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334b0e97d2
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@ -2,12 +2,12 @@
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# Copyright (c) 2013-2025 OpenMV LLC. All rights reserved.
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# https://github.com/openmv/openmv/blob/master/LICENSE
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#
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# This example shows off Google's MediaPipe BlazeFace face detection model.
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# This example shows off Google's MediaPipe Face Detection model.
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import csi
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import time
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import ml
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from ml.postprocessing import mediapipe_face_detection_postprocess
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from ml.postprocessing.mediapipe import BlazeFace
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# Initialize the sensor.
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csi0 = csi.CSI()
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@ -17,25 +17,22 @@ csi0.framesize(csi.VGA)
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csi0.window((400, 400))
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# Load built-in face detection model
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model = ml.Model("/rom/blazeface_front_128.tflite")
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model = ml.Model("/rom/blazeface_front_128.tflite", postprocess=BlazeFace(threshold=0.4))
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print(model)
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# Create the face detection post-processor. This post-processor dynamically
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# generates anchors for the model input size which should only be done once.
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face_detection_postprocess = mediapipe_face_detection_postprocess(threshold=0.6)
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clock = time.clock()
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while True:
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clock.tick()
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img = csi0.snapshot()
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# faces is a list of ((x, y, w, h), score, keypoints) tuples
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faces = model.predict([img], callback=face_detection_postprocess)
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faces = model.predict([img])
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# Draw bounding boxes around the detected faces and keypoints.
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if faces:
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for r, score, keypoints in faces[0]:
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ml.utils.draw_predictions(img, [r], ["face"], [(0, 0, 255)], format=None)
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ml.utils.draw_predictions(img, [r], ("face",), ((0, 0, 255),), format=None)
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# keypoints is a ndarray of shape (6, 2)
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# 0 - right eye (x, y)
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# 1 - left eye (x, y)
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@ -43,7 +40,6 @@ while True:
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# 3 - mouth (x, y)
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# 4 - right ear (x, y)
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# 5 - left ear (x, y)
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for kp in keypoints.tolist():
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img.draw_circle(int(kp[0]), int(kp[1]), 4, color=(255, 0, 0))
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ml.utils.draw_keypoints(img, keypoints, color=(255, 0, 0))
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print(clock.fps(), "fps")
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@ -25,12 +25,6 @@ print(model)
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# Line connections between hand joints for drawing the hand skeleton.
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palm_lines = ((0, 1), (1, 2), (2, 3), (3, 4), (4, 0), (0, 5), (5, 6))
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# Visualization parameters.
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palm_labels = ["palm"]
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palm_colors = [(0, 0, 255)]
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kp_color = (255, 0, 0)
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line_color = (0, 255, 0)
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clock = time.clock()
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while True:
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clock.tick()
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@ -42,7 +36,7 @@ while True:
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# Draw bounding boxes around the detected palms and keypoints.
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if palms:
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for r, score, keypoints in palms[0]:
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ml.utils.draw_predictions(img, [r], palm_labels, palm_colors, format=None)
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ml.utils.draw_predictions(img, [r], ("palm",), ((0, 0, 255),), format=None)
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# keypoints is a ndarray of shape (7, 2)
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# 0 - wrist (x, y)
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@ -55,6 +49,6 @@ while True:
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#
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# mcp = Metacarpophalangeal Joint - the knuckle
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# cmc = Carpometacarpal Joint - the base of the thumb
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ml.utils.draw_skeleton(img, keypoints, palm_lines, kp_color=kp_color, line_color=line_color)
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ml.utils.draw_skeleton(img, keypoints, palm_lines, kp_color=(255, 0, 0), line_color=(0, 255, 0))
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print(clock.fps(), "fps")
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@ -33,11 +33,6 @@ hand_lines = ((0, 1), (1, 2), (2, 3), (3, 4), (0, 5), (5, 6), (6, 7), (7, 8),
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(5, 9), (9, 10), (10, 11), (11, 12), (9, 13), (13, 14), (14, 15), (15, 16),
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(13, 17), (17, 18), (18, 19), (19, 20), (0, 17))
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# Visualization parameters.
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palm_colors = [(0, 0, 255)]
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kp_color = (255, 0, 0)
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line_color = (0, 255, 0)
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clock = time.clock()
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while True:
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clock.tick()
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@ -61,7 +56,7 @@ while True:
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# Draw bounding boxes around the detected hands and keypoints.
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for i, detections in enumerate(hands):
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for r, score, keypoints in detections:
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ml.utils.draw_predictions(img, [r], ["right" if i else "left"], palm_colors, format=None)
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ml.utils.draw_predictions(img, [r], ("right",) if i else ("left",), ((0, 0, 255),), format=None)
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# keypoints: ndarray (21, 3) of hand joints (x, y, z)
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# Indices follow MediaPipe convention:
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@ -72,6 +67,6 @@ while True:
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# Ring: 13 mcp, 14 pip, 15 dip, 16 tip
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# Pinky: 17 mcp, 18 pip, 19 dip, 20 tip
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# (cmc=base, mcp=knuckle, pip=mid, dip=distal, ip=thumb joint, tip=fingertip)
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ml.utils.draw_skeleton(img, keypoints, hand_lines, kp_color=kp_color, line_color=line_color)
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ml.utils.draw_skeleton(img, keypoints, hand_lines, kp_color=(255, 0, 0), line_color=(0, 255, 0))
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print(clock.fps(), "fps")
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@ -33,11 +33,6 @@ hand_lines = ((0, 1), (1, 2), (2, 3), (3, 4), (0, 5), (5, 6), (6, 7), (7, 8),
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(5, 9), (9, 10), (10, 11), (11, 12), (9, 13), (13, 14), (14, 15), (15, 16),
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(13, 17), (17, 18), (18, 19), (19, 20), (0, 17))
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# Visualization parameters.
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palm_colors = [(0, 0, 255)]
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kp_color = (255, 0, 0)
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line_color = (0, 255, 0)
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# Tracking vars.
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n = None
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@ -71,7 +66,7 @@ while True:
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# Draw bounding boxes around the detected hands and keypoints.
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for i, detections in enumerate(hands):
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for r, score, keypoints in detections:
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ml.utils.draw_predictions(img, [r], ["right" if i else "left"], [(0, 0, 255)], format=None)
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ml.utils.draw_predictions(img, [r], ("right",) if i else ("left",), ((0, 0, 255),), format=None)
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# keypoints: ndarray (21, 3) of hand joints (x, y, z)
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# Indices follow MediaPipe convention:
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@ -82,7 +77,7 @@ while True:
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# Ring: 13 mcp, 14 pip, 15 dip, 16 tip
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# Pinky: 17 mcp, 18 pip, 19 dip, 20 tip
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# (cmc=base, mcp=knuckle, pip=mid, dip=distal, ip=thumb joint, tip=fingertip)
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ml.utils.draw_skeleton(img, keypoints, hand_lines, kp_color=kp_color, line_color=line_color)
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ml.utils.draw_skeleton(img, keypoints, hand_lines, kp_color=(255, 0, 0), line_color=(0, 255, 0))
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# Center new_wider_rect on hand for tracking
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new_wider_rect = (r[0] + (r[2] // 2) - (wider_rect[2] // 2),
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@ -9,7 +9,7 @@
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import sensor
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import time
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import ml
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from ml.postprocessing import fomo_postprocess
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from ml.postprocessing.edgeimpulse import Fomo
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import math
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sensor.reset() # Reset and initialize the sensor.
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@ -19,7 +19,7 @@ sensor.set_windowing((240, 240)) # Set 240x240 window.
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sensor.skip_frames(time=2000) # Let the camera adjust.
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# Load built-in FOMO face detection model
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model = ml.Model("/rom/fomo_face_detection.tflite")
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model = ml.Model("/rom/fomo_face_detection.tflite", postprocess=Fomo(threshold=0.4))
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print(model)
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# Alternatively, models can be loaded from the filesystem storage.
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@ -39,10 +39,9 @@ colors = [ # Add more colors if you are detecting more than 7 types of classes
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clock = time.clock()
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while True:
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clock.tick()
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img = sensor.snapshot()
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for i, detection_list in enumerate(model.predict([img], callback=fomo_postprocess())):
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for i, detection_list in enumerate(model.predict([img])):
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if i == 0:
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continue # background class
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if len(detection_list) == 0:
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