Add: DFF support

yolov5.py

@@ -1,195 +0,0 @@
-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
-"""
-# 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 xai_yolov5(image):
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
-    model.eval()
-    model.cpu()
-
-    target_layers = [model.model.model.model[-2]]  # Grad-CAM target layer
-
-    # Run YOLO detection
-    results = model([image])
-    boxes, colors, names, classes = parse_detections(results) 
-    detections_img = draw_detections(boxes, colors, names,classes,  image.copy())
-
-    # Prepare input tensor for Grad-CAM
-    img_float = np.float32(image) / 255
-    transform = transforms.ToTensor()
-    tensor = transform(img_float).unsqueeze(0)
-
-    # Grad-CAM visualization
-    cam_image, renormalized_cam_image = generate_cam_image(model, target_layers, tensor, image, boxes)
-
-    # Combine results
-    final_image = np.hstack((image, detections_img, renormalized_cam_image))
-    caption = "Results using YOLOv5"
-    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