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import pdb
import config
from pathlib import Path
import sys
PROJECT_ROOT = Path(__file__).absolute().parents[0].absolute()
sys.path.insert(0, str(PROJECT_ROOT))
import os
import cv2
import einops
import numpy as np
import random
import time
import json
# from pytorch_lightning import seed_everything
from preprocess.openpose.annotator.util import resize_image, HWC3
from preprocess.openpose.annotator.openpose import OpenposeDetector
import argparse
from PIL import Image
import torch
import pdb
# os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
class OpenPose:
def __init__(self, gpu_id: int):
# self.gpu_id = gpu_id
# torch.cuda.set_device(gpu_id)
self.preprocessor = OpenposeDetector()
def __call__(self, input_image, resolution=384):
# torch.cuda.set_device(self.gpu_id)
if isinstance(input_image, Image.Image):
input_image = np.asarray(input_image)
elif type(input_image) == str:
input_image = np.asarray(Image.open(input_image))
else:
raise ValueError
with torch.no_grad():
input_image = HWC3(input_image)
input_image = resize_image(input_image, resolution)
H, W, C = input_image.shape
assert (H == 512 and W == 384), 'Incorrect input image shape'
pose, detected_map = self.preprocessor(input_image, hand_and_face=False)
candidate = pose['bodies']['candidate']
subset = pose['bodies']['subset'][0][:18]
for i in range(18):
if subset[i] == -1:
candidate.insert(i, [0, 0])
for j in range(i, 18):
if(subset[j]) != -1:
subset[j] += 1
elif subset[i] != i:
candidate.pop(i)
for j in range(i, 18):
if(subset[j]) != -1:
subset[j] -= 1
candidate = candidate[:18]
for i in range(18):
candidate[i][0] *= 384
candidate[i][1] *= 512
keypoints = {"pose_keypoints_2d": candidate}
# with open("/home/aigc/ProjectVTON/OpenPose/keypoints/keypoints.json", "w") as f:
# json.dump(keypoints, f)
#
# # print(candidate)
# output_image = cv2.resize(cv2.cvtColor(detected_map, cv2.COLOR_BGR2RGB), (768, 1024))
# cv2.imwrite('/home/aigc/ProjectVTON/OpenPose/keypoints/out_pose.jpg', output_image)
return keypoints
if __name__ == '__main__':
model = OpenPose()
model('./images/model.jpg')
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