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import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
import torch
import cv2
import numpy as np
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 PIL import Image
import gradio as gr
# Global Color Palette
COLORS = np.random.uniform(0, 255, size=(80, 3))
# Function to parse YOLO detections
def parse_detections(results):
detections = results.pandas().xyxy[0].to_dict()
boxes, colors, names = [], [], []
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)
return boxes, colors, names
# Draw bounding boxes and labels
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
# Main function for Grad-CAM visualization
def process_image(image):
image = np.array(image)
image = cv2.resize(image, (640, 640))
rgb_img = image.copy()
img_float = np.float32(image) / 255
# Image transformation
transform = transforms.ToTensor()
tensor = transform(img_float).unsqueeze(0)
# Load YOLOv5 model
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([rgb_img])
boxes, colors, names = parse_detections(results)
detections_img = draw_detections(boxes, colors, names, rgb_img.copy())
# Grad-CAM visualization
cam = EigenCAM(model, target_layers)
grayscale_cam = cam(tensor)[0, :, :]
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)
# Concatenate images
final_image = np.hstack((rgb_img, cam_image, renormalized_cam_image))
return Image.fromarray(final_image)
# Gradio Interface
interface = gr.Interface(
fn=process_image,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=gr.Image(type="pil", label="Result"),
title="Visualising the key image features that drive decisions with our explainable AI tool.",
description="XAI: Upload an image to visualize object detection of your models."
)
if __name__ == "__main__":
interface.launch()
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