Create app.py

app.py

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+import os
+import cv2
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+import torch.nn.functional as F
+from gradio.themes.utils import sizes
+from PIL import Image
+from torchvision import transforms
+import tempfile
+
+class Config:
+    ASSETS_DIR = os.path.join(os.path.dirname(__file__), 'assets')
+    CHECKPOINTS_DIR = os.path.join(ASSETS_DIR, "checkpoints")
+    CHECKPOINTS = {
+        "0.3b": "sapiens_0.3b_normal_render_people_epoch_66_torchscript.pt2",
+        "0.6b": "sapiens_0.6b_normal_render_people_epoch_200_torchscript.pt2",
+        "1b": "sapiens_1b_normal_render_people_epoch_115_torchscript.pt2",
+        "2b": "sapiens_2b_normal_render_people_epoch_70_torchscript.pt2",
+    }
+    SEG_CHECKPOINTS = {
+        "fg-bg-1b (recommended)": "sapiens_1b_seg_foreground_epoch_8_torchscript.pt2",
+        "no-bg-removal": None,
+        "part-seg-1b": "sapiens_1b_goliath_best_goliath_mIoU_7994_epoch_151_torchscript.pt2",
+    }
+
+class ModelManager:
+    @staticmethod
+    def load_model(checkpoint_name: str):
+        if checkpoint_name is None:
+            return None
+        checkpoint_path = os.path.join(Config.CHECKPOINTS_DIR, checkpoint_name)
+        model = torch.jit.load(checkpoint_path)
+        model.eval()
+        model.to("cuda")
+        return model
+
+    @staticmethod
+    @torch.inference_mode()
+    def run_model(model, input_tensor, height, width):
+        output = model(input_tensor)
+        return F.interpolate(output, size=(height, width), mode="bilinear", align_corners=False)
+
+class ImageProcessor:
+    def __init__(self):
+        self.transform_fn = transforms.Compose([
+            transforms.Resize((1024, 768)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[123.5/255, 116.5/255, 103.5/255], std=[58.5/255, 57.0/255, 57.5/255]),
+        ])
+
+    @spaces.GPU
+    def process_image(self, image: Image.Image, normal_model_name: str, seg_model_name: str):
+        # Load models here instead of storing them as class attributes
+        normal_model = ModelManager.load_model(Config.CHECKPOINTS[normal_model_name])
+        input_tensor = self.transform_fn(image).unsqueeze(0).to("cuda")
+
+        # Run normal estimation
+        normal_output = ModelManager.run_model(normal_model, input_tensor, image.height, image.width)
+        normal_map = normal_output.squeeze().cpu().numpy().transpose(1, 2, 0)
+
+        # Create a copy of the normal map for visualization
+        normal_map_vis = normal_map.copy()
+
+        # Run segmentation
+        if seg_model_name != "no-bg-removal":
+            seg_model = ModelManager.load_model(Config.SEG_CHECKPOINTS[seg_model_name])
+            seg_output = ModelManager.run_model(seg_model, input_tensor, image.height, image.width)
+            seg_mask = (seg_output.argmax(dim=1) > 0).float().cpu().numpy()[0]
+
+            # Apply segmentation mask to normal maps
+            normal_map[seg_mask == 0] = np.nan  # Set background to NaN for NPY file
+            normal_map_vis[seg_mask == 0] = -1  # Set background to -1 for visualization
+
+        # Normalize and visualize normal map
+        normal_map_vis = self.visualize_normal_map(normal_map_vis)
+
+        # Create downloadable .npy file
+        npy_path = tempfile.mktemp(suffix='.npy')
+        np.save(npy_path, normal_map)
+
+        return Image.fromarray(normal_map_vis), npy_path
+
+    @staticmethod
+    def visualize_normal_map(normal_map):
+        normal_map_norm = np.linalg.norm(normal_map, axis=-1, keepdims=True)
+        normal_map_normalized = normal_map / (normal_map_norm + 1e-5)
+        normal_map_vis = ((normal_map_normalized + 1) / 2 * 255).astype(np.uint8)
+        return normal_map_vis
+
+class GradioInterface:
+    def __init__(self):
+        self.image_processor = ImageProcessor()
+
+    def create_interface(self):
+        app_styles = """
+        <style>
+            /* Global Styles */
+            body, #root {
+                font-family: Helvetica, Arial, sans-serif;
+                background-color: #1a1a1a;
+                color: #fafafa;
+            }
+
+            /* Header Styles */
+            .app-header {
+                background: linear-gradient(45deg, #1a1a1a 0%, #333333 100%);
+                padding: 24px;
+                border-radius: 8px;
+                margin-bottom: 24px;
+                text-align: center;
+            }
+
+            .app-title {
+                font-size: 48px;
+                margin: 0;
+                color: #fafafa;
+            }
+
+            .app-subtitle {
+                font-size: 24px;
+                margin: 8px 0 16px;
+                color: #fafafa;
+            }
+
+            .app-description {
+                font-size: 16px;
+                line-height: 1.6;
+                opacity: 0.8;
+                margin-bottom: 24px;
+            }
+
+            /* Button Styles */
+            .publication-links {
+                display: flex;
+                justify-content: center;
+                flex-wrap: wrap;
+                gap: 8px;
+                margin-bottom: 16px;
+            }
+
+            .publication-link {
+                display: inline-flex;
+                align-items: center;
+                padding: 8px 16px;
+                background-color: #333;
+                color: #fff !important;
+                text-decoration: none !important;
+                border-radius: 20px;
+                font-size: 14px;
+                transition: background-color 0.3s;
+            }
+
+            .publication-link:hover {
+                background-color: #555;
+            }
+
+            .publication-link i {
+                margin-right: 8px;
+            }
+
+            /* Content Styles */
+            .content-container {
+                background-color: #2a2a2a;
+                border-radius: 8px;
+                padding: 24px;
+                margin-bottom: 24px;
+            }
+
+            /* Image Styles */
+            .image-preview img {
+                max-width: 100%;
+                max-height: 512px;
+                margin: 0 auto;
+                border-radius: 4px;
+                display: block;
+            }
+
+            /* Control Styles */
+            .control-panel {
+                background-color: #333;
+                padding: 16px;
+                border-radius: 8px;
+                margin-top: 16px;
+            }
+
+            /* Gradio Component Overrides */
+            .gr-button {
+                background-color: #4a4a4a;
+                color: #fff;
+                border: none;
+                border-radius: 4px;
+                padding: 8px 16px;
+                cursor: pointer;
+                transition: background-color 0.3s;
+            }
+
+            .gr-button:hover {
+                background-color: #5a5a5a;
+            }
+
+            .gr-input, .gr-dropdown {
+                background-color: #3a3a3a;
+                color: #fff;
+                border: 1px solid #4a4a4a;
+                border-radius: 4px;
+                padding: 8px;
+            }
+
+            .gr-form {
+                background-color: transparent;
+            }
+
+            .gr-panel {
+                border: none;
+                background-color: transparent;
+            }
+
+            /* Override any conflicting styles from Bulma */
+            .button.is-normal.is-rounded.is-dark {
+                color: #fff !important;
+                text-decoration: none !important;
+            }
+        </style>
+        """
+
+        header_html = f"""
+        <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bulma@0.9.3/css/bulma.min.css">
+        <link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.15.4/css/all.css">
+        {app_styles}
+        <div class="app-header">
+            <h1 class="app-title">Sapiens: Normal Estimation</h1>
+            <h2 class="app-subtitle">ECCV 2024 (Oral)</h2>
+            <p class="app-description">
+                Meta presents Sapiens, foundation models for human tasks pretrained on 300 million human images. 
+                This demo showcases the finetuned normal estimation model. <br>
+                Checkout other normal estimation baselines to compare: <a href="https://huggingface.co/spaces/Stable-X/normal-estimation-arena" style="color: #3273dc;">normal-estimation-arena</a>
+            </p>
+            <div class="publication-links">
+                <a href="https://arxiv.org/abs/2408.12569" class="publication-link">
+                    <i class="fas fa-file-pdf"></i>arXiv
+                </a>
+                <a href="https://github.com/facebookresearch/sapiens" class="publication-link">
+                    <i class="fab fa-github"></i>Code
+                </a>
+                <a href="https://about.meta.com/realitylabs/codecavatars/sapiens/" class="publication-link">
+                    <i class="fas fa-globe"></i>Meta
+                </a>
+                <a href="https://rawalkhirodkar.github.io/sapiens" class="publication-link">
+                    <i class="fas fa-chart-bar"></i>Results
+                </a>
+            </div>
+            <div class="publication-links">
+                <a href="https://huggingface.co/spaces/facebook/sapiens_pose" class="publication-link">
+                    <i class="fas fa-user"></i>Demo-Pose
+                </a>
+                <a href="https://huggingface.co/spaces/facebook/sapiens_seg" class="publication-link">
+                    <i class="fas fa-puzzle-piece"></i>Demo-Seg
+                </a>
+                <a href="https://huggingface.co/spaces/facebook/sapiens_depth" class="publication-link">
+                    <i class="fas fa-cube"></i>Demo-Depth
+                </a>
+                <a href="https://huggingface.co/spaces/facebook/sapiens_normal" class="publication-link">
+                    <i class="fas fa-vector-square"></i>Demo-Normal
+                </a>
+            </div>
+        </div>
+        """
+
+        def process_image(image, normal_model_name, seg_model_name):
+            result, npy_path = self.image_processor.process_image(image, normal_model_name, seg_model_name)
+            return result, npy_path
+        
+        js_func = """
+        function refresh() {
+            const url = new URL(window.location);
+            if (url.searchParams.get('__theme') !== 'dark') {
+                url.searchParams.set('__theme', 'dark');
+                window.location.href = url.href;
+            }
+        }
+        """
+
+        with gr.Blocks(js=js_func, theme=gr.themes.Default()) as demo:
+            gr.HTML(header_html)
+            with gr.Row(elem_classes="content-container"):
+                with gr.Column():
+                    input_image = gr.Image(label="Input Image", type="pil", format="png", elem_classes="image-preview")
+                    with gr.Row(elem_classes="control-panel"):
+                        normal_model_name = gr.Dropdown(
+                            label="Normal Model Size",
+                            choices=list(Config.CHECKPOINTS.keys()),
+                            value="1b",
+                        )
+                        seg_model_name = gr.Dropdown(
+                            label="Background Removal Model",
+                            choices=list(Config.SEG_CHECKPOINTS.keys()),
+                            value="fg-bg-1b (recommended)",
+                        )
+                    example_model = gr.Examples(
+                        inputs=input_image,
+                        examples_per_page=14,
+                        examples=[
+                            os.path.join(Config.ASSETS_DIR, "images", img)
+                            for img in os.listdir(os.path.join(Config.ASSETS_DIR, "images"))
+                        ],
+                    )
+                with gr.Column():
+                    result_image = gr.Image(label="Normal Estimation Result", type="pil", elem_classes="image-preview")
+                    npy_output = gr.File(label="Output (.npy). Note: Background normal is NaN.")
+                    run_button = gr.Button("Run", elem_classes="gr-button")
+
+            run_button.click(
+                fn=process_image,
+                inputs=[input_image, normal_model_name, seg_model_name],
+                outputs=[result_image, npy_output],
+            )
+
+        return demo
+
+def main():
+    # Configure CUDA if available
+    if torch.cuda.is_available() and torch.cuda.get_device_properties(0).major >= 8:
+        torch.backends.cuda.matmul.allow_tf32 = True
+        torch.backends.cudnn.allow_tf32 = True
+
+    interface = GradioInterface()
+    demo = interface.create_interface()
+    demo.launch(share=False)
+
+if __name__ == "__main__":
+main()