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detection.py
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#-*- coding: UTF-8 -*-
import os
import torch
from torch.autograd import Variable
import numpy as np
import cv2
import matplotlib
from data import VOC_CLASSES as labels
import time
import data.config as cfg
import os
from ssd_mobilenetv2 import build_ssd
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"#设置型号
os.environ["CUDA_VISIBLE_DEVICES"] = "0"#设置使用哪块GPU
if torch.cuda.is_available():
torch.set_default_tensor_type('torch.cuda.FloatTensor')
num_classes = 10#VOC2012的种类，需要修改
image_size = 300#使用的SSD图片大小
def detection_video(path,weight):#识别的video
global image_size,tracker_rgb,init_num
flag = 0
net = build_ssd('test', 300, num_classes)
net.eval()
net.load_weights(weight)#导入模型参数
cap = cv2.VideoCapture(path)
fps = cap.get(cv2.CAP_PROP_FPS)
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
init_num=0
t4=time.time()
while cap.isOpened():
ret,image = cap.read()
if init_num==0:#初始化程序
flag += 1
if ret == False:
print("video is over!")
break
if flag % 3 != 0:#每三帧处理一次，为了防止jetson nano速率不够
continue
t0 = time.time()
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
resize_image = cv2.resize(image, (300, 300)).astype(np.float32)
resize_image -= (104, 117, 123)#对SSD实现均值化
resize_image = resize_image.astype(np.float32)#转为float32
resize_image = resize_image[:, :, ::-1].copy()
torch_image = torch.from_numpy(resize_image).permute(2, 0, 1)#重新排列传入torch
input_image = Variable(torch_image.unsqueeze(0))#扩展第一列
if torch.cuda.is_available():
input_image = input_image.cuda()#设置为CUDA形式
out = net(input_image)#传入到模型当中
colors = cfg.COLORS
detections = out.data
scale = torch.Tensor(rgb_image.shape[1::-1]).repeat(2)#[ 起始下标 : 终止下标 : 间隔距离 ]
rgb_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)#转化为BGR参数
idx_obj = -1#初始为-1
center_point=[0,0]
gallery_best_draw=[0,0,0,0]
for i in range(detections.size(1)):#获取所有的参数
j = 0#都要循环类的次数
#print(detections.size())
#print(i)
if detections[0,i,j,0] >= 0.95:#设定阈值
idx_obj += 1#物体数+1
score = detections[0,i,j,0]#计算得分
label_name = labels[i-1]#得到名称
display_txt = '%s %.2f'%(label_name, score)#显示目标物体位置
pt = (detections[0,i,j,1:]*scale).cpu().numpy()#获取四个点位置
#j += 1
# 求得四个边角，并防止溢出
pt[0] = max(pt[0],0)
pt[1] = max(pt[1],0)
pt[2] = min(pt[2],size[1])
pt[3] = min(pt[3],size[0])
#print(pt[0],pt[3])
if abs(pt[2]-pt[0])*abs(pt[3]-pt[1])>500:
print((pt[2]-pt[0])*(pt[3]-pt[1]))
if (pt[0]+pt[2])/2>100 and (pt[1]+pt[3])/2>140 and (pt[0]+pt[1]+pt[2]+pt[3])/2>(center_point[0]+center_point[1]):#处理一帧中的最优点
center_point=[(pt[0]+pt[2])/2,(pt[1]+pt[3])/2]#更新最优点
gallery_best_draw=[pt[0],pt[1],pt[2],pt[3]]
#init_num=1
#print(pt[0],pt[1],pt[2],pt[3])
#print(center_point)
else:
print("error",(pt[2]-pt[0])*(pt[3]-pt[1]))
continue
color = colors[idx_obj%len(colors)]#选择颜色
textsize = cv2.getTextSize(display_txt, cv2.FONT_HERSHEY_COMPLEX, 1, 2)[0]#显示文本文字
text_x = int(pt[0])#文本位置
text_y = int(pt[1])
cv2.rectangle(rgb_image,(int(pt[0]), int(pt[1])),(int(pt[2]), int(pt[3])),color,4)#框选位置
cv2.putText(rgb_image, display_txt, (text_x + 4, text_y), cv2.FONT_HERSHEY_COMPLEX, 1,(255 - color[0], 255 - color[1], 255 - color[2]), 2)#输出结果
if gallery_best_draw[0]!=0:
track_roi=(gallery_best_draw[0],gallery_best_draw[1],abs(gallery_best_draw[2]-gallery_best_draw[0]),abs(gallery_best_draw[3]-gallery_best_draw[1]))
print("track_roi:",track_roi)
try:
tracker_rgb=cv2.TrackerMOSSE_create()#重置
tracker_rgb.init(rgb_image, track_roi)#初始化对应的参数
except:
pass
#t1 = time.time()
#cv2.putText(rgb_image, "FPS: %.2f" % (1 / (t1 - t0)), (5, 30), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 255, 255), 2)
#cv2.imshow("result",rgb_image)
elif init_num==1:
t0 = time.time()
images = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
images = cv2.cvtColor(images, cv2.COLOR_RGB2BGR)#转化为BGR参数
rgb_image=images.copy()
(success, box) = tracker_rgb.update(rgb_image)
if time.time()-t4>10:
init_num=0
t4=time.time()
#print(time.time()-t4)
if success:
(x, y, w, h) = [int(v) for v in box]
csrt_best_draw=[int(x),int(y),int(x+w),int(y+h)]
cv2.rectangle(rgb_image,tuple(csrt_best_draw),color,4)#框选位置
t1 = time.time()
cv2.putText(rgb_image, "FPS: %.2f" % (1 / (t1 - t0)), (5, 30), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 255, 255), 2)
cv2.imshow("result",rgb_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
cap.release()
cv2.destroyAllWindows()
def csrt():
global tracker_rgb
OPENCV_OBJECT_TRACKERS = {
"kcf": cv2.TrackerKCF_create,
"mosse": cv2.TrackerMOSSE_create
}
tracker_rgb = OPENCV_OBJECT_TRACKERS["mosse"]()
if __name__ == "__main__":
weight = 'weights/ssd_mobilenetv2/mobilenetv2_final.pth'
#path = r"test_images/example.jpg"
path = r"test_videos/tests.mp4"
csrt()
detection_video(path,weight)