update README

README.md

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 license: mit
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+
+# DepthPro: Human Segmentation
+
+- 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:
+    - Image Segmentation - Human Segmentation
+    - Image Super Resolution - 384px to 1536px (4x Upscaling)
+    - Image Super Resolution - 256px to 1024px (4x Upscaling)
+
+# Usage
+
+Install the required libraries:
+```bash
+pip install -q numpy pillow torch torchvision
+pip install -q git+https://github.com/geetu040/transformers.git@depth-pro-projects#egg=transformers
+```
+
+Import the required libraries:
+```py
+import requests
+from PIL import Image
+import torch
+from huggingface_hub import hf_hub_download
+import matplotlib.pyplot as plt
+
+# custom installation from this PR: https://github.com/huggingface/transformers/pull/34583
+# !pip install git+https://github.com/geetu040/transformers.git@depth-pro-projects#egg=transformers
+from transformers import DepthProConfig, DepthProImageProcessorFast, DepthProForDepthEstimation
+```
+
+Load DepthProForDepthEstimation model
+```py
+# load DepthPro model, used as backbone
+config = DepthProConfig(
+    patch_size=192,
+    patch_embeddings_size=16,
+    num_hidden_layers=12,
+    intermediate_hook_ids=[11, 8, 7, 5],
+    intermediate_feature_dims=[256, 256, 256, 256],
+    scaled_images_ratios=[0.5, 1.0],
+    scaled_images_overlap_ratios=[0.5, 0.25],
+    scaled_images_feature_dims=[1024, 512],
+    use_fov_model=False,
+)
+depthpro_for_depth_estimation = DepthProForDepthEstimation(config)
+```
+
+Create DepthProForSuperResolution model
+```py
+# create DepthPro for super resolution
+class DepthProForSuperResolution(torch.nn.Module):
+    def __init__(self, depthpro_for_depth_estimation):
+        super().__init__()
+
+        self.depthpro_for_depth_estimation = depthpro_for_depth_estimation
+        hidden_size = self.depthpro_for_depth_estimation.config.fusion_hidden_size
+
+        self.image_head = torch.nn.Sequential(
+            torch.nn.ConvTranspose2d(
+                in_channels=config.num_channels,
+                out_channels=hidden_size,
+                kernel_size=4, stride=2, padding=1
+            ),
+            torch.nn.ReLU(),
+        )
+
+        self.head = torch.nn.Sequential(
+            torch.nn.Conv2d(
+                in_channels=hidden_size,
+                out_channels=hidden_size,
+                kernel_size=3, stride=1, padding=1
+            ),
+            torch.nn.ReLU(),
+            torch.nn.ConvTranspose2d(
+                in_channels=hidden_size,
+                out_channels=hidden_size,
+                kernel_size=4, stride=2, padding=1
+            ),
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(
+                in_channels=hidden_size,
+                out_channels=self.depthpro_for_depth_estimation.config.num_channels,
+                kernel_size=3, stride=1, padding=1
+            ),
+        )
+
+    def forward(self, pixel_values):
+        # x is the low resolution image
+        x = pixel_values
+        encoder_features = self.depthpro_for_depth_estimation.depth_pro(x).features
+        fused_hidden_state = self.depthpro_for_depth_estimation.fusion_stage(encoder_features)[-1]
+        x = self.image_head(x)
+        x = torch.nn.functional.interpolate(x, size=fused_hidden_state.shape[2:])
+        x = x + fused_hidden_state
+        x = self.head(x)
+        return x
+```
+
+Load the model and image processor:
+```py
+# initialize the model
+model = DepthProForSuperResolution(depthpro_for_depth_estimation)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+# load weights
+weights_path = hf_hub_download(repo_id="geetu040/DepthPro_SR_4x_384p", filename="model_weights.pth")
+model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
+
+# load image processor
+image_processor = DepthProImageProcessorFast(
+    do_resize=True,
+    size={"width": 384, "height": 384},
+    do_rescale=True,
+    do_normalize=True
+)
+
+# define crop function to ensure square image
+def crop_image(image):
+    """
+    Crops the image from the center to make aspect ratio 1:1.
+    """
+    width, height = image.size
+    min_dim = min(width, height)
+    left = (width - min_dim) // 2
+    top = (height - min_dim) // 2
+    right = left + min_dim
+    bottom = top + min_dim
+    image = image.crop((left, top, right, bottom))
+    return image
+```
+
+Inference:
+```py
+# inference
+
+url = "https://huggingface.co/spaces/geetu040/DepthPro_SR_4x_384p/resolve/main/assets/examples/man_with_arms_open.jpeg"
+
+image = Image.open(requests.get(url, stream=True).raw)
+image = crop_image(image)
+image = image.resize((384, 384), Image.Resampling.BICUBIC)
+
+# prepare image for the model
+inputs = image_processor(images=image, return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# convert tensors to PIL.Image
+output = outputs[0]                        # extract the first and only batch
+output = output.cpu()                      # unload from cuda if used
+output = torch.permute(output, (1, 2, 0))  # (C, H, W) -> (H, W, C)
+output = output * 0.5 + 0.5                # undo normalization
+output = output * 255.                     # undo scaling
+output = output.clip(0, 255.)              # fix out of range
+output = output.numpy()                    # convert to numpy
+output = output.astype('uint8')            # convert to PIL.Image compatible format
+output = Image.fromarray(output)           # create PIL.Image object
+
+# visualize the prediction
+fig, axes = plt.subplots(1, 2, figsize=(20, 20))
+axes[0].imshow(image)
+axes[0].set_title(f'Low-Resolution (LR) {image.size}')
+axes[0].axis('off')
+axes[1].imshow(output)
+axes[1].set_title(f'Super-Resolution (SR) {output.size}')
+axes[1].axis('off')
+plt.subplots_adjust(wspace=0, hspace=0)
+plt.show()
+```