In this video lesson we introduce you to MediaPipe. The OS we had you flash in LESSON 1 already has the MediaPipe framework installed, and all the needed and working dependencies. If you have installed that OS and not modified it, this and future lessons will work. If you find dependency errors, you might need to reflash the original OS.
MediaPipe is a free, open-source framework developed by Google that makes it much easier to add advanced computer vision and AI features to your Python programs. It is especially popular among developers who use OpenCV because it works seamlessly with it and delivers excellent real-time performance, even on devices like the Raspberry Pi 5.
With MediaPipe, you can quickly add powerful capabilities such as face detection, face mesh (detailed facial landmarks), hand tracking, body pose estimation, and more — all without having to write complex deep learning code from scratch. It comes with pre-trained machine learning models that are optimized for speed, allowing your programs to run smoothly at 30 frames per second or higher.
The biggest advantage for Python + OpenCV users is its simplicity. You capture video frames using OpenCV or picamera2, pass them to MediaPipe for processing, and then draw the results (such as bounding boxes or landmarks) back onto your image using normal OpenCV functions. This combination gives developers a fast and straightforward way to build interactive computer vision projects like face trackers, gesture-controlled robots, or smart camera applications.
In short, MediaPipe acts as a powerful, easy-to-use toolkit that bridges the gap between OpenCV and modern AI vision technology.
We introduce you to MediaPipe using a simple example where we create a faceFinder on our Raspberry Pi 5.
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import cv2 import time from picamera2 import Picamera2 import mediapipe as mp piCam = Picamera2() W=1280 H=720 tStart = time.time() fps = 0 RES = (W,H) piCam.preview_configuration.main.size = RES piCam.preview_configuration.main.format = "RGB888" piCam.preview_configuration.controls.FrameRate=60 piCam.preview_configuration.align() piCam.configure("preview") piCam.start() textLowerLeft = (int(W*.01),int(H*.05)) fontFace = cv2.FONT_HERSHEY_SIMPLEX fontThickness = int(W/425) fontScale = H*.0015 fontColor = (0,0,255) faceFinder = mp.solutions.face_detection.FaceDetection( model_selection = 0, min_detection_confidence = .5) mpDraw = mp.solutions.drawing_utils while True: deltaT = time.time() - tStart tStart=time.time() fps = fps*.95 + (1/deltaT)*.05 frame= piCam.capture_array() #frame=cv2.flip(frame,-1) rgbFrame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB) results = faceFinder.process(rgbFrame) if results.detections: for detection in results.detections: mpDraw.draw_detection(frame,detection) myText = "FPS: "+str(round(fps,1)) cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness) cv2.imshow("Camera", frame) cv2.moveWindow("Camera",0,60) if cv2.waitKey(1)==ord('q'): break cv2.destroyAllWindows() print('Program Terminated') |