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app.py

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+import gradio as gr
+import cv2
+import matplotlib
+import numpy as np
+import os
+from PIL import Image
+import spaces
+import torch
+import tempfile
+from gradio_imageslider import ImageSlider
+from huggingface_hub import hf_hub_download
+
+from ppd.utils.set_seed import set_seed
+from ppd.models.ppd import PixelPerfectDepth
+
+css = """
+#img-display-container {
+    max-height: 100vh;
+}
+#img-display-input {
+    max-height: 80vh;
+}
+#img-display-output {
+    max-height: 80vh;
+}
+#download {
+    height: 62px;
+}
+"""
+# DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+# model_configs = {
+#     'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+#     'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+#     'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+#     'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+# }
+# encoder2name = {
+#     'vits': 'Small',
+#     'vitb': 'Base',
+#     'vitl': 'Large',
+#     'vitg': 'Giant', # we are undergoing company review procedures to release our giant model checkpoint
+# }
+# encoder = 'vitl'
+# model_name = encoder2name[encoder]
+# model = DepthAnythingV2(**model_configs[encoder])
+# filepath = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_name}", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model")
+# state_dict = torch.load(filepath, map_location="cpu")
+# model.load_state_dict(state_dict)
+# model = model.to(DEVICE).eval()
+
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = PixelPerfectDepth(semantics_pth='checkpoints/depth_anything_v2_vitl.pth', sampling_steps=10)
+model.load_state_dict(torch.load('checkpoints/ppd.pth', map_location='cpu'), strict=False)
+model = model.to(DEVICE).eval()
+
+
+
+title = "# Pixel-Perfect Depth"
+description = """Official demo for **Pixel-Perfect Depth**.
+Please refer to our [paper](https://arxiv.org/abs/2406.09414), [project page](https://depth-anything-v2.github.io), and [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details."""
+
+@spaces.GPU
+def predict_depth(image):
+    return model.forward_test(image)
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    gr.Markdown("### Depth Prediction demo")
+
+    with gr.Row():
+        input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
+        depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
+    submit = gr.Button(value="Predict Depth")
+    gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download",)
+    raw_file = gr.File(label="16-bit raw output (can be considered as disparity)", elem_id="download",)
+
+    cmap = matplotlib.colormaps.get_cmap('Spectral')
+
+    def on_submit(image):
+        original_image = image.copy()
+
+        # h, w = image.shape[:2]
+
+        depth = predict_depth(image[:, :, ::-1])
+
+        raw_depth = Image.fromarray(depth.astype('uint16'))
+        tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        raw_depth.save(tmp_raw_depth.name)
+
+        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        depth = depth.astype(np.uint8)
+        colored_depth = (cmap(depth)[:, :, :3] * 255).astype(np.uint8)
+
+        gray_depth = Image.fromarray(depth)
+        tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+        gray_depth.save(tmp_gray_depth.name)
+
+        return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name]
+
+    submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file])
+
+    example_files = os.listdir('assets/examples')
+    example_files.sort()
+    example_files = [os.path.join('assets/examples', filename) for filename in example_files]
+    examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file], fn=on_submit)
+
+
+if __name__ == '__main__':
+    demo.queue().launch(share=True)

requirements.txt

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+gradio_imageslider
+gradio==4.36.0
+torch
+torchvision
+opencv-python
+matplotlib
+huggingface_hub