openmv/scripts/examples/03-Machine-Learning/00-TensorFlow/hand_landmarks_multi_hand.py
2025-10-18 10:20:33 -07:00

78 lines
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Python

# 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 Hand Landmarks Detection model for multiple hands.
#
# 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.preprocessing import Normalization
from ml.postprocessing.mediapipe import BlazePalm
from ml.postprocessing.mediapipe import HandLandmarks
# 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
palm_detection = ml.Model("/rom/palm_detection_full_192.tflite", postprocess=BlazePalm(threshold=0.4))
print(palm_detection)
# Load built-in hand landmark model
hand_landmarks = ml.Model("/rom/hand_landmarks_full_224.tflite", postprocess=HandLandmarks(threshold=0.4))
print(hand_landmarks)
# Line connections between hand joints for drawing the hand skeleton.
hand_lines = ((0, 1), (1, 2), (2, 3), (3, 4), (0, 5), (5, 6), (6, 7), (7, 8),
(5, 9), (9, 10), (10, 11), (11, 12), (9, 13), (13, 14), (14, 15), (15, 16),
(13, 17), (17, 18), (18, 19), (19, 20), (0, 17))
# Visualization parameters.
palm_colors = [(0, 0, 255)]
kp_color = (255, 0, 0)
line_color = (0, 255, 0)
clock = time.clock()
while True:
clock.tick()
img = csi0.snapshot()
# palms is a list of ((x, y, w, h), score, keypoints) tuples
palms = palm_detection.predict([img])
if palms:
for r, score, keypoints in palms[0]:
# rect is (x, y, w, h) - enlarge by 3x for hand landmarks model
wider_rect = (r[0] - r[2], r[1] - r[3], r[2] * 3, r[3] * 3)
# Operate on just the ROI of the detected palm
n = Normalization(roi=wider_rect)
# hands is a list of ((x, y, w, h), score, keypoints) tuples
# index 0 (if present) is left hand
# index 1 (if present) is right hand
hands = hand_landmarks.predict([n(img)])
# Draw bounding boxes around the detected hands and keypoints.
for i, detections in enumerate(hands):
for r, score, keypoints in detections:
ml.utils.draw_predictions(img, [r], ["right" if i else "left"], palm_colors, format=None)
# keypoints: ndarray (21, 3) of hand joints (x, y, z)
# Indices follow MediaPipe convention:
# 0: wrist
# Thumb: 1 cmc, 2 mcp, 3 ip, 4 tip
# Index: 5 mcp, 6 pip, 7 dip, 8 tip
# Middle: 9 mcp, 10 pip, 11 dip, 12 tip
# Ring: 13 mcp, 14 pip, 15 dip, 16 tip
# Pinky: 17 mcp, 18 pip, 19 dip, 20 tip
# (cmc=base, mcp=knuckle, pip=mid, dip=distal, ip=thumb joint, tip=fingertip)
ml.utils.draw_skeleton(img, keypoints, hand_lines, kp_color=kp_color, line_color=line_color)
print(clock.fps(), "fps")