#-------------------------------------#
#       mAP所需文件计算代码
#       具体教程请查看Bilibili
#       Bubbliiiing
#-------------------------------------#
import cv2
import numpy as np
import colorsys
import os
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from yolo import YOLO
from nets.yolo4 import YoloBody
from PIL import Image,ImageFont, ImageDraw
from utils.utils import non_max_suppression, bbox_iou, DecodeBox,letterbox_image,yolo_correct_boxes

class mAP_Yolo(YOLO):
    #---------------------------------------------------#
    #   检测图片
    #---------------------------------------------------#
    def detect_image(self,image_id,image):
        self.confidence = 0.001
        f = open("./input/detection-results/"+image_id+".txt","w") 
        image_shape = np.array(np.shape(image)[0:2])

        crop_img = np.array(letterbox_image(image, (self.model_image_size[0],self.model_image_size[1])))
        photo = np.array(crop_img,dtype = np.float32)
        photo /= 255.0
        photo = np.transpose(photo, (2, 0, 1))
        photo = photo.astype(np.float32)
        images = []
        images.append(photo)
        images = np.asarray(images)

        with torch.no_grad():
            images = torch.from_numpy(images)
            if self.cuda:
                images = images.cuda()
            outputs = self.net(images)
            
        output_list = []
        for i in range(3):
            output_list.append(self.yolo_decodes[i](outputs[i]))
        output = torch.cat(output_list, 1)
        batch_detections = non_max_suppression(output, len(self.class_names),
                                                conf_thres=self.confidence,
                                                nms_thres=0.3)

        try:
            batch_detections = batch_detections[0].cpu().numpy()
        except:
            return image
            
        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)

        # 去掉灰条
        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)

        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 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(image_id," done!")
    

print("Conversion completed!")
