Spaces:
Build error
Build error
File size: 8,855 Bytes
d7a991a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 |
"""
Render OpenPose keypoints.
Code was ported to Python from the official C++ implementation https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/utilities/keypoint.cpp
"""
import cv2
import math
import numpy as np
from typing import List, Tuple
def get_keypoints_rectangle(keypoints: np.array, threshold: float) -> Tuple[float, float, float]:
"""
Compute rectangle enclosing keypoints above the threshold.
Args:
keypoints (np.array): Keypoint array of shape (N, 3).
threshold (float): Confidence visualization threshold.
Returns:
Tuple[float, float, float]: Rectangle width, height and area.
"""
valid_ind = keypoints[:, -1] > threshold
if valid_ind.sum() > 0:
valid_keypoints = keypoints[valid_ind][:, :-1]
max_x = valid_keypoints[:,0].max()
max_y = valid_keypoints[:,1].max()
min_x = valid_keypoints[:,0].min()
min_y = valid_keypoints[:,1].min()
width = max_x - min_x
height = max_y - min_y
area = width * height
return width, height, area
else:
return 0,0,0
def render_keypoints(img: np.array,
keypoints: np.array,
pairs: List,
colors: List,
thickness_circle_ratio: float,
thickness_line_ratio_wrt_circle: float,
pose_scales: List,
threshold: float = 0.1,
alpha: float = 1.0) -> np.array:
"""
Render keypoints on input image.
Args:
img (np.array): Input image of shape (H, W, 3) with pixel values in the [0,255] range.
keypoints (np.array): Keypoint array of shape (N, 3).
pairs (List): List of keypoint pairs per limb.
colors: (List): List of colors per keypoint.
thickness_circle_ratio (float): Circle thickness ratio.
thickness_line_ratio_wrt_circle (float): Line thickness ratio wrt the circle.
pose_scales (List): List of pose scales.
threshold (float): Only visualize keypoints with confidence above the threshold.
Returns:
(np.array): Image of shape (H, W, 3) with keypoints drawn on top of the original image.
"""
img_orig = img.copy()
width, height = img.shape[1], img.shape[2]
area = width * height
lineType = 8
shift = 0
numberColors = len(colors)
thresholdRectangle = 0.1
person_width, person_height, person_area = get_keypoints_rectangle(keypoints, thresholdRectangle)
if person_area > 0:
ratioAreas = min(1, max(person_width / width, person_height / height))
thicknessRatio = np.maximum(np.round(math.sqrt(area) * thickness_circle_ratio * ratioAreas), 2)
thicknessCircle = np.maximum(1, thicknessRatio if ratioAreas > 0.05 else -np.ones_like(thicknessRatio))
thicknessLine = np.maximum(1, np.round(thicknessRatio * thickness_line_ratio_wrt_circle))
radius = thicknessRatio / 2
img = np.ascontiguousarray(img.copy())
for i, pair in enumerate(pairs):
index1, index2 = pair
if keypoints[index1, -1] > threshold and keypoints[index2, -1] > threshold:
thicknessLineScaled = int(round(min(thicknessLine[index1], thicknessLine[index2]) * pose_scales[0]))
colorIndex = index2
color = colors[colorIndex % numberColors]
keypoint1 = keypoints[index1, :-1].astype(np.int)
keypoint2 = keypoints[index2, :-1].astype(np.int)
cv2.line(img, tuple(keypoint1.tolist()), tuple(keypoint2.tolist()), tuple(color.tolist()), thicknessLineScaled, lineType, shift)
for part in range(len(keypoints)):
faceIndex = part
if keypoints[faceIndex, -1] > threshold:
radiusScaled = int(round(radius[faceIndex] * pose_scales[0]))
thicknessCircleScaled = int(round(thicknessCircle[faceIndex] * pose_scales[0]))
colorIndex = part
color = colors[colorIndex % numberColors]
center = keypoints[faceIndex, :-1].astype(np.int)
cv2.circle(img, tuple(center.tolist()), radiusScaled, tuple(color.tolist()), thicknessCircleScaled, lineType, shift)
return img
def render_hand_keypoints(img, right_hand_keypoints, threshold=0.1, use_confidence=False, map_fn=lambda x: np.ones_like(x), alpha=1.0):
if use_confidence and map_fn is not None:
#thicknessCircleRatioLeft = 1./50 * map_fn(left_hand_keypoints[:, -1])
thicknessCircleRatioRight = 1./50 * map_fn(right_hand_keypoints[:, -1])
else:
#thicknessCircleRatioLeft = 1./50 * np.ones(left_hand_keypoints.shape[0])
thicknessCircleRatioRight = 1./50 * np.ones(right_hand_keypoints.shape[0])
thicknessLineRatioWRTCircle = 0.75
pairs = [0,1, 1,2, 2,3, 3,4, 0,5, 5,6, 6,7, 7,8, 0,9, 9,10, 10,11, 11,12, 0,13, 13,14, 14,15, 15,16, 0,17, 17,18, 18,19, 19,20]
pairs = np.array(pairs).reshape(-1,2)
colors = [100., 100., 100.,
100., 0., 0.,
150., 0., 0.,
200., 0., 0.,
255., 0., 0.,
100., 100., 0.,
150., 150., 0.,
200., 200., 0.,
255., 255., 0.,
0., 100., 50.,
0., 150., 75.,
0., 200., 100.,
0., 255., 125.,
0., 50., 100.,
0., 75., 150.,
0., 100., 200.,
0., 125., 255.,
100., 0., 100.,
150., 0., 150.,
200., 0., 200.,
255., 0., 255.]
colors = np.array(colors).reshape(-1,3)
#colors = np.zeros_like(colors)
poseScales = [1]
#img = render_keypoints(img, left_hand_keypoints, pairs, colors, thicknessCircleRatioLeft, thicknessLineRatioWRTCircle, poseScales, threshold, alpha=alpha)
img = render_keypoints(img, right_hand_keypoints, pairs, colors, thicknessCircleRatioRight, thicknessLineRatioWRTCircle, poseScales, threshold, alpha=alpha)
#img = render_keypoints(img, right_hand_keypoints, pairs, colors, thickness_circle_ratio, thickness_line_ratio_wrt_circle, pose_scales, 0.1)
return img
def render_body_keypoints(img: np.array,
body_keypoints: np.array) -> np.array:
"""
Render OpenPose body keypoints on input image.
Args:
img (np.array): Input image of shape (H, W, 3) with pixel values in the [0,255] range.
body_keypoints (np.array): Keypoint array of shape (N, 3); 3 <====> (x, y, confidence).
Returns:
(np.array): Image of shape (H, W, 3) with keypoints drawn on top of the original image.
"""
thickness_circle_ratio = 1./75. * np.ones(body_keypoints.shape[0])
thickness_line_ratio_wrt_circle = 0.75
pairs = []
pairs = [1,8,1,2,1,5,2,3,3,4,5,6,6,7,8,9,9,10,10,11,8,12,12,13,13,14,1,0,0,15,15,17,0,16,16,18,14,19,19,20,14,21,11,22,22,23,11,24]
pairs = np.array(pairs).reshape(-1,2)
colors = [255., 0., 85.,
255., 0., 0.,
255., 85., 0.,
255., 170., 0.,
255., 255., 0.,
170., 255., 0.,
85., 255., 0.,
0., 255., 0.,
255., 0., 0.,
0., 255., 85.,
0., 255., 170.,
0., 255., 255.,
0., 170., 255.,
0., 85., 255.,
0., 0., 255.,
255., 0., 170.,
170., 0., 255.,
255., 0., 255.,
85., 0., 255.,
0., 0., 255.,
0., 0., 255.,
0., 0., 255.,
0., 255., 255.,
0., 255., 255.,
0., 255., 255.]
colors = np.array(colors).reshape(-1,3)
pose_scales = [1]
return render_keypoints(img, body_keypoints, pairs, colors, thickness_circle_ratio, thickness_line_ratio_wrt_circle, pose_scales, 0.1)
def render_openpose(img: np.array,
hand_keypoints: np.array) -> np.array:
"""
Render keypoints in the OpenPose format on input image.
Args:
img (np.array): Input image of shape (H, W, 3) with pixel values in the [0,255] range.
body_keypoints (np.array): Keypoint array of shape (N, 3); 3 <====> (x, y, confidence).
Returns:
(np.array): Image of shape (H, W, 3) with keypoints drawn on top of the original image.
"""
#img = render_body_keypoints(img, body_keypoints)
img = render_hand_keypoints(img, hand_keypoints)
return img
|