update

README.md

@@ -1,14 +1,13 @@
 ---
-title: Blurable
-emoji: 📚
-colorFrom: gray
+title: Test
+emoji: 🌍
+colorFrom: yellow
 colorTo: indigo
 sdk: gradio
 sdk_version: 5.6.0
 app_file: app.py
 pinned: false
-license: apache-2.0
-short_description: Background Blur using Gaussian Blur and Lens Blur
+short_description: gaussian blurs
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,87 @@
+import gradio as gr
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from transformers import (
+    SegformerImageProcessor, 
+    SegformerForSemanticSegmentation,
+    AutoImageProcessor, 
+    AutoModelForDepthEstimation
+)
+
+# Load Segformer model for Gaussian blur
+segformer_processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
+segformer_model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
+
+# Load Depth-Anything model for lens blur
+depth_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 = image
+
+    if blur_type == "Gaussian":
+        # Use Segformer for Gaussian blur
+        pil_image = Image.fromarray(img)
+        inputs = segformer_processor(images=pil_image, return_tensors="pt")
+        outputs = segformer_model(**inputs)
+        logits = outputs.logits
+
+        mask = logits[0, 12, :, :].detach().cpu().numpy() > depth_threshold
+        mask = cv2.resize(mask.astype(np.uint8), (img.shape[1], img.shape[0]))
+
+    elif blur_type == "Lens":
+        # Use Depth-Anything for lens blur
+        pil_image = Image.fromarray(img)
+        inputs = depth_processor(images=pil_image, return_tensors="pt")
+
+        with torch.no_grad():
+            outputs = depth_model(**inputs)
+            predicted_depth = outputs.predicted_depth
+
+        prediction = torch.nn.functional.interpolate(
+            predicted_depth.unsqueeze(1),
+            size=img.shape[:2],
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        mask = prediction[0, 0, :, :].detach().cpu().numpy() > depth_threshold
+        mask = mask.astype(np.uint8)
+
+    # Invert mask using cv2
+    mask = cv2.bitwise_not(mask)
+    mask = np.repeat(mask[:, :, np.newaxis], 3, axis=2)
+
+    # 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, blurred_image, img)
+    
+    return output
+
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=apply_blur,
+    inputs=[
+        gr.Image(label="Input Image"),
+        gr.Radio(["Gaussian", "Lens"], label="Blur Type", value="Gaussian"),
+        gr.Slider(1, 30, value=15, step=1, label="Blur Strength"),
+        gr.Slider(-20, 20, value=-4, 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 using different segmentation models."
+)
+
+# Launch the app
+iface.launch(share=True)

packages.txt

@@ -0,0 +1 @@
+python3-opencv

requirements.txt

@@ -0,0 +1,7 @@
+opencv-python
+jinja2
+gradio
+numpy
+torch
+Pillow
+transformers