import time

import numpy as np
import torch
from PIL import Image

from utils.utils import (letterbox_image,
                         non_max_suppression, yolo_correct_boxes)
from yolo import YOLO

'''
该FPS测试不包括前处理（归一化与resize部分）、绘图。
包括的内容为：网络推理、得分门限筛选、非极大抑制。
使用'img/street.jpg'图片进行测试，该测试方法参考库https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch

video.py里面测试的FPS会低于该FPS，因为摄像头的读取频率有限，而且处理过程包含了前处理和绘图部分。
'''
class FPS_YOLO(YOLO):
    def get_FPS(self, image, test_interval):
        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()
            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=self.iou)
            try:
                batch_detections = batch_detections[0].cpu().numpy()
                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)
                
                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)
            except:
                pass
                
        t1 = time.time()
        for _ in range(test_interval):
            with torch.no_grad():
                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=self.iou)
                try:
                    batch_detections = batch_detections[0].cpu().numpy()
                    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)
                    
                    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)
                except:
                    pass

        t2 = time.time()
        tact_time = (t2 - t1) / test_interval
        return tact_time
        
yolo = FPS_YOLO()
test_interval = 100
img = Image.open('img/street.jpg')
tact_time = yolo.get_FPS(img, test_interval)
print(str(tact_time) + ' seconds, ' + str(1/tact_time) + 'FPS, @batch_size 1')
