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import cv2 | |
import datetime | |
import imutils | |
import numpy as np | |
protopath = "deploy.prototxt" | |
modelpath = "res10_300x300_ssd_iter_140000.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) | |
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] | |
face_blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0), False, False) | |
detector.setInput(face_blob) | |
face_detections = detector.forward() | |
for i in np.arange(0, face_detections.shape[2]): | |
confidence = face_detections[0, 0, i, 2] | |
if confidence > 0.5: | |
face_box = face_detections[0, 0, i, 3:7] * np.array([W, H, W, H]) | |
(startX, startY, endX, endY) = face_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() | |