scripts/examples: Simplify blazeface and palm examples.

This commit is contained in:
Kwabena W. Agyeman 2025-11-02 12:31:01 -08:00
parent 75e16b5738
commit 3527c4d423
4 changed files with 52 additions and 66 deletions

View File

@ -26,20 +26,16 @@ while True:
img = csi0.snapshot()
# faces is a list of ((x, y, w, h), score, keypoints) tuples
faces = model.predict([img])
for r, score, keypoints in model.predict([img]):
ml.utils.draw_predictions(img, [r], ("face",), ((0, 0, 255),), format=None)
# Draw bounding boxes around the detected faces and keypoints.
if faces:
for r, score, keypoints in faces[0]:
ml.utils.draw_predictions(img, [r], ("face",), ((0, 0, 255),), format=None)
# keypoints is a ndarray of shape (6, 2)
# 0 - right eye (x, y)
# 1 - left eye (x, y)
# 2 - nose (x, y)
# 3 - mouth (x, y)
# 4 - right ear (x, y)
# 5 - left ear (x, y)
ml.utils.draw_keypoints(img, keypoints, color=(255, 0, 0))
# keypoints is a ndarray of shape (6, 2)
# 0 - right eye (x, y)
# 1 - left eye (x, y)
# 2 - nose (x, y)
# 3 - mouth (x, y)
# 4 - right ear (x, y)
# 5 - left ear (x, y)
ml.utils.draw_keypoints(img, keypoints, color=(255, 0, 0))
print(clock.fps(), "fps")

View File

@ -31,24 +31,20 @@ while True:
img = csi0.snapshot()
# palms is a list of ((x, y, w, h), score, keypoints) tuples
palms = model.predict([img])
for r, score, keypoints in model.predict([img]):
ml.utils.draw_predictions(img, [r], ("palm",), ((0, 0, 255),), format=None)
# 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))
# 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")

View File

@ -39,34 +39,31 @@ while True:
img = csi0.snapshot()
# palms is a list of ((x, y, w, h), score, keypoints) tuples
palms = palm_detection.predict([img])
for r, score, keypoints in palm_detection.predict([img]):
# 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)
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)])
# 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",), ((0, 0, 255),), format=None)
# 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",), ((0, 0, 255),), 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=(255, 0, 0), line_color=(0, 255, 0))
# 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=(255, 0, 0), line_color=(0, 255, 0))
print(clock.fps(), "fps")

View File

@ -43,14 +43,11 @@ while True:
if n is None:
# 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)
for r, score, keypoints in palm_detection.predict([img]):
# 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)
else:
# hands is a list of ((x, y, w, h), score, keypoints) tuples