热力图，批量处理_heatmapprocess

本帖主要是为了记录学习魔傀面具作者的热力图代码，YOLOV8-gradcam 热力图可视化 即插即用 不需要对源码做任何修改!_魔鬼面具的博客-CSDN博客

并将其适配批量处理，代码仅供参考，多海涵。

import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
import torch, yaml, cv2, os, shutil
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
from tqdm import trange
from PIL import Image
from models.yolo import Model
from utils.general import intersect_dicts
from utils.augmentations import letterbox
from utils.general import xywh2xyxy
from pytorch_grad_cam import GradCAMPlusPlus, GradCAM, XGradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from pytorch_grad_cam.activations_and_gradients import ActivationsAndGradients
 
class yolov5_heatmap:
    def __init__(self, weight, cfg, device, method, layer, backward_type, conf_threshold, ratio):
        device = torch.device(device)
        ckpt = torch.load(weight)
        model_names = ckpt['model'].names
        csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
        model = Model(cfg, ch=3, nc=len(model_names)).to(device)
        csd = intersect_dicts(csd, model.state_dict(), exclude=['anchor'])  # intersect
        model.load_state_dict(csd, strict=False)  # load
        model.eval()
        print(f'Transferred {len(csd)}/{len(model.state_dict())} items')
        
        target_layers = [eval(layer)]
        method = eval(method)
 
        colors = np.random.uniform(0, 255, size=(len(model_names), 3)).astype(np.int)
        self.__dict__.update(locals())
    
    def post_process(self, result):
        logits_ = result[..., 4:]
        boxes_ = result[..., :4]
        sorted, indices = torch.sort(logits_[..., 0], descending=True)
        return logits_[0][indices[0]], xywh2xyxy(boxes_[0][indices[0]]).cpu().detach().numpy()
 
    """def draw_detections(self, box, color, name, img):
        xmin, ymin, xmax, ymax = list(map(int, list(box)))
        cv2.rectangle(img, (xmin, ymin), (xmax, ymax), tuple(int(x) for x in color), 2)
        cv2.putText(img, str(name), (xmin, ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, tuple(int(x) for x in color), 2, lineType=cv2.LINE_AA)
        return img"""
        #此处控制是否显示预测框
 
    def __call__(self, img_path, save_path):
        # remove dir if exist
        if os.path.exists(save_path):
            shutil.rmtree(save_path)
        # make dir if not exist
        os.makedirs(save_path, exist_ok=True)
 
        # img process
        img = cv2.imread(img_path)
        img = letterbox(img)[0]
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = np.float32(img) / 255.0
        tensor = torch.from_numpy(np.transpose(img, axes=[2, 0, 1])).unsqueeze(0).to(self.device)
 
        # init ActivationsAndGradients
        grads = ActivationsAndGradients(self.model, self.target_layers, reshape_transform=None)
 
        # get ActivationsAndResult
        result = grads(tensor)
        activations = grads.activations[0].cpu().detach().numpy()
 
        # postprocess to yolo output
        post_result, post_boxes = self.post_process(result[0])
        for i in trange(int(post_result.size(0) * self.ratio)):
            if post_result[i][0] < self.conf_threshold:
                break
 
            self.model.zero_grad()
            if self.backward_type == 'conf':
                post_result[i, 0].backward(retain_graph=True)
            else:
                # get max probability for this prediction
                score = post_result[i, 1:].max()
                score.backward(retain_graph=True)
 
            # process heatmap
            gradients = grads.gradients[0]
            b, k, u, v = gradients.size()
            weights = self.method.get_cam_weights(self.method, None, None, None, activations, gradients.detach().numpy())
            weights = weights.reshape((b, k, 1, 1))
            saliency_map = np.sum(weights * activations, axis=1)
            saliency_map = np.squeeze(np.maximum(saliency_map, 0))
            saliency_map = cv2.resize(saliency_map, (tensor.size(3), tensor.size(2)))
            saliency_map_min, saliency_map_max = saliency_map.min(), saliency_map.max()
            if (saliency_map_max - saliency_map_min) == 0:
                continue
            saliency_map = (saliency_map - saliency_map_min) / (saliency_map_max - saliency_map_min)
 
            # add heatmap and box to image
            cam_image = show_cam_on_image(img.copy(), saliency_map, use_rgb=True)
            """cam_image = self.draw_detections(post_boxes[i], self.colors[int(post_result[i, 1:].argmax())], f'{self.model_names[int(post_result[i, 1:].argmax())]} {post_result[i][0]:.2f}', cam_image)"""
            cam_image = Image.fromarray(cam_image)
            cam_image.save(f'{save_path}/{i}.png')
 
def get_params():
    params = {
        'weight': r'权重绝对地址',
        'cfg': r'模型绝对地址',
        'device': 'cuda:0',
        'method': 'GradCAM', # GradCAMPlusPlus, GradCAM, XGradCAM
        'layer': 'model.model[-2]',
        'backward_type': 'class', # class or conf
        'conf_threshold': 0.6, # 0.6
        'ratio': 0.02 # 0.02-0.1
    }
    return params
 
#################   要使用热力图需要将YOLO.py文件中所有的inplace设置为False,默认是True  #############################################
 
if __name__ == '__main__':
    model = yolov5_heatmap(**get_params())
    #model(r'图片绝对地址', 'result')#单张处理
 
    path=r"图片绝对地址"
    print(path)
    path1 = os.listdir(path)
    for i1 in path1:
        i2="result/"+str(i1)
        i1=os.path.join(path,i1)
        model(i1,i2)#多张处理