cheating-detection / person_detection_video.py
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import cv2
import datetime
import imutils
import numpy as np
protopath = "MobileNetSSD_deploy.prototxt"
modelpath = "MobileNetSSD_deploy.caffemodel"
detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
# Only enable it if you are using OpenVino environment
# detector.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
# detector.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
def main():
cap = cv2.VideoCapture('test_video.mp4')
fps_start_time = datetime.datetime.now()
fps = 0
total_frames = 0
while True:
ret, frame = cap.read()
frame = imutils.resize(frame, width=600)
total_frames = total_frames + 1
(H, W) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
detector.setInput(blob)
person_detections = detector.forward()
for i in np.arange(0, person_detections.shape[2]):
confidence = person_detections[0, 0, i, 2]
if confidence > 0.5:
idx = int(person_detections[0, 0, i, 1])
if CLASSES[idx] != "person":
continue
person_box = person_detections[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = person_box.astype("int")
cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 0, 255), 2)
fps_end_time = datetime.datetime.now()
time_diff = fps_end_time - fps_start_time
if time_diff.seconds == 0:
fps = 0.0
else:
fps = (total_frames / time_diff.seconds)
fps_text = "FPS: {:.2f}".format(fps)
cv2.putText(frame, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
cv2.imshow("Application", frame)
key = cv2.waitKey(1)
if key == ord('q'):
break
cv2.destroyAllWindows()
main()