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# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import os
import cv2
import torch
import numpy as np
from . import util
from .wholebody import Wholebody, HWC3, resize_image
from PIL import Image
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
def convert_to_numpy(image):
if isinstance(image, Image.Image):
image = np.array(image)
elif isinstance(image, torch.Tensor):
image = image.detach().cpu().numpy()
elif isinstance(image, np.ndarray):
image = image.copy()
else:
raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.'
return image
def draw_pose(pose, H, W, use_hand=False, use_body=False, use_face=False):
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)
if use_body:
canvas = util.draw_bodypose(canvas, candidate, subset)
if use_hand:
canvas = util.draw_handpose(canvas, hands)
if use_face:
canvas = util.draw_facepose(canvas, faces)
return canvas
class PoseAnnotator:
def __init__(self, cfg, device=None):
onnx_det = cfg['DETECTION_MODEL']
onnx_pose = cfg['POSE_MODEL']
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
self.pose_estimation = Wholebody(onnx_det, onnx_pose, device=self.device)
self.resize_size = cfg.get("RESIZE_SIZE", 1024)
self.use_body = cfg.get('USE_BODY', True)
self.use_face = cfg.get('USE_FACE', True)
self.use_hand = cfg.get('USE_HAND', True)
@torch.no_grad()
@torch.inference_mode
def forward(self, image):
image = convert_to_numpy(image)
input_image = HWC3(image[..., ::-1])
return self.process(resize_image(input_image, self.resize_size), image.shape[:2])
def process(self, ori_img, ori_shape):
ori_h, ori_w = ori_shape
ori_img = ori_img.copy()
H, W, C = ori_img.shape
with torch.no_grad():
candidate, subset, det_result = self.pose_estimation(ori_img)
nums, keys, locs = candidate.shape
candidate[..., 0] /= float(W)
candidate[..., 1] /= float(H)
body = candidate[:, :18].copy()
body = body.reshape(nums * 18, locs)
score = subset[:, :18]
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[:, 24:92]
hands = candidate[:, 92:113]
hands = np.vstack([hands, candidate[:, 113:]])
bodies = dict(candidate=body, subset=score)
pose = dict(bodies=bodies, hands=hands, faces=faces)
ret_data = {}
if self.use_body:
detected_map_body = draw_pose(pose, H, W, use_body=True)
detected_map_body = cv2.resize(detected_map_body[..., ::-1], (ori_w, ori_h),
interpolation=cv2.INTER_LANCZOS4 if ori_h * ori_w > H * W else cv2.INTER_AREA)
ret_data["detected_map_body"] = detected_map_body
if self.use_face:
detected_map_face = draw_pose(pose, H, W, use_face=True)
detected_map_face = cv2.resize(detected_map_face[..., ::-1], (ori_w, ori_h),
interpolation=cv2.INTER_LANCZOS4 if ori_h * ori_w > H * W else cv2.INTER_AREA)
ret_data["detected_map_face"] = detected_map_face
if self.use_body and self.use_face:
detected_map_bodyface = draw_pose(pose, H, W, use_body=True, use_face=True)
detected_map_bodyface = cv2.resize(detected_map_bodyface[..., ::-1], (ori_w, ori_h),
interpolation=cv2.INTER_LANCZOS4 if ori_h * ori_w > H * W else cv2.INTER_AREA)
ret_data["detected_map_bodyface"] = detected_map_bodyface
if self.use_hand and self.use_body and self.use_face:
detected_map_handbodyface = draw_pose(pose, H, W, use_hand=True, use_body=True, use_face=True)
detected_map_handbodyface = cv2.resize(detected_map_handbodyface[..., ::-1], (ori_w, ori_h),
interpolation=cv2.INTER_LANCZOS4 if ori_h * ori_w > H * W else cv2.INTER_AREA)
ret_data["detected_map_handbodyface"] = detected_map_handbodyface
# convert_size
if det_result.shape[0] > 0:
w_ratio, h_ratio = ori_w / W, ori_h / H
det_result[..., ::2] *= h_ratio
det_result[..., 1::2] *= w_ratio
det_result = det_result.astype(np.int32)
return ret_data, det_result
class PoseBodyFaceAnnotator(PoseAnnotator):
def __init__(self, cfg):
super().__init__(cfg)
self.use_body, self.use_face, self.use_hand = True, True, False
@torch.no_grad()
@torch.inference_mode
def forward(self, image):
ret_data, det_result = super().forward(image)
return ret_data['detected_map_bodyface']
class PoseBodyFaceVideoAnnotator(PoseBodyFaceAnnotator):
def forward(self, frames):
ret_frames = []
for frame in frames:
anno_frame = super().forward(np.array(frame))
ret_frames.append(anno_frame)
return ret_frames
import imageio
def save_one_video(file_path, videos, fps=8, quality=8, macro_block_size=None):
try:
video_writer = imageio.get_writer(file_path, fps=fps, codec='libx264', quality=quality, macro_block_size=macro_block_size)
for frame in videos:
video_writer.append_data(frame)
video_writer.close()
return True
except Exception as e:
print(f"Video save error: {e}")
return False
def get_frames(video_path):
frames = []
# Opens the Video file with CV2
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
print("video fps: " + str(fps))
i = 0
while cap.isOpened():
ret, frame = cap.read()
if ret == False:
break
frames.append(frame)
i += 1
cap.release()
cv2.destroyAllWindows()
return frames, fps
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