def main():
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
# cap = cv2.VideoCapture(0)
vs = WebcamVideoStream(src=0).start()
fps = FPS().start() #Notes the start time
width = 440
with open("consumer_thread.csv", 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow([
"Thread Frame #",
"Time spent in reading the frame (seconds) from queue",
"Time spent performing inference on the frame (seconds)"
])
# loop over the frames from the video stream
#while True:
while fps._numFrames < args["num_frames"]:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
# Capture frame-by-frame
start = timer()
frame = vs.readFromQueue()
end = timer()
# if frame is not None then there was atleast one frame in queue
# when read from the queue and returned. Else queue was empty.
if frame is not None:
# update the FPS counter
fps.update()
consumerThreadFrameNumber = fps._numFrames
consumerThreadTimeTakenToReadThisFrame = (end - start)
print(
"[INFO] Consumer Thread : Time taken to read frame number",
consumerThreadFrameNumber, "from queue is",
consumerThreadTimeTakenToReadThisFrame, "seconds")
height = frame.shape[0]
dim = (width, height)
frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
# detect faces in the frame and determine if they are wearing a
# face mask or not
startInferenceTime = timer()
(locs, preds) = detect_and_predict_mask(frame, net, model)
endInferenceTime = timer()
consumerThreadTimeTakenToPerformInference = (
endInferenceTime - startInferenceTime)
print(
"[INFO] Consumer Thread : Time taken to performing inference on consumed frame number",
consumerThreadFrameNumber, "is",
consumerThreadTimeTakenToPerformInference, "seconds")
writer.writerow([
consumerThreadFrameNumber,
consumerThreadTimeTakenToReadThisFrame,
consumerThreadTimeTakenToPerformInference
])
for (box, pred) in zip(locs, preds):
# unpack the bounding box and predictions
(startX, startY, endX, endY) = box
(mask, withoutMask) = pred
label = "Mask" if mask > withoutMask else "No Mask"
color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
# include the probability in the label
#label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
# display the label and bounding box rectangle on the output
# frame
cv2.putText(frame, label, (startX, startY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), color,
2)
print("Showing frame")
# show the output frame
cv2.imshow("Output", frame)
#cv2.destroyAllWindows()
#key = cv2.waitKey(10) & 0xFF
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
fps.stop()
vs.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
cv2.destroyAllWindows()
with open("consumer_thread_ip_camera.csv", 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow([
"Thread Frame #",
"Time spent in reading the frame (seconds) from queue",
"Time spent performing inference on the frame (seconds)"
])
# loop over the frames from the video stream
#while True:
while fps._numFrames < args["num_frames"]:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
# Capture frame-by-frame
start = timer()
frame = vs.readFromQueue()
end = timer()
# if frame is not None then there was atleast one frame in queue
# when read from the queue and returned. Else queue was empty.
if frame is not None:
# update the FPS counter
fps.update()
consumerThreadFrameNumber = fps._numFrames
consumerThreadTimeTakenToReadThisFrame = (end - start)
print("[INFO] Consumer Thread : Time taken to read frame number",
consumerThreadFrameNumber, "from queue is",
consumerThreadTimeTakenToReadThisFrame, "seconds")
height = frame.shape[0]
dim = (width, height)
frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
# detect faces in the frame and determine if they are wearing a
