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demo.py
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demo.py
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# -*- coding: utf-8 -*-
'''
@Time : 20/04/25 15:49
@Author : huguanghao
@File : demo.py
@Noice :
@Modificattion :
@Author :
@Time :
@Detail :
'''
# import sys
# import time
# from PIL import Image, ImageDraw
# from models.tiny_yolo import TinyYoloNet
from tool.utils import *
from tool.torch_utils import *
from tool.darknet2pytorch import Darknet
import torch
import argparse
"""hyper parameters"""
use_cuda = True
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()
num_classes = m.num_classes
if num_classes == 20:
namesfile = 'data/voc.names'
elif num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/x.names'
class_names = load_class_names(namesfile)
img = cv2.imread(imgfile)
sized = cv2.resize(img, (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()
if i == 1:
print('%s: Predicted in %f seconds.' % (imgfile, (finish - start)))
plot_boxes_cv2(img, boxes[0], savename='predictions.jpg', class_names=class_names)
def detect_cv2_camera(cfgfile, weightfile):
import cv2
m = Darknet(cfgfile)
m.print_network()
if args.torch:
m.load_state_dict(torch.load(weightfile))
else:
m.load_weights(weightfile)
print('Loading weights from %s... Done!' % (weightfile))
if use_cuda:
m.cuda()
cap = cv2.VideoCapture(0)
# cap = cv2.VideoCapture("./test.mp4")
cap.set(3, 1280)
cap.set(4, 720)
print("Starting the YOLO loop...")
num_classes = m.num_classes
if num_classes == 20:
namesfile = 'data/voc.names'
elif num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/x.names'
class_names = load_class_names(namesfile)
while True:
ret, img = cap.read()
sized = cv2.resize(img, (m.width, m.height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
start = time.time()
boxes = do_detect(m, sized, 0.4, 0.6, use_cuda)
finish = time.time()
print('Predicted in %f seconds.' % (finish - start))
result_img = plot_boxes_cv2(img, boxes[0], savename=None, class_names=class_names)
cv2.imshow('Yolo demo', result_img)
cv2.waitKey(1)
cap.release()
def detect_skimage(cfgfile, weightfile, imgfile):
from skimage import io
from skimage.transform import resize
m = Darknet(cfgfile)
m.print_network()
m.load_weights(weightfile)
print('Loading weights from %s... Done!' % (weightfile))
if use_cuda:
m.cuda()
num_classes = m.num_classes
if num_classes == 20:
namesfile = 'data/voc.names'
elif num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/x.names'
class_names = load_class_names(namesfile)
img = io.imread(imgfile)
sized = resize(img, (m.width, m.height)) * 255
for i in range(2):
start = time.time()
boxes = do_detect(m, sized, 0.4, 0.4, use_cuda)
finish = time.time()
if i == 1:
print('%s: Predicted in %f seconds.' % (imgfile, (finish - start)))
plot_boxes_cv2(img, boxes, savename='predictions.jpg', class_names=class_names)
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='path of cfg file', dest='cfgfile')
parser.add_argument('-weightfile', type=str,
default='./checkpoints/Yolov4_epoch1.pth',
help='path of trained model.', dest='weightfile')
parser.add_argument('-imgfile', type=str,
default='./data/mscoco2017/train2017/190109_180343_00154162.jpg',
help='path of your image file.', dest='imgfile')
parser.add_argument('-torch', type=bool, default=false,
help='use torch weights')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = get_args()
if args.imgfile:
detect_cv2(args.cfgfile, args.weightfile, args.imgfile)
# detect_imges(args.cfgfile, args.weightfile)
# detect_cv2(args.cfgfile, args.weightfile, args.imgfile)
# detect_skimage(args.cfgfile, args.weightfile, args.imgfile)
else:
detect_cv2_camera(args.cfgfile, args.weightfile)