Vanyadoing commited on
Commit
e9f1a2b
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1 Parent(s): 27e35d9

Update app.py

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Files changed (1) hide show
  1. app.py +18 -10
app.py CHANGED
@@ -2,12 +2,12 @@ import gradio as gr
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  import torch
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  import numpy as np
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  from PIL import Image
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- from huggingface_hub import hf_hub_download
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  import matplotlib.pyplot as plt
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-
 
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  from depth_anything_v2.dpt import DepthAnythingV2
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- # Load model as before
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  DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
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  model_configs = {
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  'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
@@ -23,21 +23,28 @@ state_dict = torch.load(model_path, map_location="cpu")
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  model.load_state_dict(state_dict)
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  model = model.to(DEVICE).eval()
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- # Use a matplotlib colormap
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  CMAP = plt.get_cmap('Spectral_r')
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  def infer(image: np.ndarray):
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- # 1. Run the model (BGR to RGB if needed)
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  with torch.no_grad():
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  depth = model.infer_image(image[:, :, ::-1])
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- # 2. Grayscale map (normalize to 0..255)
 
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  depth_norm = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
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  depth_norm = depth_norm.astype(np.uint8)
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  gray = Image.fromarray(depth_norm)
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- # 3. Color map
 
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  colored = (CMAP(depth_norm)[:, :, :3] * 255).astype(np.uint8)
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  color = Image.fromarray(colored)
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- return gray, color
 
 
 
 
 
 
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  iface = gr.Interface(
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  fn=infer,
@@ -45,9 +52,10 @@ iface = gr.Interface(
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  outputs=[
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  gr.Image(label="Grayscale Depth"),
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  gr.Image(label="Colored Depth"),
 
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  ],
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- title="Depth Anything V2 (Minimal, with Colored Output)",
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- description="Upload an image, get depth as grayscale and colored."
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  )
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  iface.launch()
 
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  import torch
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  import numpy as np
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  from PIL import Image
 
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  import matplotlib.pyplot as plt
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+ import cv2
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+ from huggingface_hub import hf_hub_download
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  from depth_anything_v2.dpt import DepthAnythingV2
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+ # Model loading (as before)
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  DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
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  model_configs = {
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  'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
 
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  model.load_state_dict(state_dict)
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  model = model.to(DEVICE).eval()
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  CMAP = plt.get_cmap('Spectral_r')
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  def infer(image: np.ndarray):
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+ # Run depth model
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  with torch.no_grad():
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  depth = model.infer_image(image[:, :, ::-1])
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+
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+ # Grayscale map (normalize to 0..255)
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  depth_norm = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
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  depth_norm = depth_norm.astype(np.uint8)
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  gray = Image.fromarray(depth_norm)
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+
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+ # Colored map
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  colored = (CMAP(depth_norm)[:, :, :3] * 255).astype(np.uint8)
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  color = Image.fromarray(colored)
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+
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+ # Edge map using Canny on the original image (convert to grayscale first)
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+ image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if image.shape[2] == 3 else image
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+ edges = cv2.Canny(image_gray, 100, 200) # threshold1/2 can be tuned
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+ edge_img = Image.fromarray(edges)
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+
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+ return gray, color, edge_img
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  iface = gr.Interface(
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  fn=infer,
 
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  outputs=[
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  gr.Image(label="Grayscale Depth"),
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  gr.Image(label="Colored Depth"),
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+ gr.Image(label="Canny Edge Map"),
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  ],
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+ title="Depth Anything V2 (with Colored Output + Canny Edges)",
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+ description="Upload an image to get depth (gray, color), plus Canny edge map."
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  )
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  iface.launch()