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video.py
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import argparse
import time
from pathlib import Path
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, 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
from dis_count import *
import camera_configs
fpss=0.0
def detect(save_img=False):
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = True
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
# (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Initialize
set_logging()
device = select_device(opt.device)
half = device.type != 'cpu' # half precision only supported on CUDA
# print(half)
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap,imgg,im0ss,aaaa,bbbb in dataset:
# print(img.shape) (1, 3, 480, 640)
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0 # torch.Size([1, 3, 480, 640])
imgg = torch.from_numpy(imgg).to(device)
imgg = imgg.half() if half else imgg.float() # uint8 to fp16/32
imgg /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
if imgg.ndimension() == 3:
imgg = imgg.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
predd = model(imgg, augment=opt.augment)[0]
# print('********************')
# print(opt.augment)
# print('********************')
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
predd = non_max_suppression(predd, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# print(pred)
def ved(pred):
for i, det in enumerate(pred): # detections per image
dis_box=[]
if webcam: # batch_size >= 1
p, s, im0 = Path(path[i]), '%g: ' % i, im0s[i].copy()
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
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
# print(det)
dddd=0.
for *xyxy, conf, cls in reversed(det):
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
x=xywh[0]
y=xywh[1]
# dddd=dis_co(aaaa,bbbb,x,y)
label = '%s %.2f %.2f' % (names[int(cls)], conf,dddd)
# print(label)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
return im0
def vedd(pred):
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = Path(path[i]), '%g: ' % i, im0ss[i].copy()
save_path = str(save_dir / p.name)
txt_path = str(save_dir / 'labels' / p.stem) + (
'_%g' % dataset.frame if dataset.mode == 'video' else '')
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
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
return im0
#计算两针深度图 左帧img 右帧imgg
v1=ved(pred)
v2=vedd(predd)
cv2.imshow('0',v1)
cv2.imshow('1', v2)
# Process detections
# print(pred) # list
# for i, det in enumerate(pred): # detections per image
# if webcam: # batch_size >= 1
# p, s, im0 = Path(path[i]), '%g: ' % i, im0s[i].copy()
# else:
# p, s, im0 = Path(path), '', im0s
#
# save_path = str(save_dir / p.name)
# txt_path = str(save_dir / 'labels' / p.stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
# 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
# det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
#
# # Print results
# for c in det[:, -1].unique():
# n = (det[:, -1] == c).sum() # detections per class
# s += '%g %ss, ' % (n, names[int(c)]) # add to string
#
# # Write results
# for *xyxy, conf, cls in reversed(det):
# if save_txt: # Write to file
# xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
# line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
# with open(txt_path + '.txt', 'a') as f:
# f.write(('%g ' * len(line)).rstrip() % line + '\n')
#
# if save_img or view_img: # Add bbox to image
# label = '%s %.2f' % (names[int(cls)], conf)
# plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
#
# # Print time (inference + NMS)
# print('%sDone. (%.3fs)' % (s, t2 - t1))
# global fpss
# # Stream results
#
# cv2.namedWindow(str(p), cv2.WINDOW_NORMAL)
# fpss=(fpss+(1./(t2-t1)))/2
# im0 = cv2.putText(im0, "fps= %.2f" % (fpss), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
# cv2.imshow(str(p), im0)
# # 在这里让im0变成两个 这样的话重新展示会有两个框
# if cv2.waitKey(1) == ord('q'): # q to quit
# raise StopIteration
# for i, det in enumerate(predd): # detections per image
# if webcam: # batch_size >= 1
# p1, s1, im01 = Path(path[i]), '%g: ' % i, im0ss[i].copy()
# else:
# p1, s1, im01 = Path(path), '', im0ss
#
# txt_path = str(save_dir / 'labels' / p1.stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
# s1 += '%gx%g ' % imgg.shape[2:] # print string
# gn = torch.tensor(im01.shape)[[1, 0, 1, 0]] # normalization gain whwh
# if len(det):
# # Rescale boxes from img_size to im0 size
# det[:, :4] = scale_coords(imgg.shape[2:], det[:, :4], im01.shape).round()
#
# # Print results
# for c in det[:, -1].unique():
# n = (det[:, -1] == c).sum() # detections per class
# s1 += '%g %ss, ' % (n, names[int(c)]) # add to string
#
# # Write results
# for *xyxy, conf, cls in reversed(det):
# if save_txt: # Write to file
# xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
# line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
# with open(txt_path + '.txt', 'a') as f:
# f.write(('%g ' * len(line)).rstrip() % line + '\n')
#
# if save_img or view_img: # Add bbox to image
# label = '%s %.2f' % (names[int(cls)], conf)
# plot_one_box(xyxy, im01, label=label, color=colors[int(cls)], line_thickness=3)
#
# # Print time (inference + NMS)
# print('%sDone. (%.3fs)' % (s1, t2 - t1))
#
# # Stream results
#
# cv2.namedWindow(str(p1), cv2.WINDOW_NORMAL)
# cv2.imshow(str(p1), im01)
# # 在这里让im0变成两个 这样的话重新展示会有两个框
# if cv2.waitKey(1) == ord('q'): # q to quit
# raise StopIteration
print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='0,1', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()
print(opt)
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
detect()
strip_optimizer(opt.weights)
else:
detect()