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app.py

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+import gradio as gr
+import numpy as np
+import torch
+from PIL import Image
+import cv2
+import os  # Import the os module
+
+from transformers import DPTImageProcessor, DPTForDepthEstimation
+
+# Access the token from the Space's secrets
+HF_TOKEN = os.environ.get("HUGGING_FACE_TOKEN")
+if HF_TOKEN is None:
+    raise ValueError("Hugging Face token not found. Make sure you've added it as a secret to your Space.")
+
+# Load the image processor and model
+image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas", token=HF_TOKEN)
+model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas", low_cpu_mem_usage=True, token=HF_TOKEN)
+model.to('cuda')
+model.eval()
+
+def apply_depth_aware_blur(image, foreground_blur, midground_blur, background_blur, foreground_threshold, midground_lower, midground_upper, background_threshold):
+    original_image = Image.fromarray(image).convert("RGB")
+    original_image = original_image.resize((512, 512))
+    image_np = np.array(original_image)
+
+    inputs = image_processor(images=original_image, return_tensors="pt").to('cuda')
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_depth = outputs.predicted_depth
+
+    prediction = torch.nn.functional.interpolate(
+        predicted_depth.unsqueeze(1),
+        size=(512, 512),
+        mode="bicubic",
+        align_corners=False,
+    )
+
+    depth_map = prediction.squeeze().cpu().numpy()
+    normalized_depth_map = (depth_map - np.min(depth_map)) / (np.max(depth_map) - np.min(depth_map))
+
+    foreground_mask = (normalized_depth_map < foreground_threshold).astype(np.uint8) * 255
+    midground_mask = ((normalized_depth_map >= midground_lower) & (normalized_depth_map < midground_upper)).astype(np.uint8) * 255
+    background_mask = (normalized_depth_map >= background_threshold).astype(np.uint8) * 255
+
+    blurred_image = np.copy(np.array(original_image))
+
+    if foreground_blur > 0:
+        blurred_image = np.where((foreground_mask[..., None] == 255), cv2.GaussianBlur(blurred_image, (foreground_blur, foreground_blur), 10), blurred_image)
+    if midground_blur > 0:
+        blurred_image = np.where((midground_mask[..., None] == 255), cv2.GaussianBlur(blurred_image, (midground_blur, midground_blur), 8), blurred_image)
+    if background_blur > 0:
+        blurred_image = np.where((background_mask[..., None] == 255), cv2.GaussianBlur(blurred_image, (background_blur, background_blur), 20), blurred_image)
+
+    return Image.fromarray(blurred_image.astype(np.uint8))
+
+iface = gr.Interface(
+    fn=apply_depth_aware_blur,
+    inputs=[
+        gr.Image(label="Input Image"),
+        gr.Slider(0, 51, step=2, label="Foreground Blur Kernel Size", default=0),
+        gr.Slider(0, 51, step=2, label="Midground Blur Kernel Size", default=0),
+        gr.Slider(0, 51, step=2, label="Background Blur Kernel Size", default=35),
+        gr.Slider(0, 1, label="Foreground Threshold", default=0.2),
+        gr.Slider(0, 1, label="Midground Lower Threshold", default=0.2),
+        gr.Slider(0, 1, label="Midground Upper Threshold", default=0.6),
+        gr.Slider(0, 1, label="Background Threshold", default=0.6)
+
+    ],
+    outputs=gr.Image(label="Blurred Image"),
+    title="Depth-Aware Lens Blur App",
+    description="Apply depth-based blur to uploaded images. Adjust blur intensity for foreground, midground, and background.",
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+transformers
+opencv-python
+Pillow
+torch