yolov8热力图可视化

安装pytorch_grad_cam

pip install grad-cam

自动化生成不同层的bash脚本

# 循环10次，将i的值从0到9
for i in $(seq 0 13)
doecho "Running iteration $i";python yolov8_heatmap.py $i;
done

热力图生成python代码

import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
import torch, yaml, cv2, os, shutil
import numpy as np
np.random.seed(0)
import sys
import matplotlib.pyplot as plt
from tqdm import trange
from PIL import Image
from ultralytics.nn.tasks import DetectionModel as Model
from ultralytics.yolo.utils.torch_utils import intersect_dicts
# from ultralytics.yolo.data.augment import LetterBox
from ultralytics.yolo.utils.ops 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 ActivationsAndGradientsdef letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):# Resize and pad image while meeting stride-multiple constraintsshape = im.shape[:2]  # current shape [height, width]if isinstance(new_shape, int):new_shape = (new_shape, new_shape)# Scale ratio (new / old)r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])if not scaleup:  # only scale down, do not scale up (for better val mAP)r = min(r, 1.0)# Compute paddingratio = r, r  # width, height ratiosnew_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh paddingif auto:  # minimum rectangledw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh paddingelif scaleFill:  # stretchdw, dh = 0.0, 0.0new_unpad = (new_shape[1], new_shape[0])ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratiosdw /= 2  # divide padding into 2 sidesdh /= 2if shape[::-1] != new_unpad:  # resizeim = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))left, right = int(round(dw - 0.1)), int(round(dw + 0.1))im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add borderreturn im, ratio, (dw, dh)class yolov8_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'].namescsd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32model = Model(cfg, ch=3, nc=len(model_names)).to(device)csd = intersect_dicts(csd, model.state_dict(), exclude=['anchor'])  # intersectmodel.load_state_dict(csd, strict=False)  # loadmodel.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.int64)self.__dict__.update(locals())def post_process(self, result):logits_ = result[:, 4:]boxes_ = result[:, :4]sorted, indices = torch.sort(logits_.max(1)[0], descending=True)return torch.transpose(logits_[0], dim0=0, dim1=1)[indices[0]], torch.transpose(boxes_[0], dim0=0, dim1=1)[indices[0]], xywh2xyxy(torch.transpose(boxes_[0], dim0=0, dim1=1)[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 imgdef crop(self,box,img):xmin, ymin, xmax, ymax = list(map(int, list(box)))return img[ymin:ymax,xmin:xmax].copy()def __call__(self, img_path, save_path):# remove dir if existif os.path.exists(save_path):shutil.rmtree(save_path)# make dir if not existos.makedirs(save_path, exist_ok=True)# img processimage = cv2.imread(img_path)img,(wratio,hratio), (dw, dh) = letterbox(image)img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img = np.float32(img) / 255.0image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)image = np.float32(image) / 255.0tensor = torch.from_numpy(np.transpose(img, axes=[2, 0, 1])).unsqueeze(0).to(self.device)# init ActivationsAndGradientsgrads = ActivationsAndGradients(self.model, self.target_layers, reshape_transform=None)# get ActivationsAndResultresult = grads(tensor)activations = grads.activations[0].cpu().detach().numpy()# postprocess to yolo outputpost_result, pre_post_boxes, post_boxes = self.post_process(result[0])for i in trange(int(post_result.size(0) * self.ratio)):if float(post_result[i].max()) < self.conf_threshold:breakself.model.zero_grad()# get max probability for this predictionif self.backward_type == 'class' or self.backward_type == 'all':score = post_result[i].max()score.backward(retain_graph=True)if self.backward_type == 'box' or self.backward_type == 'all':for j in range(4):score = pre_post_boxes[i, j]score.backward(retain_graph=True)# process heatmapif self.backward_type == 'class':gradients = grads.gradients[0]elif self.backward_type == 'box':gradients = grads.gradients[0] + grads.gradients[1] + grads.gradients[2] + grads.gradients[3]else:gradients = grads.gradients[0] + grads.gradients[1] + grads.gradients[2] + grads.gradients[3] + grads.gradients[4]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()# 如果不生成图像 注释掉下面两行if (saliency_map_max - saliency_map_min) == 0:continuesaliency_map = (saliency_map - saliency_map_min) / (saliency_map_max - saliency_map_min)saliency_map = cv2.resize(saliency_map[int(dh):-int(dh),:], (image.shape[1],image.shape[0]))winv_ratio = 1.0 / wratiohinv_ratio = 1.0 / hratiodet_box_restored = [int((post_boxes[i][0] - (dw+0.1)) * winv_ratio),int((post_boxes[i][1] - (dh+0.1)) * hinv_ratio),int((post_boxes[i][2] - (dw-0.1)) * winv_ratio),int((post_boxes[i][3] - (dh-0.1)) * hinv_ratio)]det_box_restored = [int(coord) for coord in det_box_restored]# add heatmap and box to imagecam_image = show_cam_on_image(image.copy(), saliency_map, use_rgb=True)crop_cam_image = self.crop(det_box_restored,cam_image)crop_cam_image = Image.fromarray(crop_cam_image)crop_cam_image.save(f'{save_path}/{i}_crop.png')cam_image = self.draw_detections(det_box_restored, self.colors[int(post_result[i, :].argmax())], f'{self.model_names[int(post_result[i, :].argmax())]} {float(post_result[i].max()):.2f}', cam_image)cam_image = Image.fromarray(cam_image)cam_image.save(f'{save_path}/{i}.png')def get_params():params = {'weight': '../runs/detect/my-person73-small/weights/best.pt','cfg': 'models/small-yolov8.yaml','device': 'cuda:0','method': 'GradCAM', # GradCAMPlusPlus, GradCAM, XGradCAM'layer': f'model.model[{sys.argv[1]}]','backward_type': 'all', # class, box, all'conf_threshold': 0.6, # 0.6'ratio': 0.02 # 0.02-0.1}return paramsif __name__ == '__main__':model = yolov8_heatmap(**get_params())model(r'1.jpg', f'result/{sys.argv[1]}')