"""
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) -> 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_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,
body_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)
return img