromImage(frame, 0.007843, (w, h), 127.5) |
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# pass the blob through the network and obtain the detections |
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# and predictions |
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net.setInput(blob) |
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detections = net.forward() |
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# loop over the detections |
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for i in np.arange(0, detections.shape[2]): |
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# extract the confidence (i.e., probability) associated |
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# with the prediction |
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confidence = detections[0, 0, i, 2] |
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# filter out weak detections by requiring a minimum |
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# confidence |
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if confidence > args["confidence"]: |
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# extract the index of the class label from the |
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# detections list |
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idx = int(detections[0, 0, i, 1]) |
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label = CLASSES[idx] |
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DROP = ["diningtable", "chair", "aeroplane"] |
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if label in DROP: |
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continue |
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# if the class label is not a person, ignore it |
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# if CLASSES[idx] != "person": |
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# continue |
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# compute the (x, y)-coordinates of the bounding box |
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# for the object |
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box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) |
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(startX, startY, endX, endY) = box.astype("int") |
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print(f"[{_duration:0.02f}] label: {label} (x,y) = ({startX}, {startY})") |
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# construct a dlib rectangle object from the bounding |
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# box coordinates and start the correlation tracker |
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# t = dlib.correlation_tracker() |
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# rect = dlib.rectangle(startX, startY, endX, endY) |
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# t.start_track(rgb, rect) |
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# update our set of trackers and corresponding class |
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# labels |
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labels.append(label) |
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# grab the corresponding class label for the detection |
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# and draw the bounding box |
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cv2.rectangle(frame, (startX, startY), (endX, endY), |
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(0, 255, 0), 2) |
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cv2.putText(frame, label, (startX, startY - 15), |
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cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2) |
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# show the output frame |
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cv2.imshow("Frame", frame) |
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key = cv2.waitKey(1) & 0xFF |
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# if the `q` key was pressed, break from the loop |
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if key == ord("q"): |
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break |
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# update the FPS counter |
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# fps.update() |
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# stop the timer and display FPS information |
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fps.stop() |
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print("[INFO] elapsed time: {:.2f}".format(fps.elapsed())) |
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print("[INFO] approx. FPS: {:.2f}".format(fps.fps())) |
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# do a bit of cleanup |
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cv2.destroyAllWindows() |
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vs.release()
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