geetu040/DepthPro_Segmentation_Human

DepthPro: Human Segmentation

• This work is a part of the DepthPro: Beyond Depth Estimation 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:

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:

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:

# 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:

# 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()