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# https://github.com/IDEA-Research/DWPose
# Openpose
# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
# 3rd Edited by ControlNet
# 4th Edited by ControlNet (added face and correct hands)
import copy
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
import cv2
import numpy as np
import torch
from controlnet_aux.util import HWC3, resize_image
from PIL import Image
from . import util
from .wholebody import Wholebody
def draw_pose(pose, H, W):
bodies = pose["bodies"]
faces = pose["faces"]
hands = pose["hands"]
candidate = bodies["candidate"]
subset = bodies["subset"]
canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
canvas = util.draw_bodypose(canvas, candidate, subset)
canvas = util.draw_handpose(canvas, hands)
canvas = util.draw_facepose(canvas, faces)
return canvas
class DWposeDetector:
def __init__(self):
pass
def to(self, device):
self.pose_estimation = Wholebody(device)
return self
def cal_height(self, input_image):
input_image = cv2.cvtColor(
np.array(input_image, dtype=np.uint8), cv2.COLOR_RGB2BGR
)
input_image = HWC3(input_image)
H, W, C = input_image.shape
with torch.no_grad():
candidate, subset = self.pose_estimation(input_image)
nums, keys, locs = candidate.shape
# candidate[..., 0] /= float(W)
# candidate[..., 1] /= float(H)
body = candidate
return body[0, ..., 1].min(), body[..., 1].max() - body[..., 1].min()
def __call__(
self,
input_image,
detect_resolution=512,
image_resolution=512,
output_type="pil",
**kwargs,
):
input_image = cv2.cvtColor(
np.array(input_image, dtype=np.uint8), cv2.COLOR_RGB2BGR
)
input_image = HWC3(input_image)
input_image = resize_image(input_image, detect_resolution)
H, W, C = input_image.shape
with torch.no_grad():
candidate, subset = self.pose_estimation(input_image)
nums, keys, locs = candidate.shape
candidate[..., 0] /= float(W)
candidate[..., 1] /= float(H)
score = subset[:, :18]
max_ind = np.mean(score, axis=-1).argmax(axis=0)
score = score[[max_ind]]
body = candidate[:, :18].copy()
body = body[[max_ind]]
nums = 1
body = body.reshape(nums * 18, locs)
body_score = copy.deepcopy(score)
for i in range(len(score)):
for j in range(len(score[i])):
if score[i][j] > 0.3:
score[i][j] = int(18 * i + j)
else:
score[i][j] = -1
un_visible = subset < 0.3
candidate[un_visible] = -1
foot = candidate[:, 18:24]
faces = candidate[[max_ind], 24:92]
hands = candidate[[max_ind], 92:113]
hands = np.vstack([hands, candidate[[max_ind], 113:]])
bodies = dict(candidate=body, subset=score)
pose = dict(bodies=bodies, hands=hands, faces=faces)
detected_map = draw_pose(pose, H, W)
detected_map = HWC3(detected_map)
img = resize_image(input_image, image_resolution)
H, W, C = img.shape
detected_map = cv2.resize(
detected_map, (W, H), interpolation=cv2.INTER_LINEAR
)
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map, body_score
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