Delete get_dr_txt.py

get_dr_txt.py

-#-------------------------------------#
-#       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!")