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regions_detect_ray.py
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
通过向右射线相交的数量进行判断行人是否在区域内
"""
import argparse
import os
import sys
from pathlib import Path
import imutils
import torch
import torch.backends.cudnn as cudnn
import numpy as np
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync
@torch.no_grad()
def run(
weights=ROOT / 'yolov5s.pt', # model.pt path(s)
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
project=ROOT / 'runs/detect', # save results to project/name
name='exp', # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
):
source = str(source)
save_img = not nosave and not source.endswith('.txt') # save inference images
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
if is_url and is_file:
source = check_file(source) # download
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
stride, names, pt = model.stride, model.names, model.pt
imgsz = check_img_size(imgsz, s=stride) # check image size
# Dataloader
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, auto=pt)
bs = len(dataset) # batch_size
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
seen, windows, dt = 0, [], [0.0, 0.0, 0.0]
ray_classes = 2 #选择安全区域类型，1为一个安全区域，2为两个
for path, im, im0s, vid_cap, s in dataset:
t1 = time_sync()
# mask for certain region
# # 1,2,3,4 分别对应左上，右上，右下，左下四个点
# hl1 = 4.2 / 10 # 监测区域高度距离图片顶部比例
# wl1 = 1.6 / 10 # 监测区域高度距离图片左部比例
# hl2 = 2.5 / 10 # 监测区域高度距离图片顶部比例
# wl2 = 6.8 / 10 # 监测区域高度距离图片左部比例
# hl4 = 9.9 / 10 # 监测区域高度距离图片顶部比例
# wl4 = 2.5 / 10 # 监测区域高度距离图片左部比例
# hl3 = 7.4 / 10 # 监测区域高度距离图片顶部比例
# wl3 = 9.4 / 10 # 监测区域高度距离图片左部比例
poly1 = [[933, 103], [209, 446], [1788, 977], [1878, 232], [935, 103]] #四边形区域1
poly2 = [[1121, 217], [751, 512], [1372, 773], [1624, 335], [1122, 218]] #四边形区域2
pts = [poly1, poly2]
if ray_classes ==1:
ima_w = 480
ima_h = 270
# 1,2,3,4 分别对应左上，右上，右下，左下四个点
x11, y11, x12, y12, x13, y13, x14, y14 = 947, 108, 235, 457, 818, 856, 1750, 179
hl1 = round(y11 / ima_h, 2) # 监测区域高度距离图片顶部比例
wl1 = round(x11 / ima_w, 2) # 监测区域高度距离图片左部比例
hl2 = round(y12 / ima_h, 2) # 监测区域高度距离图片顶部比例
wl2 = round(x12 / ima_w, 2) # 监测区域高度距离图片左部比例
hl3 = round(y13 / ima_h, 2) # 监测区域高度距离图片顶部比例
wl3 = round(x13 / ima_w, 2) # 监测区域高度距离图片左部比例
hl4 = round(y14 / ima_h, 2) # 监测区域高度距离图片顶部比例
wl4 = round(x14 / ima_w, 2) # 监测区域高度距离图片左部比例
im = torch.from_numpy(im).to(device)
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
t2 = time_sync()
dt[0] += t2 - t1
# Inference
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
# NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
dt[2] += time_sync() - t3
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
# if webcam: # batch_size >= 1
# p, im0, frame = path[i], im0s[i].copy(), dataset.count
# s += f'{i}: '
# else:
# p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
# *************************************************************
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
if ray_classes==1:
cv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),
cv2.FONT_HERSHEY_SIMPLEX,
1.0, (255, 255, 0), 2, cv2.LINE_AA)
pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)], # pts1
[int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)], # pts2
[int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)], # pts3
[int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32) # pts4
# pts = pts.reshape((-1, 1, 2))
zeros = np.zeros((im0.shape), dtype=np.uint8)
mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))
im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)
cv2.polylines(im0, [pts], True, (255, 255, 0), 3)
# plot_one_box(dr, im0, label='Detection_Region', color=(0, 255, 0), line_thickness=2)
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
if ray_classes == 1:
cv2.putText(im0, "Detection_Region", (int(im0.shape[1] * wl1 - 5), int(im0.shape[0] * hl1 - 5)),
cv2.FONT_HERSHEY_SIMPLEX,
1.0, (255, 255, 0), 2, cv2.LINE_AA)
pts = np.array([[int(im0.shape[1] * wl1), int(im0.shape[0] * hl1)], # pts1
[int(im0.shape[1] * wl2), int(im0.shape[0] * hl2)], # pts2
[int(im0.shape[1] * wl3), int(im0.shape[0] * hl3)], # pts3
[int(im0.shape[1] * wl4), int(im0.shape[0] * hl4)]], np.int32) # pts4
# pts = pts.reshape((-1, 1, 2))
zeros = np.zeros((im0.shape), dtype=np.uint8)
mask = cv2.fillPoly(zeros, [pts], color=(0, 165, 255))
im0 = cv2.addWeighted(im0, 1, mask, 0.2, 0)
cv2.polylines(im0, [pts], True, (255, 255, 0), 3)
# ********************************************************
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
# Print results
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
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 save_conf else (cls, *xywh) # label format
with open(f'{txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or save_crop or view_img: # Add bbox to image
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
print(torch.tensor(xyxy).view(1, 4))
pos = torch.tensor(xyxy).view(1, 4).tolist()[0]#脚底左下角做定位
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4))).view(-1).tolist()#中心点做定位
people_pos = [pos[0],pos[3]]
cv2.circle(im0, (int(pos[0]),int(pos[3])), 3, (0, 0, 255),15)
print(people_pos)
print('***************')
annotator.box_label(xyxy, label, color=(56, 56, 56))
print(colors(c,True))
is_in = is_in_poly(people_pos, pts)
if is_in:
annotator.box_label(xyxy, label, color=(0, 0, 255))
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# 在帧上绘制多边形
for poly in pts:
if len(poly) > 1:
cv2.polylines(im0, [np.array(poly)], isClosed=False, color=(0, 255, 0), thickness=2)
# Stream results
im0 = annotator.result()
if view_img:
# if p not in windows:
# windows.append(p)
# # cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
# cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
# im0 = imutils.resize(im0, width=720)
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
# Print time (inference-only)
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
# Print results
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
if update:
strip_optimizer(weights) # update model (to fix SourceChangeWarning)
# def is_in_poly(p, poly):
# """
# :param p: [x, y]
# :param poly: [[], [], [], [], ...]
# :return:
# """
# px, py = p
# is_in = False
# for i, corner in enumerate(poly):
# next_i = i + 1 if i + 1 < len(poly) else 0
# x1, y1 = corner
# x2, y2 = poly[next_i]
# if (x1 == px and y1 == py) or (x2 == px and y2 == py): # if point is on vertex 如果点在顶点上
# is_in = True
# break
# if min(y1, y2) < py <= max(y1, y2): # find horizontal edges of polygon 找到多边形的水平边缘
# x = x1 + (py - y1) * (x2 - x1) / (y2 - y1)
# if x == px: # if point is on edge 如果点在边缘上
# is_in = True
# break
# elif x > px: # if point is on left-side of line 如果点在线的左侧
# is_in = not is_in
# return is_in
def is_in_poly(p, polys):
"""
:param p: [x, y]
:param polys: [[[x1, y1], [x2, y2], ...], [[x1, y1], [x2, y2], ...]]
:return:
"""
px, py = p
is_in = False
for poly in polys:
for i, corner in enumerate(poly):
next_i = i + 1 if i + 1 < len(poly) else 0
x1, y1 = corner
x2, y2 = poly[next_i]
if (x1 == px and y1 == py) or (x2 == px and y2 == py): # if point is on vertex
is_in = True
break
if min(y1, y2) < py <= max(y1, y2): # find horizontal edges of polygon
x = x1 + (py - y1) * (x2 - x1) / (y2 - y1)
if x == px: # if point is on edge
is_in = True
break
elif x > px: # if point is on left-side of line
is_in = not is_in
return is_in
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'weights/yolov5s.pt', help='model path(s)')
parser.add_argument('--source', type=str, default=ROOT / r'D:\my_job\DATA\data/test.mp4', help='file/dir/URL/glob, 0 for webcam')
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', default=True,action='store_true', help='show 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('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
parser.add_argument('--classes', default='0',nargs='+', type=int, help='filter by class: --classes 0, or --classes 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('--visualize', action='store_true', help='visualize features')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default=ROOT / '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', default=True,action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
opt = parser.parse_args()
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
print_args(vars(opt))
return opt
def main(opt):
check_requirements(exclude=('tensorboard', 'thop'))
run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)