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pyqt5_detect.py
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pyqt5_detect.py
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import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
import sys
import clr
#C#库的地址
#clr.AddReference(r'C:\Users\64504\Desktop\ptzcontrol.csharp\ConsoleApplication1\bin\Debug\SDK.IPC.dll')
#from SDK.IPC import *
#from System import *
#from System.Threading import *
import argparse
import time
from pathlib import Path
cam_ip = "183.192.69.170"
camBG_port = "7502"
camCS_port = "7702"
cam_port = camCS_port
cam_user = "admin"
cam_psw = "SMUwm_007"
weights= r'./ship-lin.pt'
class pyqt_detect_api:
def __init__(self,vWidth,vHeight):
# 由于视频像素是1920*1080 因此对应qt界面1200*900的比例是1.6
self.scale = 1.6 # 监控像素缩放到ui像素比例
# 相机中心点
self.midX = (vWidth * self.scale) / 2
self.midY = (vHeight * self.scale) / 2
self.cam_speed = 1 # 相机转动速度
# 检测框坐标
self.trackX = 0
self.trackY = 0
self.predList = [] # 保存每一帧检测到的坐标
self.TRACKFLAG = False
# 上一帧坐标
self.tracked_X = 0
self.tracked_Y = 0
# C#相机库
ipc = IPCControl()
ipcKey = "key11111111111111111111111111111111111"
# ipcConParam = IPCConnectParam("183.192.69.170", 7701, "admin", "SMUwm_007")
ipcConParam = IPCConnectParam(cam_ip, int(cam_port) - 1, cam_user, cam_psw)
self.ipcCon = ipc.GetPtzConnecttedControl(1, ipcConParam, ipcKey)
# ipcCon.Move(EnumPtzMoveType.moveleft);
self.ipcCon.Move(EnumPtzMoveType.movestop) # 让相机初始化时发出一个停止转动的命令
def pyqt_detect(self,show_frame,tracked_X,tracked_Y):
self.tracked_X = tracked_X
self.tracked_Y = tracked_Y
print("XXXXXXXX",self.tracked_X)
print("YYYYYYYY",self.tracked_Y)
source = r'rtsp://' + cam_user + r':' + cam_psw + r'@' + cam_ip + r':' + cam_port + r'/id=1'
# source=r'rtsp://admin:[email protected]/id=1'
# weights= r'./yolov5x.pt'
view_img = True
imgsz = 640
# = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://'))
# Initialize
set_logging()
device = select_device() # 获取设备
# 如果设备为GPU 使用float16
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
# 加载float32模型，确保用户设定的输入图片分辨率能整除32（如不能则调整为能整除返回）
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # check img_size
if half:
# 设置float16
model.half() # to FP16
# Set Dataloader
# 通过不同的输入源来设置不同的数据加载方式
vid_path, vid_writer = None, None
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride)
else:
save_img = True
# 如果哦检测视频的时候想显示出来，可以在这里加一行 view_img = True
view_img = True
dataset = LoadImages(source, img_size=imgsz, stride=stride)
# Get names and colors
# 获取类别名字
names = model.module.names if hasattr(model, 'module') else model.names
# 设置画框的颜色
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
t0 = time.time()
# 进行一次前向推理,测试程序是否正常
"""
path 图片/视频路径
img 进行resize+pad之后的图片
img0 原size图片
cap 当读取图片时为None，读取视频时为视频源
"""
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
# 图片也设置为Float16
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
# 没有batch_size的话则在最前面添加一个轴
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=False)[0]
"""
前向传播 返回pred的shape是(1, num_boxes, 5+num_class)
h,w为传入网络图片的长和宽，注意dataset在检测时使用了矩形推理，所以这里h不一定等于w
num_boxes = h/32 * w/32 + h/16 * w/16 + h/8 * w/8
pred[..., 0:4]为预测框坐标
预测框坐标为xywh(中心点+宽长)格式
pred[..., 4]为objectness置信度
pred[..., 5:-1]为分类结果
"""
# Apply NMS
pred = non_max_suppression(pred, 0.25, 0.45, classes=None, agnostic=False)
t2 = time_synchronized()
# Process detections
# 检测每一帧图片
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
p = Path(p) # to Path
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det):
# Rescale boxes from img_size to im0 size
# 调整预测框的坐标：基于resize+pad的图片的坐标-->基于原size图片的坐标
# 此时坐标格式为xyxy
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
# 打印检测到的类别数量
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
countCls = 0
self.predList.clear()
checkTracked = False
for *xyxy, conf, cls in reversed(det):
# 画框
label = f'{names[int(cls)]} {conf:.2f}'
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
c1, c2 = (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3]))
# 判断tracked是否与上一针存在联系
cX = 0
cY = 0
# print(self.TRACKFLAG)
# print(checkTracked)
#print(abs(self.tracked_X - cX), abs(self.tracked_Y - cY))
print(abs(self.tracked_X - cX), abs(self.tracked_Y - cY))
if self.TRACKFLAG:
cX = (c1[0] + c2[0]) / 2
cY = (c1[1] + c2[1]) / 2
# print((c1[0]+c2[0])/2,(c1[1]+c2[1])/2)
# 如果追踪到了则按差值移动相机
print('loss:', self.tracked_X - self.midX)
if (self.tracked_X - self.midX < -10):
print('left:', self.tracked_X - self.midX)
self.ipcCon.Move(EnumPtzMoveType.moveleft, self.cam_speed)
self.ipcCon.Move(EnumPtzMoveType.movestop)
elif (self.tracked_X - self.midX > 10):
print('right:', self.tracked_X - self.midX)
self.ipcCon.Move(EnumPtzMoveType.moveright, self.cam_speed)
self.ipcCon.Move(EnumPtzMoveType.movestop)
else:
self.ipcCon.Move(EnumPtzMoveType.movestop)
# self.TRACKFLAG=False
if abs(self.tracked_X - cX) < 50 and abs(self.tracked_Y - cY) < 50:
self.tracked_X = cX
self.tracked_Y = cY
checkTracked = True
break
# print('tracked_X',self.tracked_X)
# print('tracked_Y',self.tracked_Y)
else:
checkTracked = False
self.ipcCon.Move(EnumPtzMoveType.movestop)
# 存储当前帧的检测框与下一帧关联起来
predBox = (c1, c2)
self.predList.append(predBox)
# print(c1)
# print("******************")
# print("ffffffffffffffffffffffff")
# print(self.predList[-1])
# print("ffffffffffffffffffffffff")
# print(label,c1,c2)
if checkTracked == False:
self.TRACKFLAG = False
print(self.TRACKFLAG)
# print(checkTracked)
# for pred in self.predList:
# print(pred[:1])
# Print time (inference + NMS)
# 打印前向传播+nms时间
# print(f'{s}Done. ({t2 - t1:.3f}s)')
# 调整显示的像素排列
cv2.cvtColor(im0, cv2.COLOR_BGR2RGB, im0)
show_frame(im0)