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scripts/examples: Simplify blazeface and palm examples.
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@ -26,20 +26,16 @@ while True:
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img = csi0.snapshot()
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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 is a list of ((x, y, w, h), score, keypoints) tuples
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faces = model.predict([img])
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for r, score, keypoints in model.predict([img]):
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ml.utils.draw_predictions(img, [r], ("face",), ((0, 0, 255),), format=None)
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# Draw bounding boxes around the detected faces and keypoints.
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# keypoints is a ndarray of shape (6, 2)
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if faces:
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# 0 - right eye (x, y)
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for r, score, keypoints in faces[0]:
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# 1 - left eye (x, y)
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ml.utils.draw_predictions(img, [r], ("face",), ((0, 0, 255),), format=None)
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# 2 - nose (x, y)
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# 3 - mouth (x, y)
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# keypoints is a ndarray of shape (6, 2)
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# 4 - right ear (x, y)
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# 0 - right eye (x, y)
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# 5 - left ear (x, y)
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# 1 - left eye (x, y)
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ml.utils.draw_keypoints(img, keypoints, color=(255, 0, 0))
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# 2 - nose (x, y)
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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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ml.utils.draw_keypoints(img, keypoints, color=(255, 0, 0))
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print(clock.fps(), "fps")
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print(clock.fps(), "fps")
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@ -31,24 +31,20 @@ while True:
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img = csi0.snapshot()
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img = csi0.snapshot()
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# palms is a list of ((x, y, w, h), score, keypoints) tuples
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# palms is a list of ((x, y, w, h), score, keypoints) tuples
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palms = model.predict([img])
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for r, score, keypoints in model.predict([img]):
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ml.utils.draw_predictions(img, [r], ("palm",), ((0, 0, 255),), format=None)
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# Draw bounding boxes around the detected palms and keypoints.
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# keypoints is a ndarray of shape (7, 2)
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if palms:
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# 0 - wrist (x, y)
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for r, score, keypoints in palms[0]:
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# 1 - index finger mcp (x, y)
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ml.utils.draw_predictions(img, [r], ("palm",), ((0, 0, 255),), format=None)
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# 2 - middle finger mcp (x, y)
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# 3 - ring finger mcp (x, y)
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# keypoints is a ndarray of shape (7, 2)
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# 4 - pinky mcp (x, y)
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# 0 - wrist (x, y)
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# 5 - thumb cmc (x, y)
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# 1 - index finger mcp (x, y)
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# 6 - thumb mcp (x, y)
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# 2 - middle finger mcp (x, y)
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#
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# 3 - ring finger mcp (x, y)
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# mcp = Metacarpophalangeal Joint - the knuckle
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# 4 - pinky mcp (x, y)
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# cmc = Carpometacarpal Joint - the base of the thumb
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# 5 - thumb cmc (x, y)
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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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# 6 - thumb mcp (x, y)
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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=(255, 0, 0), line_color=(0, 255, 0))
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print(clock.fps(), "fps")
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print(clock.fps(), "fps")
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@ -39,34 +39,31 @@ while True:
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img = csi0.snapshot()
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img = csi0.snapshot()
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# palms is a list of ((x, y, w, h), score, keypoints) tuples
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# palms is a list of ((x, y, w, h), score, keypoints) tuples
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palms = palm_detection.predict([img])
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for r, score, keypoints in palm_detection.predict([img]):
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# rect is (x, y, w, h) - enlarge by 3x for hand landmarks model
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wider_rect = (r[0] - r[2], r[1] - r[3], r[2] * 3, r[3] * 3)
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# Operate on just the ROI of the detected palm
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n = Normalization(roi=wider_rect)
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if palms:
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# hands is a list of ((x, y, w, h), score, keypoints) tuples
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for r, score, keypoints in palms[0]:
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# index 0 (if present) is left hand
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# rect is (x, y, w, h) - enlarge by 3x for hand landmarks model
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# index 1 (if present) is right hand
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wider_rect = (r[0] - r[2], r[1] - r[3], r[2] * 3, r[3] * 3)
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hands = hand_landmarks.predict([n(img)])
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# Operate on just the ROI of the detected palm
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n = Normalization(roi=wider_rect)
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# hands is a list of ((x, y, w, h), score, keypoints) tuples
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# Draw bounding boxes around the detected hands and keypoints.
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# index 0 (if present) is left hand
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for i, detections in enumerate(hands):
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# index 1 (if present) is right hand
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for r, score, keypoints in detections:
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hands = hand_landmarks.predict([n(img)])
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ml.utils.draw_predictions(img, [r], ("right",) if i else ("left",), ((0, 0, 255),), format=None)
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# Draw bounding boxes around the detected hands and keypoints.
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# keypoints: ndarray (21, 3) of hand joints (x, y, z)
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for i, detections in enumerate(hands):
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# Indices follow MediaPipe convention:
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for r, score, keypoints in detections:
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# 0: wrist
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ml.utils.draw_predictions(img, [r], ("right",) if i else ("left",), ((0, 0, 255),), format=None)
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# Thumb: 1 cmc, 2 mcp, 3 ip, 4 tip
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# Index: 5 mcp, 6 pip, 7 dip, 8 tip
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# keypoints: ndarray (21, 3) of hand joints (x, y, z)
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# Middle: 9 mcp, 10 pip, 11 dip, 12 tip
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# Indices follow MediaPipe convention:
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# Ring: 13 mcp, 14 pip, 15 dip, 16 tip
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# 0: wrist
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# Pinky: 17 mcp, 18 pip, 19 dip, 20 tip
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# Thumb: 1 cmc, 2 mcp, 3 ip, 4 tip
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# (cmc=base, mcp=knuckle, pip=mid, dip=distal, ip=thumb joint, tip=fingertip)
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# Index: 5 mcp, 6 pip, 7 dip, 8 tip
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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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# Middle: 9 mcp, 10 pip, 11 dip, 12 tip
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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=(255, 0, 0), line_color=(0, 255, 0))
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print(clock.fps(), "fps")
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print(clock.fps(), "fps")
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@ -43,14 +43,11 @@ while True:
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if n is None:
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if n is None:
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# palms is a list of ((x, y, w, h), score, keypoints) tuples
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# palms is a list of ((x, y, w, h), score, keypoints) tuples
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palms = palm_detection.predict([img])
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for r, score, keypoints in palm_detection.predict([img]):
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# rect is (x, y, w, h) - enlarge by 3x for hand landmarks model
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if palms:
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wider_rect = (r[0] - r[2], r[1] - r[3], r[2] * 3, r[3] * 3)
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for r, score, keypoints in palms[0]:
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# Operate on just the ROI of the detected palm
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# rect is (x, y, w, h) - enlarge by 3x for hand landmarks model
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n = Normalization(roi=wider_rect)
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wider_rect = (r[0] - r[2], r[1] - r[3], r[2] * 3, r[3] * 3)
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# Operate on just the ROI of the detected palm
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n = Normalization(roi=wider_rect)
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else:
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else:
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# hands is a list of ((x, y, w, h), score, keypoints) tuples
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# hands is a list of ((x, y, w, h), score, keypoints) tuples
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