preprocess/openpose/run_openpose.py

# 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/bad_model.jpg')


#sample 

from pathlib import Path
import sys
import numpy as np
from PIL import Image
import torch

from preprocess.openpose.annotator.util import resize_image, HWC3
from preprocess.openpose.annotator.openpose import OpenposeDetector

class OpenPose:
    def __init__(self, gpu_id: int = 0):
        self.preprocessor = OpenposeDetector()

    def __call__(self, input_image, resolution=384):
        if isinstance(input_image, Image.Image):
            input_image = np.asarray(input_image)
        elif isinstance(input_image, str):
            input_image = np.asarray(Image.open(input_image))
        else:
            raise ValueError("Invalid image input format.")

        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}

        return keypoints