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app.py

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+
+import gradio as gr
+import torch
+import torch.nn as nn
+import torchvision
+from torchvision.transforms import functional as F
+from PIL import Image
+import numpy as np
+import requests
+import os
+import cv2
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from torchvision.models import resnet18
+
+# --------------------------------------------------------------------------
+# 1. Model Definitions (We need to put all model architecture definitions here)
+# --------------------------------------------------------------------------
+
+# --- U-Net Model Definition ---
+class DoubleConv(nn.Module):
+    def __init__(self, in_channels, out_channels, mid_channels=None):
+        super().__init__()
+        if not mid_channels: mid_channels = out_channels
+        self.double_conv = nn.Sequential(nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), nn.BatchNorm2d(mid_channels), nn.ReLU(inplace=True), nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True))
+    def forward(self, x): return self.double_conv(x)
+
+class Down(nn.Module):
+    def __init__(self, in_channels, out_channels): super().__init__(); self.maxpool_conv = nn.Sequential(nn.MaxPool2d(2), DoubleConv(in_channels, out_channels))
+    def forward(self, x): return self.maxpool_conv(x)
+
+class Up(nn.Module):
+    def __init__(self, in_channels, out_channels, bilinear=True):
+        super().__init__()
+        if bilinear: self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True); self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
+        else: self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2); self.conv = DoubleConv(in_channels, out_channels)
+    def forward(self, x1, x2): x1 = self.up(x1); x = torch.cat([x2, x1], dim=1); return self.conv(x)
+
+class UNet(nn.Module):
+    def __init__(self, n_channels=3, n_classes=1, bilinear=True):
+        super(UNet, self).__init__(); factor = 2 if bilinear else 1; self.inc=DoubleConv(n_channels,64); self.down1=Down(64,128); self.down2=Down(128,256); self.down3=Down(256,512); self.down4=Down(512, 1024 // factor); self.up1=Up(1024,512 // factor,bilinear); self.up2=Up(512,256 // factor,bilinear); self.up3=Up(256,128 // factor,bilinear); self.up4=Up(128,64,bilinear); self.outc=nn.Conv2d(64,n_classes,1)
+    def forward(self, x): x1=self.inc(x); x2=self.down1(x1); x3=self.down2(x2); x4=self.down3(x3); x5=self.down4(x4); x=self.up1(x5,x4); x=self.up2(x,x3); x=self.up3(x,x2); x=self.up4(x,x1); return self.outc(x)
+
+# --------------------------------------------------------------------------
+# 2. Global Variables and Loading Functions
+# --------------------------------------------------------------------------
+
+# Use CPU, as free GPU resources on Hugging Face Spaces are limited and unstable
+device = torch.device('cpu')
+
+# Define preprocessing
+from torchvision import transforms
+val_transforms = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+classification_transforms = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# [Important] Your class names
+CLASS_NAMES = [
+    'work_dress', 'sling_dress', 'ethnic_dress', 'gown', 'casual_dress',
+    'party_dress', 'formal_dress', 'sports_dress', 'shirt_dress', 'resort_dress'
+]
+
+# Load all models at once
+def load_models():
+    # Load the object detection model
+    detection_model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True).to(device)
+    detection_model.eval()
+
+    # Load the segmentation model
+    segmentation_model = UNet(n_channels=3, n_classes=1).to(device)
+    segmentation_model.load_state_dict(torch.load("unet_dress_segmentation.pth", map_location=device))
+    segmentation_model.eval()
+
+    # Load your classification model
+    classification_model = resnet18(weights=None).to(device)
+    classification_model.fc = nn.Linear(classification_model.fc.in_features, 10)
+    classification_model.load_state_dict(torch.load("best_cnn_model_LR_1e_4.pt", map_location=device))
+    classification_model.eval()
+    
+    return detection_model, segmentation_model, classification_model
+
+detection_model, segmentation_model, classification_model = load_models()
+print("All models have been successfully loaded to the CPU.")
+
+# --------------------------------------------------------------------------
+# 3. Core Inference Function (Modified to return images and text)
+# --------------------------------------------------------------------------
+
+def process_image(input_image):
+    """
+    Receives a PIL Image object and returns the processing results.
+    """
+    original_pil_img = input_image.convert("RGB")
+    img_tensor = F.to_tensor(original_pil_img).unsqueeze(0).to(device)
+
+    # 1. Detection
+    with torch.no_grad():
+        predictions = detection_model(img_tensor)
+    
+    boxes = []
+    for box, label, score in zip(predictions[0]['boxes'], predictions[0]['labels'], predictions[0]['scores']):
+        if label.item() == 1 and score.item() > 0.8:
+            boxes.append(box.cpu().numpy())
+    
+    if not boxes:
+        return None, None, "No person detected.", None
+
+    box = boxes[0]
+    x1, y1, x2, y2 = map(int, box)
+
+    # 2. Segmentation
+    person_crop_pil = original_pil_img.crop((x1, y1, x2, y2))
+    person_crop_np = np.array(person_crop_pil)
+    seg_input_tensor = val_transforms(person_crop_pil).unsqueeze(0).to(device)
+    with torch.no_grad():
+        mask_logits = segmentation_model(seg_input_tensor)
+        mask_pred = torch.sigmoid(mask_logits) > 0.5
+    mask_np = mask_pred.squeeze().cpu().numpy().astype(np.uint8)
+    mask_resized = cv2.resize(mask_np, (person_crop_pil.width, person_crop_pil.height))
+
+    # 3. Classification
+    mask_3_channel = np.stack([mask_resized]*3, axis=-1)
+    extracted_dress_np = person_crop_np * mask_3_channel
+    extracted_dress_pil = Image.fromarray(extracted_dress_np)
+    class_input_tensor = classification_transforms(extracted_dress_pil).unsqueeze(0).to(device)
+    with torch.no_grad():
+        output_logits = classification_model(class_input_tensor)
+        probabilities = torch.softmax(output_logits, dim=1)[0]
+        confidences = {CLASS_NAMES[i]: float(probabilities[i]) for i in range(10)}
+        predicted_label = CLASS_NAMES[probabilities.argmax()]
+
+    # 4. Grad-CAM
+    target_layer = [classification_model.layer4[-1]]
+    cam = GradCAM(model=classification_model, target_layers=target_layer)
+    targets = [ClassifierOutputTarget(probabilities.argmax())]
+    rgb_img_for_cam = np.array(extracted_dress_pil) / 255.0
+    rgb_img_for_cam = rgb_img_for_cam.astype(np.float32)
+    grayscale_cam = cam(input_tensor=class_input_tensor, targets=targets)[0, :]
+    visualization = show_cam_on_image(rgb_img_for_cam, grayscale_cam, use_rgb=True)
+
+    # Return results
+    return extracted_dress_pil, visualization, confidences, Image.fromarray((mask_resized * 255).astype(np.uint8))
+
+
+# --------------------------------------------------------------------------
+# 4. Create Gradio Interface
+# --------------------------------------------------------------------------
+title = "👗✨ FashionAI: Dress Analysis Pipeline ✨👗"
+description = """
+**An end-to-end Computer Vision Pipeline.**
+Upload an image of a person wearing a dress. The AI will first detect the person, then segment the dress, classify its style, and finally show which part of the dress was most important for its decision.
+\n*Built with PyTorch, torchvision, Gradio, and ❤️ by a DS405B student.*
+"""
+
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil", label="Upload Your Image"),
+    outputs=[
+        gr.Image(type="pil", label="Extracted Dress"),
+        gr.Image(type="pil", label="Grad-CAM Explanation"),
+        gr.Label(num_top_classes=3, label="Classification Probabilities"),
+        gr.Image(type="pil", label="Segmentation Mask")
+    ],
+    title=title,
+    description=description,
+    examples=[
+        ["https://images.pexels.com/photos/1036627/pexels-photo-1036627.jpeg"],
+        ["https://images.pexels.com/photos/1126993/pexels-photo-1126993.jpeg"],
+        ["https://images.pexels.com/photos/985635/pexels-photo-985635.jpeg"]
+    ]
+)
+
+# Launch the application
+iface.launch()

best_cnn_model_LR_1e_4.pt

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+oid sha256:83e862c222c0e306a9840a5838087f64e88f14d44d2fecaf7c19265b49aa34cd
+size 786432

requirements.txt

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+torch
+torchvision
+gradio
+opencv-python-headless
+matplotlib
+Pillow
+requests
+pytorch-grad-cam
+albumentations
+scikit-learn

unet_dress_segmentation.pth

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+size 1048576