FPS_test.py

import os
import time

import numpy as np
import torch
from PIL import Image
from torch.autograd import Variable
from tqdm import tqdm

from ssd import SSD
from utils.box_utils import letterbox_image, ssd_correct_boxes

'''
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。
'''
MEANS = (104, 117, 123)
class FPS_SSD(SSD):
    def get_FPS(self, image, test_interval):
        # 调整图片使其符合输入要求
        image_shape = np.array(np.shape(image)[0:2])
        crop_img = np.array(letterbox_image(image, (self.input_shape[1],self.input_shape[0])))
        photo = np.array(crop_img,dtype = np.float64)
        # 图片预处理，归一化
        with torch.no_grad():
            photo = Variable(torch.from_numpy(np.expand_dims(np.transpose(crop_img-MEANS,(2,0,1)),0)).type(torch.FloatTensor))
            if self.cuda:
                photo = photo.cuda()
            preds = self.net(photo)
            top_conf = []
            top_label = []
            top_bboxes = []
            for i in range(preds.size(1)):
                j = 0
                while preds[0, i, j, 0] >= self.confidence:
                    score = preds[0, i, j, 0]
                    label_name = self.class_names[i-1]
                    pt = (preds[0, i, j, 1:]).detach().numpy()
                    coords = [pt[0], pt[1], pt[2], pt[3]]
                    top_conf.append(score)
                    top_label.append(label_name)
                    top_bboxes.append(coords)
                    j = j + 1
            # 将预测结果进行解码
            if len(top_conf)>0:
                top_conf = np.array(top_conf)
                top_label = np.array(top_label)
                top_bboxes = np.array(top_bboxes)
                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 = ssd_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.input_shape[0],self.input_shape[1]]),image_shape)

        t1 = time.time()
        for _ in range(test_interval):
            with torch.no_grad():
                preds = self.net(photo)
                top_conf = []
                top_label = []
                top_bboxes = []
                for i in range(preds.size(1)):
                    j = 0
                    while preds[0, i, j, 0] >= self.confidence:
                        score = preds[0, i, j, 0]
                        label_name = self.class_names[i-1]
                        pt = (preds[0, i, j, 1:]).detach().numpy()
                        coords = [pt[0], pt[1], pt[2], pt[3]]
                        top_conf.append(score)
                        top_label.append(label_name)
                        top_bboxes.append(coords)
                        j = j + 1
                # 将预测结果进行解码
                if len(top_conf)>0:
                    top_conf = np.array(top_conf)
                    top_label = np.array(top_label)
                    top_bboxes = np.array(top_bboxes)
                    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 = ssd_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.input_shape[0],self.input_shape[1]]),image_shape)

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