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Browse files- app/__pycache__/genai.cpython-311.pyc +0 -0
- app/app.py +71 -0
- app/gen_mask.py +63 -0
- app/genai.py +167 -0
- app/huggingface-cloth-segmentation/LICENSE +21 -0
- app/huggingface-cloth-segmentation/README.md +38 -0
- app/huggingface-cloth-segmentation/__pycache__/network.cpython-311.pyc +0 -0
- app/huggingface-cloth-segmentation/__pycache__/options.cpython-311.pyc +0 -0
- app/huggingface-cloth-segmentation/__pycache__/process.cpython-311.pyc +0 -0
- app/huggingface-cloth-segmentation/app.py +39 -0
- app/huggingface-cloth-segmentation/assets/1.png +0 -0
- app/huggingface-cloth-segmentation/assets/2.png +0 -0
- app/huggingface-cloth-segmentation/input/03615_00.jpg +0 -0
- app/huggingface-cloth-segmentation/input/08909_00.jpg +0 -0
- app/huggingface-cloth-segmentation/model/cloth_segm.pth +3 -0
- app/huggingface-cloth-segmentation/network.py +560 -0
- app/huggingface-cloth-segmentation/options.py +12 -0
- app/huggingface-cloth-segmentation/output/alpha/1.png +0 -0
- app/huggingface-cloth-segmentation/output/alpha/3.png +0 -0
- app/huggingface-cloth-segmentation/output/cloth_seg/final_seg.png +0 -0
- app/huggingface-cloth-segmentation/process.py +190 -0
- app/huggingface-cloth-segmentation/requirements.txt +7 -0
- app/main.py +71 -0
- app/model/cloth_segm.pth +3 -0
- app/output/alpha/1.png +0 -0
- app/output/alpha/2.png +0 -0
- app/output/alpha/3.png +0 -0
- app/output/cloth_seg/final_seg.png +0 -0
- app/output_image.jpg +0 -0
- app/output_image_1.jpg +0 -0
- app/output_image_2.jpg +0 -0
- app/output_image_3.jpg +0 -0
- app/output_image_4.jpg +0 -0
- app/processed_images/output_image.jpg +0 -0
- app/processed_images/output_image_1.jpg +0 -0
app/__pycache__/genai.cpython-311.pyc
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Binary file (5.97 kB). View file
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app/app.py
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from flask import Flask, request, jsonify, send_from_directory
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from flask_cors import CORS
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from genai import gen_vton
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from werkzeug.utils import secure_filename
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import os
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import tempfile
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#app = Flask(__name__)
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app = Flask(__name__, static_folder='processed_images')
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CORS(app, supports_credentials=True)
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#CORS(app, supports_credentials=True, resources={r"/*": {"origins": "*"}}) # Allow requests from any originorigins=["http://localhost:3000"])
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#CORS(app, resources={r"/proc": {"origins": "http://localhost:3000"}}, supports_credentials=True)
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#@app.route("/proc")
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@app.route('/proc', methods=['POST'])
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def process_images():
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# Retrieve images from the request
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print("Request came here")
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print(request)
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print(request.headers)
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print(request.files)
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user_image_t = request.files.get('userImage')
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dress_image_t = request.files.get('dressImage')
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#print(dress_image_t.filename)
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print(user_image_t.filename)
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#file = request.files['file']
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if dress_image_t:
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# Save the file to a temporary file
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temp_dir = tempfile.gettempdir()
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filename = secure_filename(dress_image_t.filename)
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temp_path = os.path.join(temp_dir, filename)
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dress_image_t.save(temp_path)
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dress_image = temp_path
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if user_image_t:
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temp_dir = tempfile.gettempdir()
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filename = secure_filename(user_image_t.filename)
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temp_path_1 = os.path.join(temp_dir, filename)
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user_image_t.save(temp_path_1)
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user_image = temp_path_1
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gen_vton(user_image, dress_image)
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processed_image_1_path = './processed_images/output_image.jpg'
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processed_image_2_path = './processed_images/output_image_1.jpg'
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# Save your images using the paths above...
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# Return the URL for the saved images
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url_to_processed_image_1 = request.host_url + processed_image_1_path
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url_to_processed_image_2 = request.host_url + processed_image_2_path
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# Process images...
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# For the sake of this example, let's say the processing function returns two image URLs
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processed_image_urls = [url_to_processed_image_1, url_to_processed_image_2]
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os.remove(temp_path)
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os.remove(temp_path_1)
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return jsonify({'processedImages': processed_image_urls})
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@app.route('/processed_images/<filename>')
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def processed_images(filename):
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print("request_came_here")
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return send_from_directory(app.static_folder, filename)
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# Example of generating a unique filename for the output
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#
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if __name__ == '__main__':
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app.run()
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app/gen_mask.py
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from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
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from PIL import Image
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import requests
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import matplotlib.pyplot as plt
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import torch.nn as nn
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processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes")
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model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
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url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
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#image = Image.open(requests.get(url, stream=True).raw)
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image_path = "C:/Users/Admin/Downloads/dress1.jpg"
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image = Image.open(image_path)
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inputs = processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits.cpu()
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print("here")
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upsampled_logits = nn.functional.interpolate(
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logits,
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size=image.size[::-1],
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mode="bilinear",
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align_corners=False,
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)
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print(upsampled_logits.argmax(dim=1))
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pred_seg = upsampled_logits.argmax(dim=1)[0]
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plt.imshow(pred_seg)
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import matplotlib as mpl
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label_names = list(model.config.id2label)
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# Create a color map with the same number of colors as your labels
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# Use the updated method to get the colormap
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cmap = mpl.colormaps['tab20']
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# Create the figure and axes for the plot and the colorbar
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fig, ax = plt.subplots()
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# Display the segmentation
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im = ax.imshow(pred_seg, cmap=cmap)
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# Create a colorbar
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cbar = fig.colorbar(im, ax=ax, ticks=range(len(label_names)))
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cbar.ax.set_yticklabels(label_names)
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plt.show()
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# Get the number of labels
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n_labels = len(label_names)
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# Extract RGB values for each color in the colormap
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colors = cmap.colors[:n_labels]
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# Convert RGBA to RGB by omitting the Alpha value
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rgb_colors = [color[:3] for color in colors]
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# Create a dictionary mapping labels to RGB colors
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label_to_color = dict(zip(label_names, rgb_colors))
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# Display the mapping
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for label, color in label_to_color.items():
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print(f"{label}: {color}")
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app/genai.py
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# Or save the image
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#output_image.save("output_image.jpg")
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from os import device_encoding
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from diffusers import StableDiffusionInpaintPipeline
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from PIL import Image
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import torch
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import numpy as np
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import torch
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import gc
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from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipelineLegacy, DDIMScheduler, AutoencoderKL
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from PIL import Image
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#import pose_estimation as pe
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import requests
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from rembg import remove
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from transformers import BlipProcessor, BlipForConditionalGeneration
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import sys
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import os
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import subprocess
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sys.path.append(
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os.path.join(os.path.dirname(__file__), "huggingface-cloth-segmentation"))
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from process import load_seg_model, get_palette, generate_mask
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device = 'cpu'
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def initialize_and_load_models():
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checkpoint_path = 'model/cloth_segm.pth'
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net = load_seg_model(checkpoint_path, device=device)
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return net
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net = initialize_and_load_models()
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palette = get_palette(4)
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def run(img):
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cloth_seg = generate_mask(img, net=net, palette=palette, device=device)
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return cloth_seg
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def image_caption(image_path, img_type):
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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processor = BlipProcessor.from_pretrained("noamrot/FuseCap")
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model = BlipForConditionalGeneration.from_pretrained("noamrot/FuseCap").to(device)
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raw_image = Image.open(image_path).convert('RGB')
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if img_type == "dress":
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raw_image = remove(raw_image)
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print("bg removed")
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raw_image.show
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#raw_image = img_np_no_bg
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text = "a picture of "
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inputs = processor(raw_image, text, return_tensors="pt").to(device)
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out = model.generate(**inputs, num_beams = 3)
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print(processor.decode(out[0], skip_special_tokens=True))
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caption = processor.decode(out[0], skip_special_tokens=True)
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return caption
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def gen_vton(image_input, dress_input):
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# Load the pre-trained model
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pipe = StableDiffusionInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting",
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#revision="fp16", # Or "full" to disable
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torch_dtype=torch.float32, # Or torch.float32
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)
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image_path = image_input
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#submodule_path = os.path.join(os.path.dirname(__file__), "huggingface-cloth-segmentation/process.py")
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img_open = Image.open(image_path)
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#
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run(img_open)
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gen_mask_1 = "./huggingface-cloth-segmentation/output/alpha/1.png"
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gen_mask_2 = "./huggingface-cloth-segmentation/output/alpha/2.png"
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gen_mask_3 = "./huggingface-cloth-segmentation/output/alpha/3.png"
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print("mask_generated")
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if gen_mask_1:
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mask_path = gen_mask_1
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elif gen_mask_2:
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mask_path = gen_mask_2
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else:
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mask_path = gen_mask_3
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dress_path = dress_input
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image = Image.open(image_path)
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mask = Image.open(mask_path) # Convert mask to grayscale
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#image = Image.open("/content/drive/MyDrive/train1/train/image/000025.jpg")
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#mask = Image.open("/content/drive/MyDrive/train1/train/image/000014.jpg")# Convert mask to grayscale
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#image = download_image(img_url).resize((512, 512))
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#mask = download_image(mask_url).resize((512, 512))
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#image = Image.open(image_path)
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#mask_image = Image.open(mask_path)
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image = image.resize((512, 512))
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mask = mask.resize((512, 512))
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# Define your prompt (text input)
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user_caption = image_caption(image_path, "user")
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dress_caption = image_caption(dress_path, "dress")
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print(user_caption)
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print(dress_caption)
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prompt = " a human wearing a {dress_caption} "
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neg_prompt = "{user_caption}"
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# Note: `image` and `mask_image` should be PIL images.
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# The mask structure is white for inpainting and black for keeping as is.
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# Replace `image` and `mask_image` with your actual images.
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guidance_scale=7.5
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denoising_strength=0.9
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num_samples = 2
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generator = torch.Generator(device="cpu") # Explicitly create a CPU generator
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121 |
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images = pipe(
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prompt=prompt,
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negative_prompt=neg_prompt,
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128 |
+
image=image,
|
129 |
+
mask_image=mask,
|
130 |
+
guidance_scale=guidance_scale,
|
131 |
+
denoising_strength=denoising_strength,
|
132 |
+
generator=generator,
|
133 |
+
num_images_per_prompt=num_samples,
|
134 |
+
).images
|
135 |
+
|
136 |
+
#Image_1 = pipe(prompt=prompt, image=image, mask_image=mask).images[0]
|
137 |
+
|
138 |
+
|
139 |
+
#images[0] # Display the image
|
140 |
+
|
141 |
+
#img = Image.open(images[0])
|
142 |
+
#img.show()
|
143 |
+
#img = Image.open(images[1])
|
144 |
+
#img.show()
|
145 |
+
|
146 |
+
#images[2].show
|
147 |
+
# Or save the image
|
148 |
+
images[0].save("./processed_images/output_image.jpg")
|
149 |
+
images[1].save("./processed_images/output_image_1.jpg")
|
150 |
+
|
151 |
+
#images[2].save("output_image_2.jpg")
|
152 |
+
#images[3].save("output_image_3.jpg")
|
153 |
+
#images[3].save("output_image_4.jpg")
|
154 |
+
|
155 |
+
|
156 |
+
#if app == "__main__":
|
157 |
+
#gen_vton()
|
158 |
+
#user_image = "C:/Users/Admin/Downloads/woman.jpg"
|
159 |
+
#dress_image = "C:/Users/Admin/Downloads/dress1.jpg"
|
160 |
+
#gen_vton(user_image, dress_image)
|
161 |
+
|
162 |
+
def predict(dict, prompt):
|
163 |
+
image = dict['image'].convert("RGB").resize((512, 512))
|
164 |
+
mask_image = dict['mask'].convert("RGB").resize((512, 512))
|
165 |
+
#images = pipe(prompt=prompt, image=image, mask_image=mask_image).images
|
166 |
+
return(images[0])
|
167 |
+
|
app/huggingface-cloth-segmentation/LICENSE
ADDED
@@ -0,0 +1,21 @@
|
|
|
|
|
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|
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|
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|
|
|
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|
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|
|
|
|
1 |
+
MIT License
|
2 |
+
|
3 |
+
Copyright (c) 2023 Alok Pandey
|
4 |
+
|
5 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
6 |
+
of this software and associated documentation files (the "Software"), to deal
|
7 |
+
in the Software without restriction, including without limitation the rights
|
8 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
9 |
+
copies of the Software, and to permit persons to whom the Software is
|
10 |
+
furnished to do so, subject to the following conditions:
|
11 |
+
|
12 |
+
The above copyright notice and this permission notice shall be included in all
|
13 |
+
copies or substantial portions of the Software.
|
14 |
+
|
15 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
16 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
17 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
18 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
19 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
20 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
21 |
+
SOFTWARE.
|
app/huggingface-cloth-segmentation/README.md
ADDED
@@ -0,0 +1,38 @@
|
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|
|
|
|
1 |
+
# Huggingface cloth segmentation using U2NET
|
2 |
+
|
3 |
+

|
4 |
+
[](https://opensource.org/licenses/MIT)
|
5 |
+
[](https://colab.research.google.com/drive/1LGgLiHiWcmpQalgazLgq4uQuVUm9ZM4M?usp=sharing)
|
6 |
+
|
7 |
+
This repo contains inference code and gradio demo script using pre-trained U2NET model for Cloths Parsing from human portrait.</br>
|
8 |
+
Here clothes are parsed into 3 category: Upper body(red), Lower body(green) and Full body(yellow). The provided script also generates alpha images for each class.
|
9 |
+
|
10 |
+
|
11 |
+
# Inference
|
12 |
+
- clone the repo `git clone https://github.com/wildoctopus/huggingface-cloth-segmentation.git`.
|
13 |
+
- Install dependencies `pip install -r requirements.txt`
|
14 |
+
- Run `python process.py --image 'input/03615_00.jpg'` . **Script will automatically download the pretrained model**.
|
15 |
+
- Outputs will be saved in `output` folder.
|
16 |
+
- `output/alpha/..` contains alpha images corresponding to each class.
|
17 |
+
- `output/cloth_seg` contains final segmentation.
|
18 |
+
-
|
19 |
+
|
20 |
+
# Gradio Demo
|
21 |
+
- Run `python app.py`
|
22 |
+
- Navigate to local or public url provided by app on successfull execution.
|
23 |
+
### OR
|
24 |
+
- Inference in colab from here [](https://colab.research.google.com/drive/1LGgLiHiWcmpQalgazLgq4uQuVUm9ZM4M?usp=sharing)
|
25 |
+
|
26 |
+
# Huggingface Demo
|
27 |
+
- Check gradio demo on Huggingface space from here [huggingface-cloth-segmentation](https://huggingface.co/spaces/wildoctopus/cloth-segmentation).
|
28 |
+
|
29 |
+
# Output samples
|
30 |
+

|
31 |
+

|
32 |
+
|
33 |
+
|
34 |
+
This model works well with any background and almost all poses.
|
35 |
+
|
36 |
+
# Acknowledgements
|
37 |
+
- U2net model is from original [u2net repo](https://github.com/xuebinqin/U-2-Net). Thanks to Xuebin Qin for amazing repo.
|
38 |
+
- Most of the code is taken and modified from [levindabhi/cloth-segmentation](https://github.com/levindabhi/cloth-segmentation)
|
app/huggingface-cloth-segmentation/__pycache__/network.cpython-311.pyc
ADDED
Binary file (27.3 kB). View file
|
|
app/huggingface-cloth-segmentation/__pycache__/options.cpython-311.pyc
ADDED
Binary file (779 Bytes). View file
|
|
app/huggingface-cloth-segmentation/__pycache__/process.cpython-311.pyc
ADDED
Binary file (10.4 kB). View file
|
|
app/huggingface-cloth-segmentation/app.py
ADDED
@@ -0,0 +1,39 @@
|
|
|
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|
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|
|
|
1 |
+
import PIL
|
2 |
+
import torch
|
3 |
+
import gradio as gr
|
4 |
+
from process import load_seg_model, get_palette, generate_mask
|
5 |
+
|
6 |
+
|
7 |
+
device = 'cpu'
|
8 |
+
|
9 |
+
|
10 |
+
|
11 |
+
def initialize_and_load_models():
|
12 |
+
|
13 |
+
checkpoint_path = 'model/cloth_segm.pth'
|
14 |
+
net = load_seg_model(checkpoint_path, device=device)
|
15 |
+
|
16 |
+
return net
|
17 |
+
|
18 |
+
net = initialize_and_load_models()
|
19 |
+
palette = get_palette(4)
|
20 |
+
|
21 |
+
|
22 |
+
def run(img):
|
23 |
+
|
24 |
+
cloth_seg = generate_mask(img, net=net, palette=palette, device=device)
|
25 |
+
return cloth_seg
|
26 |
+
|
27 |
+
# Define input and output interfaces
|
28 |
+
input_image = gr.inputs.Image(label="Input Image", type="pil")
|
29 |
+
|
30 |
+
# Define the Gradio interface
|
31 |
+
cloth_seg_image = gr.outputs.Image(label="Cloth Segmentation", type="pil")
|
32 |
+
|
33 |
+
title = "Demo for Cloth Segmentation"
|
34 |
+
description = "An app for Cloth Segmentation"
|
35 |
+
inputs = [input_image]
|
36 |
+
outputs = [cloth_seg_image]
|
37 |
+
|
38 |
+
|
39 |
+
gr.Interface(fn=run, inputs=inputs, outputs=outputs, title=title, description=description).launch(share=True)
|
app/huggingface-cloth-segmentation/assets/1.png
ADDED
![]() |
app/huggingface-cloth-segmentation/assets/2.png
ADDED
![]() |
app/huggingface-cloth-segmentation/input/03615_00.jpg
ADDED
![]() |
app/huggingface-cloth-segmentation/input/08909_00.jpg
ADDED
![]() |
app/huggingface-cloth-segmentation/model/cloth_segm.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f71fad2bc11789a996acc507d1a5a1602ae0edefc2b9aba1cd198be5cc9c1a44
|
3 |
+
size 176625341
|
app/huggingface-cloth-segmentation/network.py
ADDED
@@ -0,0 +1,560 @@
|
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|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
import torch.nn.functional as F
|
4 |
+
|
5 |
+
|
6 |
+
class REBNCONV(nn.Module):
|
7 |
+
def __init__(self, in_ch=3, out_ch=3, dirate=1):
|
8 |
+
super(REBNCONV, self).__init__()
|
9 |
+
|
10 |
+
self.conv_s1 = nn.Conv2d(
|
11 |
+
in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate
|
12 |
+
)
|
13 |
+
self.bn_s1 = nn.BatchNorm2d(out_ch)
|
14 |
+
self.relu_s1 = nn.ReLU(inplace=True)
|
15 |
+
|
16 |
+
def forward(self, x):
|
17 |
+
|
18 |
+
hx = x
|
19 |
+
xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
|
20 |
+
|
21 |
+
return xout
|
22 |
+
|
23 |
+
|
24 |
+
## upsample tensor 'src' to have the same spatial size with tensor 'tar'
|
25 |
+
def _upsample_like(src, tar):
|
26 |
+
|
27 |
+
src = F.upsample(src, size=tar.shape[2:], mode="bilinear")
|
28 |
+
|
29 |
+
return src
|
30 |
+
|
31 |
+
|
32 |
+
### RSU-7 ###
|
33 |
+
class RSU7(nn.Module): # UNet07DRES(nn.Module):
|
34 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
35 |
+
super(RSU7, self).__init__()
|
36 |
+
|
37 |
+
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
|
38 |
+
|
39 |
+
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
|
40 |
+
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
41 |
+
|
42 |
+
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
43 |
+
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
44 |
+
|
45 |
+
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
46 |
+
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
47 |
+
|
48 |
+
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
49 |
+
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
50 |
+
|
51 |
+
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
52 |
+
self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
53 |
+
|
54 |
+
self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
55 |
+
|
56 |
+
self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
|
57 |
+
|
58 |
+
self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
59 |
+
self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
60 |
+
self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
61 |
+
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
62 |
+
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
63 |
+
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
|
64 |
+
|
65 |
+
def forward(self, x):
|
66 |
+
|
67 |
+
hx = x
|
68 |
+
hxin = self.rebnconvin(hx)
|
69 |
+
|
70 |
+
hx1 = self.rebnconv1(hxin)
|
71 |
+
hx = self.pool1(hx1)
|
72 |
+
|
73 |
+
hx2 = self.rebnconv2(hx)
|
74 |
+
hx = self.pool2(hx2)
|
75 |
+
|
76 |
+
hx3 = self.rebnconv3(hx)
|
77 |
+
hx = self.pool3(hx3)
|
78 |
+
|
79 |
+
hx4 = self.rebnconv4(hx)
|
80 |
+
hx = self.pool4(hx4)
|
81 |
+
|
82 |
+
hx5 = self.rebnconv5(hx)
|
83 |
+
hx = self.pool5(hx5)
|
84 |
+
|
85 |
+
hx6 = self.rebnconv6(hx)
|
86 |
+
|
87 |
+
hx7 = self.rebnconv7(hx6)
|
88 |
+
|
89 |
+
hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
|
90 |
+
hx6dup = _upsample_like(hx6d, hx5)
|
91 |
+
|
92 |
+
hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
|
93 |
+
hx5dup = _upsample_like(hx5d, hx4)
|
94 |
+
|
95 |
+
hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
|
96 |
+
hx4dup = _upsample_like(hx4d, hx3)
|
97 |
+
|
98 |
+
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
|
99 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
100 |
+
|
101 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
|
102 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
103 |
+
|
104 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
|
105 |
+
|
106 |
+
"""
|
107 |
+
del hx1, hx2, hx3, hx4, hx5, hx6, hx7
|
108 |
+
del hx6d, hx5d, hx3d, hx2d
|
109 |
+
del hx2dup, hx3dup, hx4dup, hx5dup, hx6dup
|
110 |
+
"""
|
111 |
+
|
112 |
+
return hx1d + hxin
|
113 |
+
|
114 |
+
|
115 |
+
### RSU-6 ###
|
116 |
+
class RSU6(nn.Module): # UNet06DRES(nn.Module):
|
117 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
118 |
+
super(RSU6, self).__init__()
|
119 |
+
|
120 |
+
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
|
121 |
+
|
122 |
+
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
|
123 |
+
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
124 |
+
|
125 |
+
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
126 |
+
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
127 |
+
|
128 |
+
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
129 |
+
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
130 |
+
|
131 |
+
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
132 |
+
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
133 |
+
|
134 |
+
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
135 |
+
|
136 |
+
self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
|
137 |
+
|
138 |
+
self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
139 |
+
self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
140 |
+
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
141 |
+
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
142 |
+
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
|
143 |
+
|
144 |
+
def forward(self, x):
|
145 |
+
|
146 |
+
hx = x
|
147 |
+
|
148 |
+
hxin = self.rebnconvin(hx)
|
149 |
+
|
150 |
+
hx1 = self.rebnconv1(hxin)
|
151 |
+
hx = self.pool1(hx1)
|
152 |
+
|
153 |
+
hx2 = self.rebnconv2(hx)
|
154 |
+
hx = self.pool2(hx2)
|
155 |
+
|
156 |
+
hx3 = self.rebnconv3(hx)
|
157 |
+
hx = self.pool3(hx3)
|
158 |
+
|
159 |
+
hx4 = self.rebnconv4(hx)
|
160 |
+
hx = self.pool4(hx4)
|
161 |
+
|
162 |
+
hx5 = self.rebnconv5(hx)
|
163 |
+
|
164 |
+
hx6 = self.rebnconv6(hx5)
|
165 |
+
|
166 |
+
hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
|
167 |
+
hx5dup = _upsample_like(hx5d, hx4)
|
168 |
+
|
169 |
+
hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
|
170 |
+
hx4dup = _upsample_like(hx4d, hx3)
|
171 |
+
|
172 |
+
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
|
173 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
174 |
+
|
175 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
|
176 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
177 |
+
|
178 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
|
179 |
+
|
180 |
+
"""
|
181 |
+
del hx1, hx2, hx3, hx4, hx5, hx6
|
182 |
+
del hx5d, hx4d, hx3d, hx2d
|
183 |
+
del hx2dup, hx3dup, hx4dup, hx5dup
|
184 |
+
"""
|
185 |
+
|
186 |
+
return hx1d + hxin
|
187 |
+
|
188 |
+
|
189 |
+
### RSU-5 ###
|
190 |
+
class RSU5(nn.Module): # UNet05DRES(nn.Module):
|
191 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
192 |
+
super(RSU5, self).__init__()
|
193 |
+
|
194 |
+
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
|
195 |
+
|
196 |
+
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
|
197 |
+
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
198 |
+
|
199 |
+
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
200 |
+
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
201 |
+
|
202 |
+
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
203 |
+
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
204 |
+
|
205 |
+
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
206 |
+
|
207 |
+
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
|
208 |
+
|
209 |
+
self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
210 |
+
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
211 |
+
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
212 |
+
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
|
213 |
+
|
214 |
+
def forward(self, x):
|
215 |
+
|
216 |
+
hx = x
|
217 |
+
|
218 |
+
hxin = self.rebnconvin(hx)
|
219 |
+
|
220 |
+
hx1 = self.rebnconv1(hxin)
|
221 |
+
hx = self.pool1(hx1)
|
222 |
+
|
223 |
+
hx2 = self.rebnconv2(hx)
|
224 |
+
hx = self.pool2(hx2)
|
225 |
+
|
226 |
+
hx3 = self.rebnconv3(hx)
|
227 |
+
hx = self.pool3(hx3)
|
228 |
+
|
229 |
+
hx4 = self.rebnconv4(hx)
|
230 |
+
|
231 |
+
hx5 = self.rebnconv5(hx4)
|
232 |
+
|
233 |
+
hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
|
234 |
+
hx4dup = _upsample_like(hx4d, hx3)
|
235 |
+
|
236 |
+
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
|
237 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
238 |
+
|
239 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
|
240 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
241 |
+
|
242 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
|
243 |
+
|
244 |
+
"""
|
245 |
+
del hx1, hx2, hx3, hx4, hx5
|
246 |
+
del hx4d, hx3d, hx2d
|
247 |
+
del hx2dup, hx3dup, hx4dup
|
248 |
+
"""
|
249 |
+
|
250 |
+
return hx1d + hxin
|
251 |
+
|
252 |
+
|
253 |
+
### RSU-4 ###
|
254 |
+
class RSU4(nn.Module): # UNet04DRES(nn.Module):
|
255 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
256 |
+
super(RSU4, self).__init__()
|
257 |
+
|
258 |
+
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
|
259 |
+
|
260 |
+
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
|
261 |
+
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
262 |
+
|
263 |
+
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
264 |
+
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
265 |
+
|
266 |
+
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
|
267 |
+
|
268 |
+
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
|
269 |
+
|
270 |
+
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
271 |
+
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
|
272 |
+
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
|
273 |
+
|
274 |
+
def forward(self, x):
|
275 |
+
|
276 |
+
hx = x
|
277 |
+
|
278 |
+
hxin = self.rebnconvin(hx)
|
279 |
+
|
280 |
+
hx1 = self.rebnconv1(hxin)
|
281 |
+
hx = self.pool1(hx1)
|
282 |
+
|
283 |
+
hx2 = self.rebnconv2(hx)
|
284 |
+
hx = self.pool2(hx2)
|
285 |
+
|
286 |
+
hx3 = self.rebnconv3(hx)
|
287 |
+
|
288 |
+
hx4 = self.rebnconv4(hx3)
|
289 |
+
|
290 |
+
hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
|
291 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
292 |
+
|
293 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
|
294 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
295 |
+
|
296 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
|
297 |
+
|
298 |
+
"""
|
299 |
+
del hx1, hx2, hx3, hx4
|
300 |
+
del hx3d, hx2d
|
301 |
+
del hx2dup, hx3dup
|
302 |
+
"""
|
303 |
+
|
304 |
+
return hx1d + hxin
|
305 |
+
|
306 |
+
|
307 |
+
### RSU-4F ###
|
308 |
+
class RSU4F(nn.Module): # UNet04FRES(nn.Module):
|
309 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
310 |
+
super(RSU4F, self).__init__()
|
311 |
+
|
312 |
+
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
|
313 |
+
|
314 |
+
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
|
315 |
+
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
|
316 |
+
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
|
317 |
+
|
318 |
+
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
|
319 |
+
|
320 |
+
self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
|
321 |
+
self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
|
322 |
+
self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)
|
323 |
+
|
324 |
+
def forward(self, x):
|
325 |
+
|
326 |
+
hx = x
|
327 |
+
|
328 |
+
hxin = self.rebnconvin(hx)
|
329 |
+
|
330 |
+
hx1 = self.rebnconv1(hxin)
|
331 |
+
hx2 = self.rebnconv2(hx1)
|
332 |
+
hx3 = self.rebnconv3(hx2)
|
333 |
+
|
334 |
+
hx4 = self.rebnconv4(hx3)
|
335 |
+
|
336 |
+
hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
|
337 |
+
hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
|
338 |
+
hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
|
339 |
+
|
340 |
+
"""
|
341 |
+
del hx1, hx2, hx3, hx4
|
342 |
+
del hx3d, hx2d
|
343 |
+
"""
|
344 |
+
|
345 |
+
return hx1d + hxin
|
346 |
+
|
347 |
+
|
348 |
+
##### U^2-Net ####
|
349 |
+
class U2NET(nn.Module):
|
350 |
+
def __init__(self, in_ch=3, out_ch=1):
|
351 |
+
super(U2NET, self).__init__()
|
352 |
+
|
353 |
+
self.stage1 = RSU7(in_ch, 32, 64)
|
354 |
+
self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
355 |
+
|
356 |
+
self.stage2 = RSU6(64, 32, 128)
|
357 |
+
self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
358 |
+
|
359 |
+
self.stage3 = RSU5(128, 64, 256)
|
360 |
+
self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
361 |
+
|
362 |
+
self.stage4 = RSU4(256, 128, 512)
|
363 |
+
self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
364 |
+
|
365 |
+
self.stage5 = RSU4F(512, 256, 512)
|
366 |
+
self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
367 |
+
|
368 |
+
self.stage6 = RSU4F(512, 256, 512)
|
369 |
+
|
370 |
+
# decoder
|
371 |
+
self.stage5d = RSU4F(1024, 256, 512)
|
372 |
+
self.stage4d = RSU4(1024, 128, 256)
|
373 |
+
self.stage3d = RSU5(512, 64, 128)
|
374 |
+
self.stage2d = RSU6(256, 32, 64)
|
375 |
+
self.stage1d = RSU7(128, 16, 64)
|
376 |
+
|
377 |
+
self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
|
378 |
+
self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
|
379 |
+
self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
|
380 |
+
self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
|
381 |
+
self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
|
382 |
+
self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)
|
383 |
+
|
384 |
+
self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1)
|
385 |
+
|
386 |
+
def forward(self, x):
|
387 |
+
|
388 |
+
hx = x
|
389 |
+
|
390 |
+
# stage 1
|
391 |
+
hx1 = self.stage1(hx)
|
392 |
+
hx = self.pool12(hx1)
|
393 |
+
|
394 |
+
# stage 2
|
395 |
+
hx2 = self.stage2(hx)
|
396 |
+
hx = self.pool23(hx2)
|
397 |
+
|
398 |
+
# stage 3
|
399 |
+
hx3 = self.stage3(hx)
|
400 |
+
hx = self.pool34(hx3)
|
401 |
+
|
402 |
+
# stage 4
|
403 |
+
hx4 = self.stage4(hx)
|
404 |
+
hx = self.pool45(hx4)
|
405 |
+
|
406 |
+
# stage 5
|
407 |
+
hx5 = self.stage5(hx)
|
408 |
+
hx = self.pool56(hx5)
|
409 |
+
|
410 |
+
# stage 6
|
411 |
+
hx6 = self.stage6(hx)
|
412 |
+
hx6up = _upsample_like(hx6, hx5)
|
413 |
+
|
414 |
+
# -------------------- decoder --------------------
|
415 |
+
hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
|
416 |
+
hx5dup = _upsample_like(hx5d, hx4)
|
417 |
+
|
418 |
+
hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
|
419 |
+
hx4dup = _upsample_like(hx4d, hx3)
|
420 |
+
|
421 |
+
hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
|
422 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
423 |
+
|
424 |
+
hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
|
425 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
426 |
+
|
427 |
+
hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
|
428 |
+
|
429 |
+
# side output
|
430 |
+
d1 = self.side1(hx1d)
|
431 |
+
|
432 |
+
d2 = self.side2(hx2d)
|
433 |
+
d2 = _upsample_like(d2, d1)
|
434 |
+
|
435 |
+
d3 = self.side3(hx3d)
|
436 |
+
d3 = _upsample_like(d3, d1)
|
437 |
+
|
438 |
+
d4 = self.side4(hx4d)
|
439 |
+
d4 = _upsample_like(d4, d1)
|
440 |
+
|
441 |
+
d5 = self.side5(hx5d)
|
442 |
+
d5 = _upsample_like(d5, d1)
|
443 |
+
|
444 |
+
d6 = self.side6(hx6)
|
445 |
+
d6 = _upsample_like(d6, d1)
|
446 |
+
|
447 |
+
d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
|
448 |
+
|
449 |
+
"""
|
450 |
+
del hx1, hx2, hx3, hx4, hx5, hx6
|
451 |
+
del hx5d, hx4d, hx3d, hx2d, hx1d
|
452 |
+
del hx6up, hx5dup, hx4dup, hx3dup, hx2dup
|
453 |
+
"""
|
454 |
+
|
455 |
+
return d0, d1, d2, d3, d4, d5, d6
|
456 |
+
|
457 |
+
|
458 |
+
### U^2-Net small ###
|
459 |
+
class U2NETP(nn.Module):
|
460 |
+
def __init__(self, in_ch=3, out_ch=1):
|
461 |
+
super(U2NETP, self).__init__()
|
462 |
+
|
463 |
+
self.stage1 = RSU7(in_ch, 16, 64)
|
464 |
+
self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
465 |
+
|
466 |
+
self.stage2 = RSU6(64, 16, 64)
|
467 |
+
self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
468 |
+
|
469 |
+
self.stage3 = RSU5(64, 16, 64)
|
470 |
+
self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
471 |
+
|
472 |
+
self.stage4 = RSU4(64, 16, 64)
|
473 |
+
self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
474 |
+
|
475 |
+
self.stage5 = RSU4F(64, 16, 64)
|
476 |
+
self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
|
477 |
+
|
478 |
+
self.stage6 = RSU4F(64, 16, 64)
|
479 |
+
|
480 |
+
# decoder
|
481 |
+
self.stage5d = RSU4F(128, 16, 64)
|
482 |
+
self.stage4d = RSU4(128, 16, 64)
|
483 |
+
self.stage3d = RSU5(128, 16, 64)
|
484 |
+
self.stage2d = RSU6(128, 16, 64)
|
485 |
+
self.stage1d = RSU7(128, 16, 64)
|
486 |
+
|
487 |
+
self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
|
488 |
+
self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
|
489 |
+
self.side3 = nn.Conv2d(64, out_ch, 3, padding=1)
|
490 |
+
self.side4 = nn.Conv2d(64, out_ch, 3, padding=1)
|
491 |
+
self.side5 = nn.Conv2d(64, out_ch, 3, padding=1)
|
492 |
+
self.side6 = nn.Conv2d(64, out_ch, 3, padding=1)
|
493 |
+
|
494 |
+
self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1)
|
495 |
+
|
496 |
+
def forward(self, x):
|
497 |
+
|
498 |
+
hx = x
|
499 |
+
|
500 |
+
# stage 1
|
501 |
+
hx1 = self.stage1(hx)
|
502 |
+
hx = self.pool12(hx1)
|
503 |
+
|
504 |
+
# stage 2
|
505 |
+
hx2 = self.stage2(hx)
|
506 |
+
hx = self.pool23(hx2)
|
507 |
+
|
508 |
+
# stage 3
|
509 |
+
hx3 = self.stage3(hx)
|
510 |
+
hx = self.pool34(hx3)
|
511 |
+
|
512 |
+
# stage 4
|
513 |
+
hx4 = self.stage4(hx)
|
514 |
+
hx = self.pool45(hx4)
|
515 |
+
|
516 |
+
# stage 5
|
517 |
+
hx5 = self.stage5(hx)
|
518 |
+
hx = self.pool56(hx5)
|
519 |
+
|
520 |
+
# stage 6
|
521 |
+
hx6 = self.stage6(hx)
|
522 |
+
hx6up = _upsample_like(hx6, hx5)
|
523 |
+
|
524 |
+
# decoder
|
525 |
+
hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
|
526 |
+
hx5dup = _upsample_like(hx5d, hx4)
|
527 |
+
|
528 |
+
hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
|
529 |
+
hx4dup = _upsample_like(hx4d, hx3)
|
530 |
+
|
531 |
+
hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
|
532 |
+
hx3dup = _upsample_like(hx3d, hx2)
|
533 |
+
|
534 |
+
hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
|
535 |
+
hx2dup = _upsample_like(hx2d, hx1)
|
536 |
+
|
537 |
+
hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
|
538 |
+
|
539 |
+
# side output
|
540 |
+
d1 = self.side1(hx1d)
|
541 |
+
|
542 |
+
d2 = self.side2(hx2d)
|
543 |
+
d2 = _upsample_like(d2, d1)
|
544 |
+
|
545 |
+
d3 = self.side3(hx3d)
|
546 |
+
d3 = _upsample_like(d3, d1)
|
547 |
+
|
548 |
+
d4 = self.side4(hx4d)
|
549 |
+
d4 = _upsample_like(d4, d1)
|
550 |
+
|
551 |
+
d5 = self.side5(hx5d)
|
552 |
+
d5 = _upsample_like(d5, d1)
|
553 |
+
|
554 |
+
d6 = self.side6(hx6)
|
555 |
+
d6 = _upsample_like(d6, d1)
|
556 |
+
|
557 |
+
d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
|
558 |
+
|
559 |
+
|
560 |
+
return d0, d1, d2, d3, d4, d5, d6
|
app/huggingface-cloth-segmentation/options.py
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os.path as osp
|
2 |
+
import os
|
3 |
+
|
4 |
+
|
5 |
+
class parser(object):
|
6 |
+
def __init__(self):
|
7 |
+
|
8 |
+
self.output = "./output" # output image folder path
|
9 |
+
self.logs_dir = './logs'
|
10 |
+
self.device = 'cuda:0'
|
11 |
+
|
12 |
+
opt = parser()
|
app/huggingface-cloth-segmentation/output/alpha/1.png
ADDED
![]() |
app/huggingface-cloth-segmentation/output/alpha/3.png
ADDED
![]() |
app/huggingface-cloth-segmentation/output/cloth_seg/final_seg.png
ADDED
![]() |
app/huggingface-cloth-segmentation/process.py
ADDED
@@ -0,0 +1,190 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from network import U2NET
|
2 |
+
|
3 |
+
import os
|
4 |
+
from PIL import Image
|
5 |
+
import cv2
|
6 |
+
import gdown
|
7 |
+
import argparse
|
8 |
+
import numpy as np
|
9 |
+
|
10 |
+
import torch
|
11 |
+
import torch.nn.functional as F
|
12 |
+
import torchvision.transforms as transforms
|
13 |
+
|
14 |
+
from collections import OrderedDict
|
15 |
+
from options import opt
|
16 |
+
|
17 |
+
|
18 |
+
def load_checkpoint(model, checkpoint_path):
|
19 |
+
if not os.path.exists(checkpoint_path):
|
20 |
+
print("----No checkpoints at given path----")
|
21 |
+
return
|
22 |
+
model_state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))
|
23 |
+
new_state_dict = OrderedDict()
|
24 |
+
for k, v in model_state_dict.items():
|
25 |
+
name = k[7:] # remove `module.`
|
26 |
+
new_state_dict[name] = v
|
27 |
+
|
28 |
+
model.load_state_dict(new_state_dict)
|
29 |
+
print("----checkpoints loaded from path: {}----".format(checkpoint_path))
|
30 |
+
return model
|
31 |
+
|
32 |
+
|
33 |
+
def get_palette(num_cls):
|
34 |
+
""" Returns the color map for visualizing the segmentation mask.
|
35 |
+
Args:
|
36 |
+
num_cls: Number of classes
|
37 |
+
Returns:
|
38 |
+
The color map
|
39 |
+
"""
|
40 |
+
n = num_cls
|
41 |
+
palette = [0] * (n * 3)
|
42 |
+
for j in range(0, n):
|
43 |
+
lab = j
|
44 |
+
palette[j * 3 + 0] = 0
|
45 |
+
palette[j * 3 + 1] = 0
|
46 |
+
palette[j * 3 + 2] = 0
|
47 |
+
i = 0
|
48 |
+
while lab:
|
49 |
+
palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
|
50 |
+
palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
|
51 |
+
palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
|
52 |
+
i += 1
|
53 |
+
lab >>= 3
|
54 |
+
return palette
|
55 |
+
|
56 |
+
|
57 |
+
class Normalize_image(object):
|
58 |
+
"""Normalize given tensor into given mean and standard dev
|
59 |
+
|
60 |
+
Args:
|
61 |
+
mean (float): Desired mean to substract from tensors
|
62 |
+
std (float): Desired std to divide from tensors
|
63 |
+
"""
|
64 |
+
|
65 |
+
def __init__(self, mean, std):
|
66 |
+
assert isinstance(mean, (float))
|
67 |
+
if isinstance(mean, float):
|
68 |
+
self.mean = mean
|
69 |
+
|
70 |
+
if isinstance(std, float):
|
71 |
+
self.std = std
|
72 |
+
|
73 |
+
self.normalize_1 = transforms.Normalize(self.mean, self.std)
|
74 |
+
self.normalize_3 = transforms.Normalize([self.mean] * 3, [self.std] * 3)
|
75 |
+
self.normalize_18 = transforms.Normalize([self.mean] * 18, [self.std] * 18)
|
76 |
+
|
77 |
+
def __call__(self, image_tensor):
|
78 |
+
if image_tensor.shape[0] == 1:
|
79 |
+
return self.normalize_1(image_tensor)
|
80 |
+
|
81 |
+
elif image_tensor.shape[0] == 3:
|
82 |
+
return self.normalize_3(image_tensor)
|
83 |
+
|
84 |
+
elif image_tensor.shape[0] == 18:
|
85 |
+
return self.normalize_18(image_tensor)
|
86 |
+
|
87 |
+
else:
|
88 |
+
assert "Please set proper channels! Normlization implemented only for 1, 3 and 18"
|
89 |
+
|
90 |
+
|
91 |
+
|
92 |
+
|
93 |
+
def apply_transform(img):
|
94 |
+
transforms_list = []
|
95 |
+
transforms_list += [transforms.ToTensor()]
|
96 |
+
transforms_list += [Normalize_image(0.5, 0.5)]
|
97 |
+
transform_rgb = transforms.Compose(transforms_list)
|
98 |
+
return transform_rgb(img)
|
99 |
+
|
100 |
+
|
101 |
+
|
102 |
+
def generate_mask(input_image, net, palette, device = 'cpu'):
|
103 |
+
|
104 |
+
#img = Image.open(input_image).convert('RGB')
|
105 |
+
img = input_image
|
106 |
+
img_size = img.size
|
107 |
+
img = img.resize((768, 768), Image.BICUBIC)
|
108 |
+
image_tensor = apply_transform(img)
|
109 |
+
image_tensor = torch.unsqueeze(image_tensor, 0)
|
110 |
+
|
111 |
+
alpha_out_dir = os.path.join(opt.output,'alpha')
|
112 |
+
cloth_seg_out_dir = os.path.join(opt.output,'cloth_seg')
|
113 |
+
|
114 |
+
os.makedirs(alpha_out_dir, exist_ok=True)
|
115 |
+
os.makedirs(cloth_seg_out_dir, exist_ok=True)
|
116 |
+
|
117 |
+
with torch.no_grad():
|
118 |
+
output_tensor = net(image_tensor.to(device))
|
119 |
+
output_tensor = F.log_softmax(output_tensor[0], dim=1)
|
120 |
+
output_tensor = torch.max(output_tensor, dim=1, keepdim=True)[1]
|
121 |
+
output_tensor = torch.squeeze(output_tensor, dim=0)
|
122 |
+
output_arr = output_tensor.cpu().numpy()
|
123 |
+
|
124 |
+
classes_to_save = []
|
125 |
+
|
126 |
+
# Check which classes are present in the image
|
127 |
+
for cls in range(1, 4): # Exclude background class (0)
|
128 |
+
if np.any(output_arr == cls):
|
129 |
+
classes_to_save.append(cls)
|
130 |
+
|
131 |
+
# Save alpha masks
|
132 |
+
for cls in classes_to_save:
|
133 |
+
alpha_mask = (output_arr == cls).astype(np.uint8) * 255
|
134 |
+
alpha_mask = alpha_mask[0] # Selecting the first channel to make it 2D
|
135 |
+
alpha_mask_img = Image.fromarray(alpha_mask, mode='L')
|
136 |
+
alpha_mask_img = alpha_mask_img.resize(img_size, Image.BICUBIC)
|
137 |
+
alpha_mask_img.save(os.path.join(alpha_out_dir, f'{cls}.png'))
|
138 |
+
|
139 |
+
# Save final cloth segmentations
|
140 |
+
cloth_seg = Image.fromarray(output_arr[0].astype(np.uint8), mode='P')
|
141 |
+
cloth_seg.putpalette(palette)
|
142 |
+
cloth_seg = cloth_seg.resize(img_size, Image.BICUBIC)
|
143 |
+
cloth_seg.save(os.path.join(cloth_seg_out_dir, 'final_seg.png'))
|
144 |
+
return cloth_seg
|
145 |
+
|
146 |
+
|
147 |
+
|
148 |
+
def check_or_download_model(file_path):
|
149 |
+
if not os.path.exists(file_path):
|
150 |
+
os.makedirs(os.path.dirname(file_path), exist_ok=True)
|
151 |
+
url = "https://drive.google.com/uc?id=11xTBALOeUkyuaK3l60CpkYHLTmv7k3dY"
|
152 |
+
gdown.download(url, file_path, quiet=False)
|
153 |
+
print("Model downloaded successfully.")
|
154 |
+
else:
|
155 |
+
print("Model already exists.")
|
156 |
+
|
157 |
+
|
158 |
+
def load_seg_model(checkpoint_path, device='cpu'):
|
159 |
+
net = U2NET(in_ch=3, out_ch=4)
|
160 |
+
check_or_download_model(checkpoint_path)
|
161 |
+
net = load_checkpoint(net, checkpoint_path)
|
162 |
+
net = net.to(device)
|
163 |
+
net = net.eval()
|
164 |
+
|
165 |
+
return net
|
166 |
+
|
167 |
+
|
168 |
+
def main(args):
|
169 |
+
|
170 |
+
device = 'cuda:0' if args.cuda else 'cpu'
|
171 |
+
|
172 |
+
# Create an instance of your model
|
173 |
+
model = load_seg_model(args.checkpoint_path, device=device)
|
174 |
+
|
175 |
+
palette = get_palette(4)
|
176 |
+
|
177 |
+
img = Image.open(args.image).convert('RGB')
|
178 |
+
|
179 |
+
cloth_seg = generate_mask(img, net=model, palette=palette, device=device)
|
180 |
+
|
181 |
+
|
182 |
+
|
183 |
+
if __name__ == '__main__':
|
184 |
+
parser = argparse.ArgumentParser(description='Help to set arguments for Cloth Segmentation.')
|
185 |
+
parser.add_argument('--image', type=str, help='Path to the input image')
|
186 |
+
parser.add_argument('--cuda', action='store_true', help='Enable CUDA (default: False)')
|
187 |
+
parser.add_argument('--checkpoint_path', type=str, default='model/cloth_segm.pth', help='Path to the checkpoint file')
|
188 |
+
args = parser.parse_args()
|
189 |
+
|
190 |
+
main(args)
|
app/huggingface-cloth-segmentation/requirements.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
torch
|
2 |
+
torchvision
|
3 |
+
gradio
|
4 |
+
gdown
|
5 |
+
Pillow
|
6 |
+
opencv-python
|
7 |
+
numpy
|
app/main.py
ADDED
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from flask import Flask, request, jsonify, send_from_directory
|
2 |
+
from flask_cors import CORS
|
3 |
+
from genai import gen_vton
|
4 |
+
from werkzeug.utils import secure_filename
|
5 |
+
import os
|
6 |
+
import tempfile
|
7 |
+
|
8 |
+
#app = Flask(__name__)
|
9 |
+
|
10 |
+
app = Flask(__name__, static_folder='processed_images')
|
11 |
+
|
12 |
+
CORS(app, supports_credentials=True)
|
13 |
+
#CORS(app, supports_credentials=True, resources={r"/*": {"origins": "*"}}) # Allow requests from any originorigins=["http://localhost:3000"])
|
14 |
+
|
15 |
+
#CORS(app, resources={r"/proc": {"origins": "http://localhost:3000"}}, supports_credentials=True)
|
16 |
+
#@app.route("/proc")
|
17 |
+
@app.route('/proc', methods=['POST'])
|
18 |
+
def process_images():
|
19 |
+
# Retrieve images from the request
|
20 |
+
print("Request came here")
|
21 |
+
print(request)
|
22 |
+
print(request.headers)
|
23 |
+
print(request.files)
|
24 |
+
|
25 |
+
|
26 |
+
user_image_t = request.files.get('userImage')
|
27 |
+
dress_image_t = request.files.get('dressImage')
|
28 |
+
#print(dress_image_t.filename)
|
29 |
+
print(user_image_t.filename)
|
30 |
+
#file = request.files['file']
|
31 |
+
if dress_image_t:
|
32 |
+
# Save the file to a temporary file
|
33 |
+
temp_dir = tempfile.gettempdir()
|
34 |
+
filename = secure_filename(dress_image_t.filename)
|
35 |
+
temp_path = os.path.join(temp_dir, filename)
|
36 |
+
dress_image_t.save(temp_path)
|
37 |
+
dress_image = temp_path
|
38 |
+
if user_image_t:
|
39 |
+
temp_dir = tempfile.gettempdir()
|
40 |
+
filename = secure_filename(user_image_t.filename)
|
41 |
+
temp_path_1 = os.path.join(temp_dir, filename)
|
42 |
+
user_image_t.save(temp_path_1)
|
43 |
+
user_image = temp_path_1
|
44 |
+
|
45 |
+
gen_vton(user_image, dress_image)
|
46 |
+
processed_image_1_path = './processed_images/output_image.jpg'
|
47 |
+
processed_image_2_path = './processed_images/output_image_1.jpg'
|
48 |
+
|
49 |
+
# Save your images using the paths above...
|
50 |
+
|
51 |
+
# Return the URL for the saved images
|
52 |
+
url_to_processed_image_1 = request.host_url + processed_image_1_path
|
53 |
+
url_to_processed_image_2 = request.host_url + processed_image_2_path
|
54 |
+
# Process images...
|
55 |
+
# For the sake of this example, let's say the processing function returns two image URLs
|
56 |
+
processed_image_urls = [url_to_processed_image_1, url_to_processed_image_2]
|
57 |
+
os.remove(temp_path)
|
58 |
+
os.remove(temp_path_1)
|
59 |
+
return jsonify({'processedImages': processed_image_urls})
|
60 |
+
|
61 |
+
@app.route('/processed_images/<filename>')
|
62 |
+
def processed_images(filename):
|
63 |
+
print("request_came_here")
|
64 |
+
return send_from_directory(app.static_folder, filename)
|
65 |
+
# Example of generating a unique filename for the output
|
66 |
+
|
67 |
+
|
68 |
+
#
|
69 |
+
|
70 |
+
if __name__ == '__main__':
|
71 |
+
app.run(debug=True, host='0.0.0.0')
|
app/model/cloth_segm.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f71fad2bc11789a996acc507d1a5a1602ae0edefc2b9aba1cd198be5cc9c1a44
|
3 |
+
size 176625341
|
app/output/alpha/1.png
ADDED
![]() |
app/output/alpha/2.png
ADDED
![]() |
app/output/alpha/3.png
ADDED
![]() |
app/output/cloth_seg/final_seg.png
ADDED
![]() |
app/output_image.jpg
ADDED
![]() |
app/output_image_1.jpg
ADDED
![]() |
app/output_image_2.jpg
ADDED
![]() |
app/output_image_3.jpg
ADDED
![]() |
app/output_image_4.jpg
ADDED
![]() |
app/processed_images/output_image.jpg
ADDED
![]() |
app/processed_images/output_image_1.jpg
ADDED
![]() |