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detect_app.py

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+import torch
+import warnings
+import gradio as gr
+import cv2
+import torchvision
+from torch import nn
+from torchvision.models import mobilenet_v3_small
+import numpy as np
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+warnings.filterwarnings("ignore")
+
+
+def flip_text(x):
+    return x[::-1]
+
+
+def method1_prep(image):
+    transforms = torchvision.models.MobileNet_V3_Small_Weights.IMAGENET1K_V1.transforms()
+    image = torch.from_numpy(image).permute(2, 0, 1)
+    return transforms(image)
+
+
+def method2_prep(image):
+    transforms = torchvision.transforms.Compose([
+        torchvision.transforms.Resize((256, 256)),
+        torchvision.transforms.CenterCrop((224, 224))
+    ])
+    t_lower = 50
+    t_upper = 150
+    
+    height, width = image.shape[:2]
+
+    x = (width - 1920) // 2
+    y = (height - 1080) // 2
+    
+    image = image[y:y+1080, x:x+1920]
+
+    img = torch.from_numpy(cv2.Canny(image, t_lower, t_upper)[np.newaxis, ...])
+    img = torch.vstack((img, img, img))
+
+    return transforms(img.type(torch.float32))
+
+
+def model1_inf(x):
+    print("Method 1")
+
+    image = method1_prep(x).unsqueeze(dim=0)
+    model = mobilenet_v3_small(weights='DEFAULT')
+    model.classifier[3] = nn.Linear(in_features=1024, out_features=2, bias=True)
+
+    model.load_state_dict(torch.load('./method1(0.668).pt'))
+
+    model.eval()  # Set the model to evaluation mode
+
+    with torch.inference_mode():
+        model = model.to(device)
+        image = image.to(device)
+        output = torch.softmax(model(image), dim=1).detach().cpu()
+        prediction = torch.argmax(output, dim=1).item()
+        del model
+        torch.cuda.empty_cache()
+        if prediction == 0:
+            return "The image is not pixelated"
+        else:
+            return "The image is pixelated"
+
+
+def model2_inf(x):
+    print("Method 2")
+
+    image = method2_prep(x).unsqueeze(dim=0)
+    model = mobilenet_v3_small(weights='DEFAULT')
+    model.classifier[3] = nn.Linear(in_features=1024, out_features=2, bias=True)
+
+    image_np = image[0].permute(1, 2, 0).cpu().numpy()
+    image_np = (image_np * 255).astype(np.uint8)  # Ensure the image is of type uint8
+
+    model.load_state_dict(torch.load('./method2(0.960).pt'))
+    #print("\nModel weights loaded successfully")
+
+    model.eval()  # Set the model to evaluation mode
+
+    with torch.inference_mode():
+        model = model.to(device)
+        image = image.to(device)
+        output = torch.softmax(model(image), dim=1).detach().cpu()
+        prediction = torch.argmax(output, dim=1).item()
+        del model
+        torch.cuda.empty_cache()
+        if prediction == 0:
+            return "The image is not pixelated"
+        else:
+            return "The image is pixelated"
+
+
+with gr.Blocks() as app:
+    gr.Markdown("### Pixelation Detection App")
+
+    # Selecting method 
+    method = gr.Radio(["Method 1", "Method 2"], label="Select Method")
+
+    with gr.Tab("Classification by image"):
+        image_input = gr.Image(type="numpy", label="Upload an image")
+        output_text = gr.Textbox(label="Output")
+
+        def classify_image(img, method):
+            if method == "Method 1":
+                return model1_inf(img)
+            else:
+                return model2_inf(img)
+
+        method.change(fn=classify_image, inputs=[image_input, method], outputs=output_text)
+        image_input.change(fn=classify_image, inputs=[image_input, method], outputs=output_text)
+
+        
+app.launch(share=False)

method2(0.960).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:af08d7011d16d43cdf5bacf03dcd12723f26464013571e597a6160dff2081c65
+size 6214802