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import os
from pathlib import Path
import sys
import torch
from PIL import Image, ImageOps
import numpy as np
from utils_ootd import get_mask_location
from cloths_db import cloths_map, modeL_db
PROJECT_ROOT = Path(__file__).absolute().parents[1].absolute()
sys.path.insert(0, str(PROJECT_ROOT))
from preprocess.openpose.run_openpose import OpenPose
from preprocess.humanparsing.run_parsing import Parsing
from ootd.inference_ootd_hd import OOTDiffusionHD
from ootd.inference_ootd_dc import OOTDiffusionDC
from preprocess.openpose.annotator.openpose.util import draw_bodypose
openpose_model = OpenPose(0)
parsing_model_dc = Parsing(0)
ootd_model_dc = OOTDiffusionDC(0)
category_dict = ['upperbody', 'lowerbody', 'dress']
category_dict_utils = ['upper_body', 'lower_body', 'dresses']
example_path = os.path.join(os.path.dirname(__file__), 'examples')
garment_path = os.path.join(os.path.dirname(__file__), 'examples','garment')
openpose_model.preprocessor.body_estimation.model.to('cuda')
ootd_model_dc.pipe.to('cuda')
ootd_model_dc.image_encoder.to('cuda')
ootd_model_dc.text_encoder.to('cuda')
def process_dc(vton_img, garm_img, category):
model_type = 'dc'
if category == 'Upper-body':
category = 0
elif category == 'Lower-body':
category = 1
else:
category =2
with torch.no_grad():
# openpose_model_dc.preprocessor.body_estimation.model.to('cuda')
# ootd_model_dc.pipe.to('cuda')
# ootd_model_dc.image_encoder.to('cuda')
# ootd_model_dc.text_encoder.to('cuda')
garm_img = Image.open(garm_img).resize((768, 1024))
vton_img = Image.open(vton_img).resize((768, 1024))
keypoints = openpose_model(vton_img.resize((384, 512)))
print(len(keypoints["pose_keypoints_2d"]))
print(keypoints["pose_keypoints_2d"])
# person_image = np.asarray(vton_img)
# print(len(person_image))
# person_image = np.asarray(Image.open(vton_img).resize((768, 1024)))
# output = draw_bodypose(canvas=person_image,candidate=candidate, subset=subset )
# output_image = Image.fromarray(output)
# output_image.save('keypose.png')
left_point = keypoints["pose_keypoints_2d"][2]
right_point = keypoints["pose_keypoints_2d"][5]
neck_point = keypoints["pose_keypoints_2d"][1]
hip_point = keypoints["pose_keypoints_2d"][8]
print(f'left shoulder - {left_point}')
print(f'right shoulder - {right_point}')
# #find disctance using Euclidian distance
shoulder_width_pixels = round(np.sqrt( np.power((right_point[0]-left_point[0]),2) + np.power((right_point[1]-left_point[1]),2)),2)
height_pixels = round(np.sqrt( np.power((neck_point[0]-hip_point[0]),2) + np.power((neck_point[1]-hip_point[1]),2)),2) *2
# # Assuming an average human height
average_height_cm = 172.72 *1.5
# Conversion factor from pixels to cm
conversion_factor = average_height_cm / height_pixels
# Convert shoulder width to real-world units
shoulder_width_cm = shoulder_width_pixels * conversion_factor
print(f'Shoulder width (in pixels): {shoulder_width_pixels}')
print(f'Estimated height (in pixels): {height_pixels}')
print(f'Conversion factor (pixels to cm): {conversion_factor}')
print(f'Shoulder width (in cm): {shoulder_width_cm}')
print(f'Shoulder width (in INCH): {round(shoulder_width_cm/2.54,1)}')
model_parse, face_mask = parsing_model_dc(vton_img.resize((384, 512)))
# model_parse_image = convert_to_image(model_parse)
# face_mask_image = convert_to_image(face_mask)
# Save the images
# model_parse_image.save('model_parse_image.png')
# face_mask_image.save('face_mask_image.png')
mask, mask_gray = get_mask_location(model_type, category_dict_utils[category], model_parse, keypoints)
# up_mask, up_mask_gray = get_mask_location(model_type, category_dict_utils[0], model_parse, keypoints)
# lo_mask, lo_mask_gray = get_mask_location(model_type, category_dict_utils[1], model_parse, keypoints)
# mask = Image.composite(up_mask,lo_mask,up_mask)
# mask_gray = Image.composite(up_mask_gray, lo_mask_gray,up_mask)
mask = mask.resize((768, 1024), Image.NEAREST)
mask_gray = mask_gray.resize((768, 1024), Image.NEAREST)
# Save the resized masks
mask.save("mask_resized.png")
mask_gray.save("mask_gray_resized.png")
masked_vton_img = Image.composite(mask_gray, vton_img, mask)
masked_vton_img.save("masked_vton_img.png")
print(f'category is {category}')
# images = ootd_model_dc(
# model_type=model_type,
# category=category_dict[category],
# image_garm=garm_img,
# image_vton=masked_vton_img,
# mask=mask,
# image_ori=vton_img,
# num_samples=1,
# num_steps=10,
# image_scale= 2.0,
# seed=-1,
# )
# return None
return None
if __name__ == '__main__':
model_dc = os.path.join(example_path, 'model/model_8.png')
garment_dc = os.path.join(example_path, 'garment/048554_1.jpg')
print(process_dc(model_dc,garment_dc,0)) |