El Bruno

Hi!

And after yesterday’s post I realize that the code is working, but there is room for performance improvement. So, I went back to my 1st sample, the one for face detection and I added some code to get some times for Frames per Second (FPS).

In my initial code, the app was working processing almost 6 FPS. Then I started to read the code and think on improvements and I manage to get an amazing +30FPS.

So, before moving forward, I want to remark this StackOverflow post that quickly pointed me in the easiest way to do a StopWatch in Python.

My original code, was this one:

	# standard face detection sample with FPS in console
	import face_recognition
	import cv2
	import time
	video_capture = cv2.VideoCapture(0)
	while True:
	start_time = time.time()
	ret, frame = video_capture.read()
	rgb_frame = frame[:, :, ::-1]
	face_locations = face_recognition.face_locations(rgb_frame)
	for top, right, bottom, left in face_locations:
	cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
	cv2.imshow('Video', frame)
	print("FPS: ", 1.0 / (time.time() – start_time)) # FPS = 1 / time to process loop
	if cv2.waitKey(1) & 0xFF == ord('q'):
	break
	video_capture.release()
	cv2.destroyAllWindows()

view raw FaceDetectionNormalDisplayPFS.py hosted with ❤ by GitHub

And then, I realize that I may use some of the OpenCV functions to increase the face detection process. I really don’t need to process a full HD image (1920 x 1080), I may resize the frame to a quarter size and work with this. That’s how, based on some of the samples, I got the following code:

	# fast face detection sample with FPS in console
	import face_recognition
	import cv2
	import time
	video_capture = cv2.VideoCapture(0)
	while True:
	start_time = time.time()
	ret, frame = video_capture.read()
	fast_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
	rgb_frame = fast_frame[:, :, ::-1]
	face_locations = face_recognition.face_locations(rgb_frame)
	for top, right, bottom, left in face_locations:
	top *= 4
	right *= 4
	bottom *= 4
	left *= 4
	cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
	cv2.imshow('Video', frame)
	print("FPS: ", 1.0 / (time.time() – start_time)) # FPS = 1 / time to process loop
	if cv2.waitKey(1) & 0xFF == ord('q'):
	break
	video_capture.release()
	cv2.destroyAllWindows()

view raw FaceDetectionFastDisplayPFS.py hosted with ❤ by GitHub

The line 12 perform the initial resize and then I recalculate back the positions before drawing the face frame. This process works almost 6 times faster than the original one.

I’ll continue improving the code and samples, and sharing my learning path !

Happy Coding!

Greetings @ Burlington

El Bruno

Resources

• Anaconda Distribution, https://www.anaconda.com/distribution/
• Visual Studio Code, https://code.visualstudio.com/
• Adam Geitgey, https://github.com/ageitgey/face_recognition
• StackOverflow, fps – how to divide count by time function to determine fps