File size: 11,405 Bytes
baa8e90 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 |
import numpy as np
import cv2
import os
import cv2
from .dw_onnx.cv_ox_det import inference_detector as inference_onnx_yolox
from .dw_onnx.cv_ox_yolo_nas import inference_detector as inference_onnx_yolo_nas
from .dw_onnx.cv_ox_pose import inference_pose as inference_onnx_pose
from .dw_torchscript.jit_det import inference_detector as inference_jit_yolox
from .dw_torchscript.jit_pose import inference_pose as inference_jit_pose
from typing import List, Optional
from .types import PoseResult, BodyResult, Keypoint
from timeit import default_timer
from controlnet_aux.dwpose.util import guess_onnx_input_shape_dtype, get_ort_providers, get_model_type, is_model_torchscript
import json
import torch
import torch.utils.benchmark.utils.timer as torch_timer
def drawBetweenKeypoints(pose_img, keypoints, indexes, color, scaleFactor):
ind0 = indexes[0] - 1
ind1 = indexes[1] - 1
point1 = (keypoints[ind0][0], keypoints[ind0][1])
point2 = (keypoints[ind1][0], keypoints[ind1][1])
thickness = int(5 // scaleFactor)
cv2.line(pose_img, (int(point1[0]), int(point1[1])), (int(point2[0]), int(point2[1])), color, thickness)
def drawBetweenKeypointsList(pose_img, keypoints, keypointPairsList, colorsList, scaleFactor):
for ind, keypointPair in enumerate(keypointPairsList):
drawBetweenKeypoints(pose_img, keypoints, keypointPair, colorsList[ind], scaleFactor)
def drawBetweenSetofKeypointLists(pose_img, keypoints_set, keypointPairsList, colorsList, scaleFactor):
for keypoints in keypoints_set:
drawBetweenKeypointsList(pose_img, keypoints, keypointPairsList, colorsList, scaleFactor)
def padImg(img, size, blackBorder=True):
left, right, top, bottom = 0, 0, 0, 0
# pad x
if img.shape[1] < size[1]:
sidePadding = int((size[1] - img.shape[1]) // 2)
left = sidePadding
right = sidePadding
# pad extra on right if padding needed is an odd number
if img.shape[1] % 2 == 1:
right += 1
# pad y
if img.shape[0] < size[0]:
topBottomPadding = int((size[0] - img.shape[0]) // 2)
top = topBottomPadding
bottom = topBottomPadding
# pad extra on bottom if padding needed is an odd number
if img.shape[0] % 2 == 1:
bottom += 1
if blackBorder:
paddedImg = cv2.copyMakeBorder(src=img, top=top, bottom=bottom, left=left, right=right, borderType=cv2.BORDER_CONSTANT, value=(0,0,0))
else:
paddedImg = cv2.copyMakeBorder(src=img, top=top, bottom=bottom, left=left, right=right, borderType=cv2.BORDER_REPLICATE)
return paddedImg
def smartCrop(img, size, center):
width = img.shape[1]
height = img.shape[0]
xSize = size[1]
ySize = size[0]
xCenter = center[0]
yCenter = center[1]
if img.shape[0] > size[0] or img.shape[1] > size[1]:
leftMargin = xCenter - xSize//2
rightMargin = xCenter + xSize//2
upMargin = yCenter - ySize//2
downMargin = yCenter + ySize//2
if(leftMargin < 0):
xCenter += (-leftMargin)
if(rightMargin > width):
xCenter -= (rightMargin - width)
if(upMargin < 0):
yCenter -= -upMargin
if(downMargin > height):
yCenter -= (downMargin - height)
img = cv2.getRectSubPix(img, size, (xCenter, yCenter))
return img
def calculateScaleFactor(img, size, poseSpanX, poseSpanY):
poseSpanX = max(poseSpanX, size[0])
scaleFactorX = 1
if poseSpanX > size[0]:
scaleFactorX = size[0] / poseSpanX
scaleFactorY = 1
if poseSpanY > size[1]:
scaleFactorY = size[1] / poseSpanY
scaleFactor = min(scaleFactorX, scaleFactorY)
return scaleFactor
def scaleImg(img, size, poseSpanX, poseSpanY, scaleFactor):
scaledImg = img
scaledImg = cv2.resize(img, (0, 0), fx=scaleFactor, fy=scaleFactor)
return scaledImg, scaleFactor
class AnimalPoseImage:
def __init__(self, det_model_path: Optional[str] = None, pose_model_path: Optional[str] = None, torchscript_device="cuda"):
self.det_filename = det_model_path and os.path.basename(det_model_path)
self.pose_filename = pose_model_path and os.path.basename(pose_model_path)
self.det, self.pose = None, None
# return type: None ort cv2 torchscript
self.det_model_type = get_model_type("AnimalPose",self.det_filename)
self.pose_model_type = get_model_type("AnimalPose",self.pose_filename)
# Always loads to CPU to avoid building OpenCV.
cv2_device = 'cpu'
cv2_backend = cv2.dnn.DNN_BACKEND_OPENCV if cv2_device == 'cpu' else cv2.dnn.DNN_BACKEND_CUDA
# You need to manually build OpenCV through cmake to work with your GPU.
cv2_providers = cv2.dnn.DNN_TARGET_CPU if cv2_device == 'cpu' else cv2.dnn.DNN_TARGET_CUDA
ort_providers = get_ort_providers()
if self.det_model_type is None:
pass
elif self.det_model_type == "ort":
try:
import onnxruntime as ort
self.det = ort.InferenceSession(det_model_path, providers=ort_providers)
except:
print(f"Failed to load onnxruntime with {self.det.get_providers()}.\nPlease change EP_list in the config.yaml and restart ComfyUI")
self.det = ort.InferenceSession(det_model_path, providers=["CPUExecutionProvider"])
elif self.det_model_type == "cv2":
try:
self.det = cv2.dnn.readNetFromONNX(det_model_path)
self.det.setPreferableBackend(cv2_backend)
self.det.setPreferableTarget(cv2_providers)
except:
print("TopK operators may not work on your OpenCV, try use onnxruntime with CPUExecutionProvider")
try:
import onnxruntime as ort
self.det = ort.InferenceSession(det_model_path, providers=["CPUExecutionProvider"])
except:
print(f"Failed to load {det_model_path}, you can use other models instead")
else:
self.det = torch.jit.load(det_model_path)
self.det.to(torchscript_device)
if self.pose_model_type is None:
pass
elif self.pose_model_type == "ort":
try:
import onnxruntime as ort
self.pose = ort.InferenceSession(pose_model_path, providers=ort_providers)
except:
print(f"Failed to load onnxruntime with {self.pose.get_providers()}.\nPlease change EP_list in the config.yaml and restart ComfyUI")
self.pose = ort.InferenceSession(pose_model_path, providers=["CPUExecutionProvider"])
elif self.pose_model_type == "cv2":
self.pose = cv2.dnn.readNetFromONNX(pose_model_path)
self.pose.setPreferableBackend(cv2_backend)
self.pose.setPreferableTarget(cv2_providers)
else:
self.pose = torch.jit.load(pose_model_path)
self.pose.to(torchscript_device)
if self.pose_filename is not None:
self.pose_input_size, _ = guess_onnx_input_shape_dtype(self.pose_filename)
def __call__(self, oriImg) -> Optional[np.ndarray]:
detect_classes = list(range(14, 23 + 1)) #https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml
if is_model_torchscript(self.det):
det_start = torch_timer.timer()
det_result = inference_jit_yolox(self.det, oriImg, detect_classes=detect_classes)
print(f"AnimalPose: Bbox {((torch_timer.timer() - det_start) * 1000):.2f}ms")
else:
det_start = default_timer()
det_onnx_dtype = np.float32 if "yolox" in self.det_filename else np.uint8
if "yolox" in self.det_filename:
det_result = inference_onnx_yolox(self.det, oriImg, detect_classes=detect_classes, dtype=det_onnx_dtype)
else:
#FP16 and INT8 YOLO NAS accept uint8 input
det_result = inference_onnx_yolo_nas(self.det, oriImg, detect_classes=detect_classes, dtype=det_onnx_dtype)
print(f"AnimalPose: Bbox {((default_timer() - det_start) * 1000):.2f}ms")
if (det_result is None) or (det_result.shape[0] == 0):
openpose_dict = {
'version': 'ap10k',
'animals': [],
'canvas_height': oriImg.shape[0],
'canvas_width': oriImg.shape[1]
}
return np.zeros_like(oriImg), openpose_dict
if is_model_torchscript(self.pose):
pose_start = torch_timer.timer()
keypoint_sets, scores = inference_jit_pose(self.pose, det_result, oriImg, self.pose_input_size)
print(f"AnimalPose: Pose {((torch_timer.timer() - pose_start) * 1000):.2f}ms on {det_result.shape[0]} animals\n")
else:
pose_start = default_timer()
_, pose_onnx_dtype = guess_onnx_input_shape_dtype(self.pose_filename)
keypoint_sets, scores = inference_onnx_pose(self.pose, det_result, oriImg, self.pose_input_size, dtype=pose_onnx_dtype)
print(f"AnimalPose: Pose {((default_timer() - pose_start) * 1000):.2f}ms on {det_result.shape[0]} animals\n")
animal_kps_scores = []
pose_img = np.zeros((oriImg.shape[0], oriImg.shape[1], 3), dtype = np.uint8)
for (idx, keypoints) in enumerate(keypoint_sets):
# don't use keypoints that go outside the frame in calculations for the center
interorKeypoints = keypoints[((keypoints[:,0] > 0) & (keypoints[:,0] < oriImg.shape[1])) & ((keypoints[:,1] > 0) & (keypoints[:,1] < oriImg.shape[0]))]
xVals = interorKeypoints[:,0]
yVals = interorKeypoints[:,1]
minX = np.amin(xVals)
minY = np.amin(yVals)
maxX = np.amax(xVals)
maxY = np.amax(yVals)
poseSpanX = maxX - minX
poseSpanY = maxY - minY
# find mean center
xSum = np.sum(xVals)
ySum = np.sum(yVals)
xCenter = xSum // xVals.shape[0]
yCenter = ySum // yVals.shape[0]
center_of_keypoints = (xCenter,yCenter)
# order of the keypoints for AP10k and a standardized list of colors for limbs
keypointPairsList = [(1,2), (2,3), (1,3), (3,4), (4,9), (9,10), (10,11), (4,6), (6,7), (7,8), (4,5), (5,15), (15,16), (16,17), (5,12), (12,13), (13,14)]
colorsList = [(255,255,255), (100,255,100), (150,255,255), (100,50,255), (50,150,200), (0,255,255), (0,150,0), (0,0,255), (0,0,150), (255,50,255), (255,0,255), (255,0,0), (150,0,0), (255,255,100), (0,150,0), (255,255,0), (150,150,150)] # 16 colors needed
drawBetweenKeypointsList(pose_img, keypoints, keypointPairsList, colorsList, scaleFactor=1.0)
score = scores[idx, ..., None]
score[score > 1.0] = 1.0
score[score < 0.0] = 0.0
animal_kps_scores.append(np.concatenate((keypoints, score), axis=-1))
openpose_dict = {
'version': 'ap10k',
'animals': [keypoints.tolist() for keypoints in animal_kps_scores],
'canvas_height': oriImg.shape[0],
'canvas_width': oriImg.shape[1]
}
return pose_img, openpose_dict |