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monitor.py
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import cv2
from tool.utils import *
from tool.torch_utils import *
from tool.darknet2pytorch import Darknet
import argparse
import datetime
"""hyper parameters"""
use_cuda = False
def init_camera():
try:
capture = cv2.VideoCapture(0)
while(True):
# 获取一帧
ret, frame = capture.read()
# 将这帧转换为灰度图
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame', frame)
# 如果输入q，则退出
if cv2.waitKey(1) == ord('q'):
break
except e:
print(e)
def cut_person(img, boxes, savename=None):
import cv2
img = np.copy(img)
width = img.shape[1]
height = img.shape[0]
for i in range(len(boxes)):
box = boxes[i]
x1 = int(box[0] * width)
y1 = int(box[1] * height)
x2 = int(box[2] * width)
y2 = int(box[3] * height)
print('========================================================',x1,x2,y1,y2)
target = img[y1:y2,x1:x2]
# img = cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 1)
if savename is None:
print("save plot results to %s" % str(datetime.datetime.now().microsecond)+'.jpg')
cv2.imwrite('./data/split/'+str(datetime.datetime.now().microsecond)+'.jpg', target)
def detect_cv2(cfgfile, weightfile, imgfile):
import cv2
m = Darknet(cfgfile)
m.print_network()
m.load_weights(weightfile)
# print('Loading weights from %s... Done!' % (weightfile))
if use_cuda:
m.cuda()
# print('-------------------------',m.num_classes)
num_classes = m.num_classes
if num_classes == 20:
namesfile = './data/voc.names'
elif num_classes == 80:
namesfile = './data/coco.names'
else:
namesfile = './data/coco.names'
class_names = load_class_names(namesfile)
sized = cv2.resize(imgfile, (m.width, m.height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
for i in range(2):
start = time.time()
boxes = do_detect(m, sized, 0.4, 0.6, use_cuda)
finish = time.time()
cut_person(imgfile, boxes[0])
return plot_boxes_cv2(imgfile, boxes[0], class_names=class_names),boxes[0]
def get_args():
parser = argparse.ArgumentParser('Test your image or video by trained model.')
parser.add_argument('-cfgfile', type=str, default= './cfg/yolov4.cfg',
help= './cfg/yolov4.cfg', dest='cfgfile')
parser.add_argument('-weightfile', type=str,
default= './weights\\yolov4.weights',
help='--todo--', dest='weightfile')
parser.add_argument('-imgfile', type=str,
default= './data\\WIN_20210525_09_49_39_Pro.mp4',
help= './data\\WIN_20210525_09_49_39_Pro.mp4', dest='imgfile')
args = parser.parse_args()
return args
def target_detect():
cap = cv2.VideoCapture('./data/WIN_20210525_09_49_39_Pro.mp4')
video_width = int(cap.get(3))
video_height = int(cap.get(4))
fps = int(cap.get(5))
codec = int(cap.get(cv2.CAP_PROP_FOURCC))
## ##
def decode_fourcc(cc):
return "".join([chr((int(cc) >> 8 * i) & 0xFF) for i in range(4)])
## ##
# print(video_width,video_height,fps,decode_fourcc(codec))
# videoWriter = cv2.VideoWriter('detected.mp4',cv2.VideoWriter_fourcc('a','v','c','1'),fps,(video_width,video_height))
# while 1:
ret,frame = cap.read()
# cv2.imshow('cap',frame)
print('==========================',type(frame))
### ###
args = get_args()
new_frame,boxes = detect_cv2(args.cfgfile, args.weightfile, frame)
# cv2.save('./data/')
# cv2.imshow('cap',new_frame)
# k = cv2.waitKey(fps)
# if k == ord('q' or 'Q'):
# break
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
target_detect()
# init_camera()