add applitcation

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+
+# Load depth estimation model
+image_processor = AutoImageProcessor.from_pretrained("depth-anything/Depth-Anything-V2-Small-hf")
+depth_model = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Small-hf")
+
+def apply_blur(image, blur_type, blur_strength, depth_threshold):
+    # Convert image to RGB
+    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    # Depth estimation
+    pil_image = Image.fromarray(img)
+    inputs = image_processor(images=pil_image, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = depth_model(**inputs)
+        predicted_depth = outputs.predicted_depth
+
+    # Interpolate to original size
+    prediction = torch.nn.functional.interpolate(
+        predicted_depth.unsqueeze(1),
+        size=img.shape[:2],
+        mode="bicubic",
+        align_corners=False,
+    )
+
+    # Create mask based on depth threshold
+    mask = prediction[0, 0, :, :].detach().cpu().numpy() < depth_threshold
+    mask = mask.astype(np.uint8)
+    mask = np.repeat(mask[:, :, np.newaxis], 3, axis=2) * 255
+
+    # Apply blur based on selected type
+    if blur_type == "Gaussian":
+        blurred_image = cv2.GaussianBlur(img, (0, 0), sigmaX=blur_strength)
+    elif blur_type == "Lens":
+        # Simulate lens blur using a larger kernel
+        kernel_size = int(blur_strength * 2) * 2 + 1
+        blurred_image = cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
+
+    # Combine blurred and original images using the mask
+    output = np.where(mask == 255, img, blurred_image)
+    
+    return output
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=apply_blur,
+    inputs=[
+        gr.Image(label="Input Image"),
+        gr.Radio(["Gaussian", "Lens"], label="Blur Type"),
+        gr.Slider(1, 30, value=15, step=1, label="Blur Strength"),
+        gr.Slider(1, 10, value=3, step=0.1, label="Depth Threshold")
+    ],
+    outputs=gr.Image(label="Output Image"),
+    title="Image Segmentation and Blurring",
+    description="Upload an image and apply Gaussian or Lens blur to the background based on depth estimation."
+)
+
+# Launch the app
+iface.launch()