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---
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license: mit
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---
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# DepthPro: Human Segmentation
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- This work is a part of the [DepthPro: Beyond Depth Estimation](https://github.com/geetu040/depthpro-beyond-depth) repository, which further explores this model's capabilities on:
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- Image Segmentation - Human Segmentation
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- Image Super Resolution - 384px to 1536px (4x Upscaling)
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- Image Super Resolution - 256px to 1024px (4x Upscaling)
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# Usage
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Install the required libraries:
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```bash
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pip install -q numpy pillow torch torchvision
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pip install -q git+https://github.com/geetu040/transformers.git@depth-pro-projects#egg=transformers
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```
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Import the required libraries:
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```py
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import requests
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from PIL import Image
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from huggingface_hub import hf_hub_download
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import matplotlib.pyplot as plt
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# custom installation from this PR: https://github.com/huggingface/transformers/pull/34583
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# !pip install git+https://github.com/geetu040/transformers.git@depth-pro-projects#egg=transformers
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from transformers import DepthProConfig, DepthProImageProcessorFast, DepthProForDepthEstimation
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```
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Load the model and image processor:
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```py
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# initialize model
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config = DepthProConfig(use_fov_model=False)
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model = DepthProForDepthEstimation(config)
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features = config.fusion_hidden_size
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semantic_classifier_dropout = 0.1
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num_labels = 1
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model.head.head = nn.Sequential(
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nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(features),
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nn.ReLU(),
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nn.Dropout(semantic_classifier_dropout),
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nn.Conv2d(features, features, kernel_size=1),
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nn.ConvTranspose2d(features, num_labels, kernel_size=2, stride=2, padding=0, bias=True),
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)
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# load weights
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weights_path = hf_hub_download(repo_id="geetu040/DepthPro_Segmentation_Human", filename="model_weights.pth")
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model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'), weights_only=True))
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# load to device
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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# load image processor
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image_processor = DepthProImageProcessorFast()
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```
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Inference:
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```py
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# inference
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url = "https://huggingface.co/spaces/geetu040/DepthPro_Segmentation_Human/resolve/main/assets/examples/man_with_arms_open.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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image = image.convert("RGB")
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# prepare image for the model
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inputs = image_processor(images=image, return_tensors="pt")
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inputs = {k: v.to(device) for k, v in inputs.items()}
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# inference
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with torch.no_grad():
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output = model(**inputs)
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# convert tensors to PIL.Image
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output = output[0] # get output logits
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output = F.interpolate(
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output.unsqueeze(0),
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size=(image.height, image.width)
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) # interpolate to match size
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output = output.squeeze() # get first and only batch and channel
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output = output.sigmoid() # apply sigmoid for binary segmentation
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output = (output > 0.5).float() # threshold to create binary mask
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output = output.cpu() # unload from cuda if used
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output = output * 255 # convert [0, 1] to [0, 255]
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output = output.numpy() # convert to numpy
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output = output.astype('uint8') # convert to PIL.Image compatible format
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output = Image.fromarray(output) # create PIL.Image object
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# visualize the prediction
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fig, axes = plt.subplots(1, 2, figsize=(20, 20))
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axes[0].imshow(image)
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axes[0].set_title(f'Low-Resolution (LR) {image.size}')
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axes[0].axis('off')
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axes[1].imshow(output)
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axes[1].set_title(f'Super-Resolution (SR) {output.size}')
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axes[1].axis('off')
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plt.subplots_adjust(wspace=0, hspace=0)
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plt.show()
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```
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