From ff81789c09ad9eb7a42108d03050835d8c57881e Mon Sep 17 00:00:00 2001 From: Bubbliiiing <47347516+bubbliiiing@users.noreply.github.com> Date: Tue, 21 Sep 2021 19:52:19 +0800 Subject: [PATCH] Delete get_dr_txt.py --- get_dr_txt.py | 137 -------------------------------------------------- 1 file changed, 137 deletions(-) delete mode 100644 get_dr_txt.py diff --git a/get_dr_txt.py b/get_dr_txt.py deleted file mode 100644 index eee3fb1..0000000 --- a/get_dr_txt.py +++ /dev/null @@ -1,137 +0,0 @@ -#-------------------------------------# -# mAP所需文件计算代码 -# 具体教程请查看Bilibili -# Bubbliiiing -#-------------------------------------# -import os - -import numpy as np -import torch -from PIL import Image -from tqdm import tqdm - -from utils.utils import letterbox_image, non_max_suppression, yolo_correct_boxes -from yolo import YOLO - - -''' -这里设置的门限值较低是因为计算map需要用到不同门限条件下的Recall和Precision值。 -所以只有保留的框足够多，计算的map才会更精确，详情可以了解map的原理。 -计算map时输出的Recall和Precision值指的是门限为0.5时的Recall和Precision值。 - -此处获得的./input/detection-results/里面的txt的框的数量会比直接predict多一些，这是因为这里的门限低， -目的是为了计算不同门限条件下的Recall和Precision值，从而实现map的计算。 - -这里的self.iou指的是非极大抑制所用到的iou，具体的可以了解非极大抑制的原理， -如果低分框与高分框的iou大于这里设定的self.iou，那么该低分框将会被剔除。 - -可能有些同学知道有0.5和0.5:0.95的mAP，这里的self.iou=0.5不代表mAP0.5。 -如果想要设定mAP0.x，比如设定mAP0.75，可以去get_map.py设定MINOVERLAP。 -''' -class mAP_Yolo(YOLO): - #---------------------------------------------------# - # 检测图片 - #---------------------------------------------------# - def detect_image(self,image_id,image): - self.confidence = 0.01 - self.iou = 0.5 - f = open("./input/detection-results/"+image_id+".txt","w") - image_shape = np.array(np.shape(image)[0:2]) - - #---------------------------------------------------------# - # 给图像增加灰条，实现不失真的resize - # 也可以直接resize进行识别 - #---------------------------------------------------------# - if self.letterbox_image: - crop_img = np.array(letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))) - else: - crop_img = image.convert('RGB') - crop_img = crop_img.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC) - photo = np.array(crop_img,dtype = np.float32) / 255.0 - photo = np.transpose(photo, (2, 0, 1)) - #---------------------------------------------------------# - # 添加上batch_size维度 - #---------------------------------------------------------# - images = [photo] - - with torch.no_grad(): - images = torch.from_numpy(np.asarray(images)) - if self.cuda: - images = images.cuda() - - #---------------------------------------------------------# - # 将图像输入网络当中进行预测！ - #---------------------------------------------------------# - outputs = self.net(images) - output_list = [] - for i in range(3): - output_list.append(self.yolo_decodes[i](outputs[i])) - - #---------------------------------------------------------# - # 将预测框进行堆叠，然后进行非极大抑制 - #---------------------------------------------------------# - output = torch.cat(output_list, 1) - batch_detections = non_max_suppression(output, len(self.class_names), - conf_thres=self.confidence, - nms_thres=self.iou) - - #---------------------------------------------------------# - # 如果没有检测出物体，返回原图 - #---------------------------------------------------------# - try: - batch_detections = batch_detections[0].cpu().numpy() - except: - return - - #---------------------------------------------------------# - # 对预测框进行得分筛选 - #---------------------------------------------------------# - top_index = batch_detections[:,4] * batch_detections[:,5] > self.confidence - top_conf = batch_detections[top_index,4]*batch_detections[top_index,5] - top_label = np.array(batch_detections[top_index,-1],np.int32) - top_bboxes = np.array(batch_detections[top_index,:4]) - top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1) - - #-----------------------------------------------------------------# - # 在图像传入网络预测前会进行letterbox_image给图像周围添加灰条 - # 因此生成的top_bboxes是相对于有灰条的图像的 - # 我们需要对其进行修改，去除灰条的部分。 - #-----------------------------------------------------------------# - if self.letterbox_image: - boxes = yolo_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape) - else: - top_xmin = top_xmin / self.model_image_size[1] * image_shape[1] - top_ymin = top_ymin / self.model_image_size[0] * image_shape[0] - top_xmax = top_xmax / self.model_image_size[1] * image_shape[1] - top_ymax = top_ymax / self.model_image_size[0] * image_shape[0] - boxes = np.concatenate([top_ymin,top_xmin,top_ymax,top_xmax], axis=-1) - - for i, c in enumerate(top_label): - predicted_class = self.class_names[c] - score = str(top_conf[i]) - - top, left, bottom, right = boxes[i] - f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom)))) - - f.close() - return - -yolo = mAP_Yolo() -image_ids = open('VOCdevkit/VOC2007/ImageSets/Main/test.txt').read().strip().split() - -if not os.path.exists("./input"): - os.makedirs("./input") -if not os.path.exists("./input/detection-results"): - os.makedirs("./input/detection-results") -if not os.path.exists("./input/images-optional"): - os.makedirs("./input/images-optional") - - -for image_id in tqdm(image_ids): - image_path = "./VOCdevkit/VOC2007/JPEGImages/"+image_id+".jpg" - image = Image.open(image_path) - # 开启后在之后计算mAP可以可视化 - # image.save("./input/images-optional/"+image_id+".jpg") - yolo.detect_image(image_id,image) - -print("Conversion completed!") -- GitLab