Add: DFF support

yolov5.py

@@ -6,7 +6,7 @@ import torchvision.transforms as transforms
 from pytorch_grad_cam import EigenCAM
 from pytorch_grad_cam.utils.image import show_cam_on_image, scale_cam_image
 import gradio as gr
-
+"""
 # Global Color Palette
 COLORS = np.random.uniform(0, 255, size=(80, 3))
 
@@ -81,4 +81,115 @@ def xai_yolov5(image):
     return Image.fromarray(final_image), caption
 
 
-    
+   """ 
+
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+import torchvision.transforms as transforms
+from pytorch_grad_cam import EigenCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image, scale_cam_image
+import gradio as gr
+from sklearn.decomposition import NMF  # For feature factorization
+
+# Global Color Palette
+COLORS = np.random.uniform(0, 255, size=(80, 3))
+
+
+def parse_detections(results):
+    detections = results.pandas().xyxy[0].to_dict()
+    boxes, colors, names, classes = [], [], [], []
+    for i in range(len(detections["xmin"])):
+        confidence = detections["confidence"][i]
+        if confidence < 0.2:
+            continue
+        xmin, ymin = int(detections["xmin"][i]), int(detections["ymin"][i])
+        xmax, ymax = int(detections["xmax"][i]), int(detections["ymax"][i])
+        name, category = detections["name"][i], int(detections["class"][i])
+        boxes.append((xmin, ymin, xmax, ymax))
+        colors.append(COLORS[category])
+        names.append(name)
+        classes.append(category) 
+    return boxes, colors, names, classes
+
+
+def draw_detections(boxes, colors, names, classes, img):
+    for box, color, name, cls in zip(boxes, colors, names, classes):
+        xmin, ymin, xmax, ymax = box
+        label = f"{cls}: {name}"  # Combine class ID and name
+        cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2)
+        cv2.putText(
+            img, label, (xmin, ymin - 5),
+            cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2,
+            lineType=cv2.LINE_AA
+        )
+    return img
+
+
+def generate_cam_image(model, target_layers, tensor, rgb_img, boxes):
+    cam = EigenCAM(model, target_layers)
+    grayscale_cam = cam(tensor)[0, :, :]
+    img_float = np.float32(rgb_img) / 255
+    cam_image = show_cam_on_image(img_float, grayscale_cam, use_rgb=True)
+    renormalized_cam = np.zeros(grayscale_cam.shape, dtype=np.float32)
+    for x1, y1, x2, y2 in boxes:
+        renormalized_cam[y1:y2, x1:x2] = scale_cam_image(grayscale_cam[y1:y2, x1:x2].copy())
+    renormalized_cam = scale_cam_image(renormalized_cam)
+    renormalized_cam_image = show_cam_on_image(img_float, renormalized_cam, use_rgb=True)
+
+    return cam_image, renormalized_cam_image
+
+
+def deep_feature_factorization(features):
+    # Reshape the features for factorization (Flatten spatial dimensions)
+    n, c, h, w = features.shape
+    reshaped_features = features.view(c, -1).detach().cpu().numpy()
+
+    # Apply Non-Negative Matrix Factorization (NMF)
+    nmf = NMF(n_components=10, init='random', random_state=42, max_iter=300)
+    basis = nmf.fit_transform(reshaped_features)
+    coefficients = nmf.components_
+
+    # Reconstruct the feature map
+    reconstructed = np.dot(basis, coefficients).reshape((c, h, w))
+
+    return torch.tensor(reconstructed, dtype=torch.float32).unsqueeze(0)
+
+
+def xai_yolov5(image):
+    model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+    model.eval()
+    model.cpu()
+
+    target_layers = [model.model.model.model[-2]]  
+
+    # Run YOLO detection
+    results = model([image])
+    boxes, colors, names, classes = parse_detections(results) 
+    detections_img = draw_detections(boxes, colors, names, classes, image.copy())
+
+    # Extract intermediate features
+    def hook(module, input, output):
+        return output
+
+    hook_handle = target_layers[0].register_forward_hook(hook)
+    with torch.no_grad():
+        model([image])
+        intermediate_features = hook_handle.remove()
+
+    # Apply Deep Feature Factorization
+    factored_features = deep_feature_factorization(intermediate_features)
+
+    # Prepare input tensor for Grad-CAM
+    img_float = np.float32(image) / 255
+    transform = transforms.ToTensor()
+    tensor = transform(img_float).unsqueeze(0)
+
+    # Grad-CAM visualization using factored features
+    cam_image, renormalized_cam_image = generate_cam_image(model, target_layers, factored_features, image, boxes)
+
+    # Combine results
+    final_image = np.hstack((image, detections_img, renormalized_cam_image))
+    caption = "Results using YOLOv5 with Deep Feature Factorization"
+    return Image.fromarray(final_image), caption