# This work is licensed under the MIT license. # Copyright (c) 2013-2025 OpenMV LLC. All rights reserved. # https://github.com/openmv/openmv/blob/master/LICENSE # # This example shows off Google's MediaPipe Palm Detection model. # # NOTE: This exaxmple requires an OpenMV Cam with an NPU like the AE3 or N6 to run real-time. import csi import time import ml from ml.postprocessing.mediapipe import BlazePalm # Initialize the sensor. csi0 = csi.CSI() csi0.reset() csi0.pixformat(csi.RGB565) csi0.framesize(csi.VGA) csi0.window((400, 400)) # Load built-in palm detection model model = ml.Model("/rom/palm_detection_full_192.tflite", postprocess=BlazePalm(threshold=0.4)) print(model) # Line connections between hand joints for drawing the hand skeleton. palm_lines = ((0, 1), (1, 2), (2, 3), (3, 4), (4, 0), (0, 5), (5, 6)) clock = time.clock() while True: clock.tick() img = csi0.snapshot() # palms is a list of ((x, y, w, h), score, keypoints) tuples palms = model.predict([img]) # Draw bounding boxes around the detected palms and keypoints. if palms: for r, score, keypoints in palms[0]: ml.utils.draw_predictions(img, [r], ("palm",), ((0, 0, 255),), format=None) # keypoints is a ndarray of shape (7, 2) # 0 - wrist (x, y) # 1 - index finger mcp (x, y) # 2 - middle finger mcp (x, y) # 3 - ring finger mcp (x, y) # 4 - pinky mcp (x, y) # 5 - thumb cmc (x, y) # 6 - thumb mcp (x, y) # # mcp = Metacarpophalangeal Joint - the knuckle # cmc = Carpometacarpal Joint - the base of the thumb ml.utils.draw_skeleton(img, keypoints, palm_lines, kp_color=(255, 0, 0), line_color=(0, 255, 0)) print(clock.fps(), "fps")