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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
from ultralytics import YOLO
COLORS = np.random.uniform(0, 255, size=(80, 3))
def parse_detections_yolov8(results):
boxes, colors, names = [], [], []
detections = results.boxes
for box in detections:
confidence = box.conf[0].item()
if confidence < 0.2:
continue
xmin, ymin, xmax, ymax = map(int, box.xyxy[0].tolist())
category = int(box.cls[0].item())
name = results.names[category]
boxes.append((xmin, ymin, xmax, ymax))
colors.append(COLORS[category])
names.append(name)
return boxes, colors, names
def draw_detections(boxes, colors, names, img):
for box, color, name in zip(boxes, colors, names):
xmin, ymin, xmax, ymax = box
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2)
cv2.putText(img, name, (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
# Generate Grad-CAM
cam_image = show_cam_on_image(img_float, grayscale_cam, use_rgb=True)
# Renormalize Grad-CAM inside bounding boxes
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_yolov8(image):
# Load YOLOv8 model
model = YOLO('yolov8n.pt') # Load YOLOv8 nano model
model.to('cpu')
model.eval()
# Run YOLO detection
results = model(image)
boxes, colors, names = parse_detections_yolov8(results[0])
detections_img = draw_detections(boxes, colors, names, 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
target_layers = [model.model.model[-2]] # Adjust the target layer if required
cam_image, renormalized_cam_image = generate_cam_image(model.model, target_layers, tensor, image, boxes)
# Combine results
final_image = np.hstack((image, cam_image, renormalized_cam_image))
caption = "Results using YOLOv8"
return Image.fromarray(final_image), caption |