update README

README.md

@@ -1,3 +1,106 @@
 ---
 license: mit
 ---
+
+# 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
+import torch.nn as nn
+import torch.nn.functional as F
+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 the model and image processor:
+```py
+# initialize model
+config = DepthProConfig(use_fov_model=False)
+model = DepthProForDepthEstimation(config)
+features = config.fusion_hidden_size
+semantic_classifier_dropout = 0.1
+num_labels = 1
+model.head.head = nn.Sequential(
+    nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
+    nn.BatchNorm2d(features),
+    nn.ReLU(),
+    nn.Dropout(semantic_classifier_dropout),
+    nn.Conv2d(features, features, kernel_size=1),
+    nn.ConvTranspose2d(features, num_labels, kernel_size=2, stride=2, padding=0, bias=True),
+)
+
+# load weights
+weights_path = hf_hub_download(repo_id="geetu040/DepthPro_Segmentation_Human", filename="model_weights.pth")
+model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'), weights_only=True))
+
+# load to device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+# load image processor
+image_processor = DepthProImageProcessorFast()
+```
+
+Inference:
+```py
+# inference
+
+url = "https://huggingface.co/spaces/geetu040/DepthPro_Segmentation_Human/resolve/main/assets/examples/man_with_arms_open.jpg"
+
+image = Image.open(requests.get(url, stream=True).raw)
+image = image.convert("RGB")
+
+# prepare image for the model
+inputs = image_processor(images=image, return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+
+# inference
+with torch.no_grad():
+    output = model(**inputs)
+
+# convert tensors to PIL.Image
+output = output[0]                 # get output logits
+output = F.interpolate(
+    output.unsqueeze(0),
+    size=(image.height, image.width)
+)                                  # interpolate to match size
+output = output.squeeze()          # get first and only batch and channel
+output = output.sigmoid()          # apply sigmoid for binary segmentation
+output = (output > 0.5).float()    # threshold to create binary mask
+output = output.cpu()              # unload from cuda if used
+output = output * 255              # convert [0, 1] to [0, 255]
+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()
+```