# -*- coding: utf-8 -*- """With os FASHION-EYE_VITON-HD Integrated Full Model Final.ipynb Automatically generated by Colaboratory. """ # !rm -rf sample_data # !rm -rf fashion-eye-try-on/ BASE_DIR = "/home/user/app/fashion-eye-try-on" import os os.system(f"git clone https://huggingface.co/spaces/sidharthism/fashion-eye-try-on {BASE_DIR}") # !pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 # !pip install -r /content/fashion-eye-try-on/requirements.txt os.system("pip install torch>=1.6.0 torchvision -f https://download.pytorch.org/whl/cu92/torch_stable.html") os.system("pip install opencv-python torchgeometry gdown Pillow") os.system(f"cd {BASE_DIR}") # Download and save checkpoints for cloth mask generation os.system(f"rm -rf {BASE_DIR}/cloth_segmentation/checkpoints/") os.system(f"gdown --id 1mhF3yqd7R-Uje092eypktNl-RoZNuiCJ -O {BASE_DIR}/cloth_segmentation/checkpoints/") os.system(f"git clone https://github.com/shadow2496/VITON-HD {BASE_DIR}/VITON-HD") #checkpoints os.system(f"gdown 1RM4OthSM6V4r7kWCu8SbPIPY14Oz8B2u -O {BASE_DIR}/VITON-HD/checkpoints/alias_final.pth") os.system(f"gdown 1MBHBddaAs7sy8W40jzLmNL83AUh035F1 -O {BASE_DIR}/VITON-HD/checkpoints/gmm_final.pth") os.system(f"gdown 1MBHBddaAs7sy8W40jzLmNL83AUh035F1 -O {BASE_DIR}/VITON-HD/checkpoints/gmm_final.pth") os.system(f"gdown 17U1sooR3mVIbe8a7rZuFIF3kukPchHfZ -O {BASE_DIR}/VITON-HD/checkpoints/seg_final.pth") #test data os.system(f"gdown 1ncEHn_6liOot8sgt3A2DOFJBffvx8tW8 -O {BASE_DIR}/VITON-HD/datasets/test_pairs.txt") os.system(f"gdown 1ZA2C8yMOprwc0TV4hvrt0X-ljZugrClq -O {BASE_DIR}/VITON-HD/datasets/test.zip") os.system(f"unzip {BASE_DIR}/VITON-HD/datasets/test.zip -d {BASE_DIR}/VITON-HD/datasets/") #@title To clear all the already existing test data # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/image # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/image-parse # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/cloth # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/cloth-mask # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/openpose-img # !rm -rf /content/fashion-eye-try-on/VITON-HD/datasets/test/openpose-json """Paddle """ os.system(f"git clone https://huggingface.co/spaces/sidharthism/pipeline_paddle {BASE_DIR}/pipeline_paddle") # Required for paddle and gradio (Jinja2 dependency) os.system("pip install paddlepaddle-gpu pymatting") os.system(f"pip install -r {BASE_DIR}/pipeline_paddle/requirements.txt") os.system(f"rm -rf {BASE_DIR}/pipeline_paddle/models") if not os.path.exists(f"{BASE_DIR}/pipeline_paddle/models/ppmatting-hrnet_w18-human_1024.pdparams"): if not os.path.exists(f"{BASE_DIR}/pipeline_paddle/models"): os.mkdir(f"{BASE_DIR}/pipeline_paddle/models") os.system(f"wget https://paddleseg.bj.bcebos.com/matting/models/ppmatting-hrnet_w18-human_1024.pdparams -O {BASE_DIR}/pipeline_paddle/models/ppmatting-hrnet_w18-human_1024.pdparams") # !wget "https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz" -O "/content/fashion-eye-try-on/pipeline_paddle/models/hrnet_w18_ssld.tar.gz" """Initialization Pose estimator - open pose """ # Clone openpose model repo # os.system(f"git clone https://github.com/CMU-Perceptual-Computing-Lab/openpose.git {BASE_DIR}/openpose") #@ Building and Installation of openpose model import os import subprocess from os.path import exists, join, basename, splitext project_name = f"{BASE_DIR}/openpose" print(project_name) if not exists(project_name): # see: https://github.com/CMU-Perceptual-Computing-Lab/openpose/issues/949 # install new CMake becaue of CUDA10 os.system(f"wget -q https://cmake.org/files/v3.13/cmake-3.13.0-Linux-x86_64.tar.gz") os.system(f"sudo tar xfz cmake-3.13.0-Linux-x86_64.tar.gz --strip-components=1 -C /usr/local") # clone openpose os.system(f"cd {BASE_DIR} && git clone -q --depth 1 https://github.com/CMU-Perceptual-Computing-Lab/openpose.git") os.system("sudo sed -i 's/execute_process(COMMAND git checkout master WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}\/3rdparty\/caffe)/execute_process(COMMAND git checkout f019d0dfe86f49d1140961f8c7dec22130c83154 WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}\/3rdparty\/caffe)/g' %s/openpose/CMakeLists.txt" % (BASE_DIR, )) # install system dependencies os.system("sudo apt-get -qq install -y libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler libgflags-dev libgoogle-glog-dev liblmdb-dev opencl-headers ocl-icd-opencl-dev libviennacl-dev") # build openpose print("Building openpose ... May take nearly 15 mins to build ...") os.system(f"sudo cd {BASE_DIR}/openpose && rm -rf {BASE_DIR}/openpose/build || true && mkdir {BASE_DIR}/openpose/build && cd {BASE_DIR}/openpose/build && cmake .. && make -j`nproc`") print("Openpose successfully build and installed.") # subprocess.Popen(f"cd {BASE_DIR}/openpose && rm -rf {BASE_DIR}/openpose/build || true && mkdir {BASE_DIR}/openpose/build && cd {BASE_DIR}/openpose/build && cmake .. && make -j`nproc`") # subprocess.call(["cd", f"{BASE_DIR}/openpose"]) # subprocess.check_output(["rm", "-rf", f"{BASE_DIR}/openpose/build || true"]) # subprocess.check_output(["mkdir", f"{BASE_DIR}/openpose/build"]) # subprocess.check_output(["cd", f"{BASE_DIR}/openpose/build"]) # subprocess.check_output(["cmake", ".."]) # subprocess.check_output(["make","-j`nproc`"]) # !cd {BASE_DIR}/openpose && rm -rf {BASE_DIR}/openpose/build || true && mkdir {BASE_DIR}/openpose/build && cd {BASE_DIR}/openpose/build && cmake .. && make -j`nproc` """Self correction human parsing""" os.system(f"git clone https://github.com/PeikeLi/Self-Correction-Human-Parsing.git {BASE_DIR}/human_parse") os.system(f"cd {BASE_DIR}/human_parse") os.system(f"mkdir {BASE_DIR}/human_parse/checkpoints") # !mkdir inputs # !mkdir outputs dataset = 'lip' import gdown dataset_url = 'https://drive.google.com/uc?id=1k4dllHpu0bdx38J7H28rVVLpU-kOHmnH' output = f'{BASE_DIR}/human_parse/checkpoints/final.pth' gdown.download(dataset_url, output, quiet=False) # For human parse os.system("pip install ninja") """Preprocessing """ # png to jpg def convert_to_jpg(path): from PIL import Image import os if os.path.exists(path): cl = Image.open(path) jpg_path = path[:-4] + ".jpg" cl.save(jpg_path) def resize_img(path): from PIL import Image print(path) im = Image.open(path) im = im.resize((768, 1024), Image.BICUBIC) im.save(path) def remove_ipynb_checkpoints(): import os os.system(f"rm -rf {BASE_DIR}/VITON-HD/datasets/test/image/.ipynb_checkpoints") os.system(f"rm -rf {BASE_DIR}/VITON-HD/datasets/test/cloth/.ipynb_checkpoints") os.system(f"rm -rf {BASE_DIR}/VITON-HD/datasets/test/cloth-mask/.ipynb_checkpoints") # os.chdir('/content/fashion-eye-try-on') def preprocess(): remove_ipynb_checkpoints() for path in os.listdir(f'{BASE_DIR}/VITON-HD/datasets/test/image/'): resize_img(f'{BASE_DIR}/VITON-HD/datasets/test/image/{path}') for path in os.listdir(f'{BASE_DIR}/VITON-HD/datasets/test/cloth/'): resize_img(f'{BASE_DIR}/VITON-HD/datasets/test/cloth/{path}') # for path in os.listdir('/content/fashion-eye-try-on/VITON-HD/datasets/test/cloth-mask/'): # resize_img(f'/content/fashion-eye-try-on/VITON-HD/datasets/test/cloth-mask/{path}') """Paddle - removing background """ # PPMatting hrnet 1024 # --fg_estimate True - for higher quality output but slower prediction def upload_remove_background_and_save_person_image(person_img): # !export CUDA_VISIBLE_DEVICES=0 person_img = person_img.resize((768, 1024), Image.BICUBIC) if os.path.exists(f"{BASE_DIR}/pipeline_paddle/image/person.jpg"): os.remove(f"{BASE_DIR}/pipeline_paddle/image/person.jpg") person_img.save(f"{BASE_DIR}/pipeline_paddle/image/person.jpg") # resize_img(f'/content/fashion-eye-try-on/pipeline_paddle/image/person.jpg') os.system(f"cd {BASE_DIR}/pipeline_paddle/") os.system(f"python {BASE_DIR}/pipeline_paddle/bg_replace.py \ --config {BASE_DIR}/pipeline_paddle/configs/ppmatting/ppmatting-hrnet_w18-human_1024.yml \ --model_path {BASE_DIR}/pipeline_paddle/models/ppmatting-hrnet_w18-human_1024.pdparams \ --image_path {BASE_DIR}/pipeline_paddle/image/person.jpg \ --background 'w' \ --save_dir {BASE_DIR}/VITON-HD/datasets/test/image \ --fg_estimate True") # --save_dir /content/fashion-eye-try-on/pipeline_paddle/output \ try: convert_to_jpg(f"{BASE_DIR}/VITON-HD/datasets/test/image/person.png") # os.remove("/content/fashion-eye-try-on/pipeline_paddle/output/person_alpha.png") os.remove(f"{BASE_DIR}/VITON-HD/datasets/test/image/person_alpha.png") # os.remove("/content/fashion-eye-try-on/pipeline_paddle/output/person_rgba.png") os.remove(f"{BASE_DIR}/VITON-HD/datasets/test/image/person_rgba.png") os.system(f"cd {BASE_DIR}") except Exception as e: print(e) os.system(f"cd {BASE_DIR}") #@title If multiple GPU available,uncomment and try this code os.system("export CUDA_VISIBLE_DEVICES=0") # Openpose pose estimation # Ubuntu and Mac def estimate_pose(): os.system(f"cd {BASE_DIR}/openpose && ./build/examples/openpose/openpose.bin --image_dir {BASE_DIR}/VITON-HD/datasets/test/image --write_json {BASE_DIR}/VITON-HD/datasets/test/openpose-json/ --display 0 --face --hand --render_pose 0") os.system(f"cd {BASE_DIR}/openpose && ./build/examples/openpose/openpose.bin --image_dir {BASE_DIR}/VITON-HD/datasets/test/image --write_images {BASE_DIR}/VITON-HD/datasets/test/openpose-img/ --display 0 --hand --render_pose 1 --disable_blending true") os.system(f"cd {BASE_DIR}") # !cd /content/fashion-eye-try-on/openpose && ./build/examples/openpose/openpose.bin --image_dir /content/fashion-eye-try-on/pipeline_paddle/output/ --write_images /content/fashion-eye-try-on/openpose_img/ --display 0 --hand --render_pose 1 --disable_blending true # Run self correction human parser # !python3 /content/fashion-eye-try-on/human_parse/simple_extractor.py --dataset 'lip' --model-restore '/content/fashion-eye-try-on/human_parse/checkpoints/final.pth' --input-dir '/content/fashion-eye-try-on/image' --output-dir '/content/fashion-eye-try-on/VITON-HD/datasets/test/image-parse' def generate_human_segmentation_map(): # remove_ipynb_checkpoints() os.system(f"python3 {BASE_DIR}/human_parse/simple_extractor.py --dataset 'lip' --model-restore '{BASE_DIR}/human_parse/checkpoints/final.pth' --input-dir '{BASE_DIR}/VITON-HD/datasets/test/image' --output-dir '{BASE_DIR}/VITON-HD/datasets/test/image-parse'") # model_image = os.listdir('/content/fashion-eye-try-on/VITON-HD/datasets/test/image') # cloth_image = os.listdir('/content/fashion-eye-try-on/VITON-HD/datasets/test/cloth') # pairs = zip(model_image, cloth_image) # with open('/content/fashion-eye-try-on/VITON-HD/datasets/test_pairs.txt', 'w') as file: # for model, cloth in pairs: # file.write(f"{model} {cloth}\n") def generate_test_pairs_txt(): with open(f"{BASE_DIR}/VITON-HD/datasets/test_pairs.txt", 'w') as file: file.write(f"person.jpg cloth.jpg\n") # VITON-HD # Transfer the cloth to the model def generate_viton_hd(): os.system(f"python {BASE_DIR}/VITON-HD/test.py --name output --dataset_list {BASE_DIR}/VITON-HD/datasets/test_pairs.txt --dataset_dir {BASE_DIR}/VITON-HD/datasets/ --checkpoint_dir {BASE_DIR}/VITON-HD/checkpoints --save_dir {BASE_DIR}/") import sys # To resolve ModuleNotFoundError during imports if BASE_DIR not in sys.path: sys.path.append(BASE_DIR) sys.path.append(f"{BASE_DIR}/cloth_segmentation") from cloth_segmentation.networks import U2NET import torchvision.transforms as transforms import torch.nn.functional as F import os from PIL import Image from collections import OrderedDict import torch device = 'cuda' if torch.cuda.is_available() else "cpu" if device == 'cuda': torch.cuda.empty_cache() # for hugging face # BASE_DIR = "/home/path/app" image_dir = 'cloth' result_dir = 'cloth_mask' checkpoint_path = 'cloth_segmentation/checkpoints/cloth_segm_u2net_latest.pth' def load_checkpoint_mgpu(model, checkpoint_path): if not os.path.exists(checkpoint_path): print("----No checkpoints at given path----") return model_state_dict = torch.load( checkpoint_path, map_location=torch.device("cpu")) new_state_dict = OrderedDict() for k, v in model_state_dict.items(): name = k[7:] # remove `module.` new_state_dict[name] = v model.load_state_dict(new_state_dict) print("----checkpoints loaded from path: {}----".format(checkpoint_path)) return model class Normalize_image(object): """Normalize given tensor into given mean and standard dev Args: mean (float): Desired mean to substract from tensors std (float): Desired std to divide from tensors """ def __init__(self, mean, std): assert isinstance(mean, (float)) if isinstance(mean, float): self.mean = mean if isinstance(std, float): self.std = std self.normalize_1 = transforms.Normalize(self.mean, self.std) self.normalize_3 = transforms.Normalize( [self.mean] * 3, [self.std] * 3) self.normalize_18 = transforms.Normalize( [self.mean] * 18, [self.std] * 18) def __call__(self, image_tensor): if image_tensor.shape[0] == 1: return self.normalize_1(image_tensor) elif image_tensor.shape[0] == 3: return self.normalize_3(image_tensor) elif image_tensor.shape[0] == 18: return self.normalize_18(image_tensor) else: assert "Please set proper channels! Normlization implemented only for 1, 3 and 18" def get_palette(num_cls): """ Returns the color map for visualizing the segmentation mask. Args: num_cls: Number of classes Returns: The color map """ n = num_cls palette = [0] * (n * 3) for j in range(0, n): lab = j palette[j * 3 + 0] = 0 palette[j * 3 + 1] = 0 palette[j * 3 + 2] = 0 i = 0 while lab: palette[j * 3 + 0] = 255 palette[j * 3 + 1] = 255 palette[j * 3 + 2] = 255 # palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i)) # palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i)) # palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i)) i += 1 lab >>= 3 return palette def generate_cloth_mask(img_dir, output_dir, chkpt_dir): global image_dir global result_dir global checkpoint_path image_dir = img_dir result_dir = output_dir checkpoint_path = chkpt_dir transforms_list = [] transforms_list += [transforms.ToTensor()] transforms_list += [Normalize_image(0.5, 0.5)] transform_rgb = transforms.Compose(transforms_list) net = U2NET(in_ch=3, out_ch=4) with torch.no_grad(): net = load_checkpoint_mgpu(net, checkpoint_path) net = net.to(device) net = net.eval() palette = get_palette(4) images_list = sorted(os.listdir(image_dir)) for image_name in images_list: img = Image.open(os.path.join( image_dir, image_name)).convert('RGB') img_size = img.size img = img.resize((768, 768), Image.BICUBIC) image_tensor = transform_rgb(img) image_tensor = torch.unsqueeze(image_tensor, 0) output_tensor = net(image_tensor.to(device)) output_tensor = F.log_softmax(output_tensor[0], dim=1) output_tensor = torch.max(output_tensor, dim=1, keepdim=True)[1] output_tensor = torch.squeeze(output_tensor, dim=0) output_tensor = torch.squeeze(output_tensor, dim=0) output_arr = output_tensor.cpu().numpy() output_img = Image.fromarray(output_arr.astype('uint8'), mode='L') output_img = output_img.resize(img_size, Image.BICUBIC) output_img.putpalette(palette) output_img = output_img.convert('L') output_img.save(os.path.join(result_dir, image_name[:-4]+'.jpg')) os.system(f"cd {BASE_DIR}") from PIL import Image def upload_resize_generate_cloth_mask_and_move_to_viton_hd_test_inputs(cloth_img): os.system(f"cd {BASE_DIR}") cloth_img = cloth_img.resize((768, 1024), Image.BICUBIC) cloth_img.save(f"{BASE_DIR}/cloth/cloth.jpg") cloth_img.save(f"{BASE_DIR}/VITON-HD/datasets/test/cloth/cloth.jpg") try: generate_cloth_mask(f"{BASE_DIR}/cloth", f"{BASE_DIR}/cloth_mask", f"{BASE_DIR}/cloth_segmentation/checkpoints/cloth_segm_u2net_latest.pth") cloth_mask_img = Image.open(f"{BASE_DIR}/cloth_mask/cloth.jpg") cloth_mask_img.save(f"{BASE_DIR}/VITON-HD/datasets/test/cloth-mask/cloth.jpg") except Exception as e: print(e) # Gradio os.system("pip install gradio") import gradio as gr # import cv2 from PIL import Image IMAGEPATH='/content/fashion-eye-try-on/VITON-HD/datasets/test/image' CLOTHPATH='/content/fashion-eye-try-on/VITON-HD/datasets/test/cloth' CLOTHMASKPATH='/content/fashion-eye-try-on/VITON-HD/datasets/test/image' from threading import Thread def fashion_eye_tryon(person_img, cloth_img): result_img = person_img # img.save(IMAGEPATH + "person.jpg") # dress.save(CLOTHPATH + "cloth.jpg") # txt = open("/content/VITON-HD/datasets/test_pairs.txt", "a") # txt.write("person_img.jpg dress_img.jpg\n") # txt.close() # # result # print(person_img.info, cloth_img.info) # p_t1 = Thread(target=upload_remove_background_and_save_person_image, args=(person_img, )) # c_t2 = Thread(target=upload_resize_generate_cloth_mask_and_move_to_viton_hd_test_inputs, args=(cloth_img, )) # p_t1.start() # c_t2.start() # p_t1.join() # c_t2.join() # Estimate pose try: upload_resize_generate_cloth_mask_and_move_to_viton_hd_test_inputs(cloth_img) upload_remove_background_and_save_person_image(person_img) remove_ipynb_checkpoints() estimate_pose() # Generate human parse remove_ipynb_checkpoints() generate_human_segmentation_map() generate_test_pairs_txt() remove_ipynb_checkpoints() generate_viton_hd() for p in ["/content/fashion-eye-try-on/output/person_cloth.jpg", "/content/fashion-eye-try-on/output/person.jpg_cloth.jpg"]: if os.path.exists(p): result_img = Image.open(p) except Exception as e: print(e) return return result_img # res = fashion_eye_tryon("", "") # res.show() gr.Interface(fn=fashion_eye_tryon, inputs=[gr.Image(type = "pil", label="Your image"), gr.Image(type="pil", label="Dress")], outputs="image" ).launch(debug=True) # !pip freeze > /content/requirements_final.txt