heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB) image = cv2.resize(image, (w, h)) # 调整原始图像的大小与热图相同 image = image / image.max() heatmap = heatmap / heatmap.max() result = 0.4 * heatmap + 0.6 * image result = result / result.max() plt.figure() plt.imshow...
它将热图叠加在原始图像上,保存并显示新的可视化效果。 def save_and_display_gradcam(img_path, heatmap, cam_path="save_cam_image.jpg", alpha=0.4): ## Loading the original image img = keras.utils.load_img(img_path) img = keras.utils.img_to_array(img) ## Rescale heatmap to a range 0...
importcv2importnumpyasnp# 1. 使用 OpenCV 读取原始图像img=cv2.imread('your_image_path_here.jpg')# 2. 调整 Grad-CAM 热图的大小以匹配原始图像的大小heatmap=cv2.resize(grad_cam.cpu().numpy(),(img.shape[1],img.shape[0]))# 3. 将 Grad-CAM 热图应用到原始图像上heatmap=(heatmap-heatmap.m...
cv2.waitKey(0) cv2.destroyAllWindows() 在上面的代码中,我们首先加载了YOLOv8模型和YOLOv8-Grad-CAM插件。然后,我们读取了一张测试图像,并使用YOLOv8模型进行预测。最后,我们调用generate_heatmap函数生成热力图,并使用OpenCV库显示热力图。 YOLOv8-Grad-CAM插件的优点在于它不需要对YOLOv8的源码做任何修改,可以即...
Github上的这个heatmap库挺好用的，我做分割的时候一直在用它来产生heatmap GitHub - jacobgil/pytorch-...
import torch.nn.functional as Fimport matplotlib.pyplot as plt# weight the channels by corresponding gradientsfor i in range(activations.size()[1]): activations[:, i, :, :] *= pooled_gradients[i]# average the channels of the activationsheatmap = torch.mean(activations, dim=1).squeeze...
return heatmap.numpy() 输出: 使用模型为图像创建热图 ## Preparing the image img_array = preprocess_input(get_img_array(img_path, size=img_size)) ## Making the model with imagenet dataset model = model_builder(weights="imagenet")
# Resize the heatmap to the same size as the input image and defines # a resample algorithm for increasing image resolution # we need heatmap.detach() because it can't be converted to numpy array while # requiring gradients overlay=to_pil_image(heatmap.detach(), mode='F') ...
可以看出,heatmap 的高亮区域,似乎只出现在猫猫头上的部分区域,有聪明的同学知道这是为什么吗?(提示:ImageNet-1k 数据集中,猫的种类有 12 种;判别性区域) 再来看看换用更大一点的 DeiT-base 会怎么样呢? 关注的区域变了,甚至一些似乎不在猫猫身上了,是为什么呢(想想 token mixer,或者有没有可能是分类错误呢...
# Apply any colormap you want cmap = colormaps['jet'] overlay = (255 * cmap(np.asarray(overlay) ** 2)[:, :, :3]).astype(np.uint8) # Plot the heatmap on the same axes, # but with alpha < 1 (this defines the transparency of the heatmap) ...