?? 之前我已经写过了以篇博客，写了一个很简易的GUI界面，在经过了几天对PyQt的学习之后，我对之前的页面进行了优化和美化，同时新增输出识别物体名称及相应数量的功能。
? 本机配置：
pyCharm：PyCharm 2022.1.3 (Community Edition)
python 3.6.9
PyQt: Qt5 Version Number is: 5.9.5
PyQt5 Version is: 5.10.1
Sip Version is: 4.19.7
本例适用于PyQt5查看PyQt版本，新建check.py，复制以下内容：
from PyQt5.QtWidgets import QApplication
from PyQt5.QtCore import QT_VERSION_STR
from PyQt5.Qt import PYQT_VERSION_STR
from sip import SIP_VERSION_STR
if __name__=='__main__':
import sys
app=QApplication(sys.argv)
print("Qt5 Version Number is: {0}".format(QT_VERSION_STR))
print("PyQt5 Version is: {}".format(PYQT_VERSION_STR))
print("Sip Version is: {}".format(SIP_VERSION_STR))
sys.exit(app.exec_())运行：
终端运行
python3 check.py
或
pyCharm 运行本文件打开Qt-designer,添加控件如图，本例名称为yolov3Gui.ui，保存在在darknet文件夹下：
修改控件名称如下：（这里status_text打错了，后边也就将错就错了）
将ui文件转化为py文件
终端输入：
pyuic5 -o yolov3Gui.py yolov3Gui. ui
或使用PyUIC转化转化的py文件如下：（修改：将startbtn和closebtn宽度均修改为71以完成后续将其变为圆形的操作）
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'yolov3Gui3.ui'
#
# Created by: PyQt5 UI code generator 5.9.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName("Form")
Form.resize(1317, 907)
self.output_text = QtWidgets.QTextEdit(Form)
self.output_text.setGeometry(QtCore.QRect(840, 90, 441, 591))
self.output_text.setObjectName("output_text")
self.status_tetx = QtWidgets.QTextEdit(Form)
self.status_tetx.setGeometry(QtCore.QRect(870, 550, 381, 111))
self.status_tetx.setObjectName("status_tetx")
self.startbtn = QtWidgets.QPushButton(Form)
self.startbtn.setGeometry(QtCore.QRect(841, 760, 71, 71))
self.startbtn.setObjectName("startbtn")
self.label = QtWidgets.QLabel(Form)
self.label.setGeometry(QtCore.QRect(850, 50, 111, 16))
self.label.setObjectName("label")
self.image_show_label = QtWidgets.QLabel(Form)
self.image_show_label.setGeometry(QtCore.QRect(40, 90, 751, 771))
self.image_show_label.setObjectName("image_show_label")
self.closebtn = QtWidgets.QPushButton(Form)
self.closebtn.setGeometry(QtCore.QRect(1044, 760, 71, 71))
self.closebtn.setObjectName("closebtn")
self.retranslateUi(Form)
self.closebtn.clicked.connect(Form.close)
QtCore.QMetaObject.connectSlotsByName(Form)
def retranslateUi(self, Form):
_translate = QtCore.QCoreApplication.translate
Form.setWindowTitle(_translate("Form", "Form"))
self.status_tetx.setHtml(_translate("Form", "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n"
"<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n"
"p, li { white-space: pre-wrap; }\n"
"</style></head><body style=\" font-family:\'Ubuntu\'; font-size:11pt; font-weight:400; font-style:normal;\">\n"
"<p style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">识别程序状态输出</p></body></html>"))
self.startbtn.setText(_translate("Form", "开始"))
self.label.setText(_translate("Form", "识别结果输出区"))
self.image_show_label.setText(_translate("Form", " 图像显示"))
self.closebtn.setText(_translate("Form", "退出"))在darknet文件夹下新建detect.py文件，复制以下内容：
import argparse
import os
import glob
import random
import darknet
import time
import cv2
import numpy as np
import darknet
'''def parser():
parser = argparse.ArgumentParser(description="YOLO Object Detection")
parser.add_argument("--input", type=str, default="",
help="image source. It can be a single image, a"
"txt with paths to them, or a folder. Image valid"
" formats are jpg, jpeg or png."
"If no input is given, ")
parser.add_argument("--batch_size", default=1, type=int,
help="number of images to be processed at the same time")
parser.add_argument("--weights", default="yolov4.weights",
help="yolo weights path")
parser.add_argument("--dont_show", action='store_true',
help="windown inference display. For headless systems")
parser.add_argument("--ext_output", action='store_true',
help="display bbox coordinates of detected objects")
parser.add_argument("--save_labels", action='store_true',
help="save detections bbox for each image in yolo format")
parser.add_argument("--config_file", default="./cfg/yolov4.cfg",
help="path to config file")
parser.add_argument("--data_file", default="./cfg/coco.data",
help="path to data file")
parser.add_argument("--thresh", type=float, default=.25,
help="remove detections with lower confidence")
return parser.parse_args()'''
def parser():
parser = argparse.ArgumentParser(description="YOLO Object Detection")
parser.add_argument("--input", type=str, default="",
help="image source. It can be a single image, a"
"txt with paths to them, or a folder. Image valid"
" formats are jpg, jpeg or png."
"If no input is given, ")
parser.add_argument("--batch_size", default=1, type=int,
help="number of images to be processed at the same time")
parser.add_argument("--weights", default="myData/backup/my_yolov3_last.weights",#修改为自己的路径
help="yolo weights path")
parser.add_argument("--dont_show", action='store_true',
help="windown inference display. For headless systems")
parser.add_argument("--ext_output", action='store_true',
help="display bbox coordinates of detected objects")
parser.add_argument("--save_labels", action='store_true',
help="save detections bbox for each image in yolo format")
parser.add_argument("--config_file", default="./cfg/my_yolov3.cfg",
help="path to config file")
parser.add_argument("--data_file", default="./cfg/my_data.data",
help="path to data file")
parser.add_argument("--thresh", type=float, default=.25,
help="remove detections with lower confidence")
return parser.parse_args()
def check_arguments_errors(args):
assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
if not os.path.exists(args.config_file):
raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file))))
if not os.path.exists(args.weights):
raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights))))
if not os.path.exists(args.data_file):
raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file))))
if args.input and not os.path.exists(args.input):
raise(ValueError("Invalid image path {}".format(os.path.abspath(args.input))))
def check_batch_shape(images, batch_size):
"""
Image sizes should be the same width and height
"""
shapes = [image.shape for image in images]
if len(set(shapes)) > 1:
raise ValueError("Images don't have same shape")
if len(shapes) > batch_size:
raise ValueError("Batch size higher than number of images")
return shapes[0]
def load_images(images_path):
"""
If image path is given, return it directly
For txt file, read it and return each line as image path
In other case, it's a folder, return a list with names of each
jpg, jpeg and png file
"""
input_path_extension = images_path.split('.')[-1]
if input_path_extension in ['jpg', 'jpeg', 'png']:
return [images_path]
elif input_path_extension == "txt":
with open(images_path, "r") as f:
return f.read().splitlines()
else:
return glob.glob(
os.path.join(images_path, "*.jpg")) + \
glob.glob(os.path.join(images_path, "*.png")) + \
glob.glob(os.path.join(images_path, "*.jpeg"))
def prepare_batch(images, network, channels=3):
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_images = []
for image in images:
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
custom_image = image_resized.transpose(2, 0, 1)
darknet_images.append(custom_image)
batch_array = np.concatenate(darknet_images, axis=0)
batch_array = np.ascontiguousarray(batch_array.flat, dtype=np.float32)/255.0
darknet_images = batch_array.ctypes.data_as(darknet.POINTER(darknet.c_float))
return darknet.IMAGE(width, height, channels, darknet_images)
def image_detection(image_path,network, class_names, class_colors, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)
image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)
darknet.free_image(darknet_image)
image = darknet.draw_boxes(detections, image_resized, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections
#return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
def batch_detection(network, images, class_names, class_colors,
thresh=0.25, hier_thresh=.5, nms=.45, batch_size=4):
image_height, image_width, _ = check_batch_shape(images, batch_size)
darknet_images = prepare_batch(images, network)
batch_detections = darknet.network_predict_batch(network, darknet_images, batch_size, image_width,
image_height, thresh, hier_thresh, None, 0, 0)
batch_predictions = []
for idx in range(batch_size):
num = batch_detections[idx].num
detections = batch_detections[idx].dets
if nms:
darknet.do_nms_obj(detections, num, len(class_names), nms)
predictions = darknet.remove_negatives(detections, class_names, num)
images[idx] = darknet.draw_boxes(predictions, images[idx], class_colors)
batch_predictions.append(predictions)
darknet.free_batch_detections(batch_detections, batch_size)
return images, batch_predictions
def image_classification(image, network, class_names):
width = darknet.network_width(network)
height = darknet.network_height(network)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet_image = darknet.make_image(width, height, 3)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.predict_image(network, darknet_image)
predictions = [(name, detections[idx]) for idx, name in enumerate(class_names)]
darknet.free_image(darknet_image)
return sorted(predictions, key=lambda x: -x[1])
def convert2relative(image, bbox):
"""
YOLO format use relative coordinates for annotation
"""
x, y, w, h = bbox
height, width, _ = image.shape
return x/width, y/height, w/width, h/height
def save_annotations(name, image, detections, class_names):
"""
Files saved with image_name.txt and relative coordinates
"""
file_name = name.split(".")[:-1][0] + ".txt"
with open(file_name, "w") as f:
for label, confidence, bbox in detections:
x, y, w, h = convert2relative(image, bbox)
label = class_names.index(label)
f.write("{} {:.4f} {:.4f} {:.4f} {:.4f}\n".format(label, x, y, w, h))
def batch_detection_example():
args = parser()
check_arguments_errors(args)
batch_size = 3
random.seed(3) # deterministic bbox colors
network, class_names, class_colors = darknet.load_network(
args.config_file,
args.data_file,
args.weights,
batch_size=batch_size
)
image_names = ['data/horses.jpg', 'data/horses.jpg', 'data/eagle.jpg']
images = [cv2.imread(image) for image in image_names]
images, detections, = batch_detection(network, images, class_names,
class_colors, batch_size=batch_size)
for name, image in zip(image_names, images):
cv2.imwrite(name.replace("data/", ""), image)
print(detections)
def get_files(dir, suffix):
res = []
for root, directory, files in os.walk(dir):
for filename in files:
name, suf = os.path.splitext(filename)
if suf == suffix:
#res.append(filename)
res.append(os.path.join(root, filename))
return res
def bbox2points_zs(bbox):
"""
From bounding box yolo format
to corner points cv2 rectangle
"""
x, y, w, h = bbox
xmin = int(round(x - (w / 2)))
xmax = int(round(x + (w / 2)))
ymin = int(round(y - (h / 2)))
ymax = int(round(y + (h / 2)))
return xmin, ymin, xmax, ymax
def main():
args = parser()
check_arguments_errors(args)
input_dir = '/home/yourdarknet'
config_file = '/home/your/darknet/cfg/my_yolov3.cfg'#修改为自己的路径
data_file = '/home/your/darknet/cfg/my_data.data'
weights = '/home/your/darknet/myData/backup/my_yolov3_last.weights'
random.seed(3) # deterministic bbox colors
network, class_names, class_colors = darknet.load_network(
config_file,
data_file,
weights,
batch_size=args.batch_size
)
src_width = darknet.network_width(network)
src_height = darknet.network_height(network)
#生成保存图片路径文件夹
save_dir = os.path.join(input_dir, 'object_result')
# 去除首位空格
save_dir=save_dir.strip()
# 去除尾部 \ 符号
save_dir=save_dir.rstrip("\\")
# 判断路径是否存在 # 存在 True # 不存在 False
isExists=os.path.exists(save_dir)
# 判断结果
if not isExists:
# 如果不存在则创建目录 # 创建目录操作函数
os.makedirs(save_dir)
print(save_dir+' 创建成功')
else:
# 如果目录存在 则不创建，并提示目录已存在
print(save_dir + ' 目录已存在')
image_list = get_files(input_dir, '.jpg')
total_len = len(image_list)
index = 0
#while True:
for i in range(0, total_len):
image_name = image_list[i]
src_image = cv2.imread(image_name)
cv2.imshow('src_image', src_image)
cv2.waitKey(1)
prev_time = time.time()
image, detections = image_detection(
image_name, network, class_names, class_colors, args.thresh)
#'''
file_name, type_name = os.path.splitext(image_name)
#print(file_name)
#print(file_name.split(r'/'))
print(''.join(file_name.split(r'/')[-1]) + 'bbbbbbbbb')
cut_image_name_list = file_name.split(r'/')[-1:] #cut_image_name_list is list
save_dir_image = os.path.join(save_dir ,cut_image_name_list[0])
if not os.path.exists(save_dir_image):
os.makedirs(save_dir_image)
cut_image_name = ''.join(cut_image_name_list) #list to str
object_count = 0
for label, confidence, bbox in detections:
cut_image_name_temp = cut_image_name + "_{}.jpg".format(object_count)
object_count += 1
xmin, ymin, xmax, ymax = bbox2points_zs(bbox)
print("aaaaaaaaa x,{} y,{} w,{} h{}".format(xmin, ymin, xmax, ymax))
xmin_coordinary = (int)(xmin * src_image.shape[1] / src_width-0.5)
ymin_coordinary = (int)(ymin * src_image.shape[0] / src_height-0.5)
xmax_coordinary = (int)(xmax * src_image.shape[1] / src_width+0.5)
ymax_coordinary = (int)(ymax * src_image.shape[0] / src_height+0.5)
if xmin_coordinary>src_image.shape[1]:
xmin_coordinary = src_image.shape[1]
if ymin_coordinary>src_image.shape[0]:
ymin_coordinary = src_image.shape[0]
if xmax_coordinary>src_image.shape[1]:
xmax_coordinary = src_image.shape[1]
if ymax_coordinary>src_image.shape[0]:
ymax_coordinary = src_image.shape[0]
if xmin_coordinary < 0:
xmin_coordinary = 0
if ymin_coordinary < 0:
ymin_coordinary = 0
if xmax_coordinary < 0:
xmax_coordinary = 0
if ymax_coordinary < 0:
ymax_coordinary = 0
print("qqqqqqqq x,{} y,{} w,{} h{}".format(xmin_coordinary, ymin_coordinary, xmax_coordinary, ymax_coordinary))
out_iou_img = np.full((ymax_coordinary - ymin_coordinary, xmax_coordinary - xmin_coordinary, src_image.shape[2]), 114, dtype=np.uint8)
out_iou_img[:,:] = src_image[ymin_coordinary:ymax_coordinary,xmin_coordinary:xmax_coordinary]
cv2.imwrite(os.path.join(save_dir_image,cut_image_name_temp),out_iou_img)
#'''
#if args.save_labels:
#if True:
#save_annotations(image_name, image, detections, class_names)
darknet.print_detections(detections, args.ext_output)
fps = int(1/(time.time() - prev_time))
print("FPS: {}".format(fps))
if not args.dont_show:
#cv2.imshow('Inference', image)
cv2.waitKey(1)
#if cv2.waitKey() & 0xFF == ord('q'):
#break
index += 1
if __name__ == "__main__":
# unconmment next line for an example of batch processing
# batch_detection_example()
main()
在darknet文件夹下新建Callyolov3.py文件，复制以下内容：
import sys
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from yolov3Gui3 import Ui_Form
import cv2
from PIL import Image
import random
import string
from detect import image_detection, parser, check_arguments_errors
import darknet
from collections import Counter
class yolov3Gui(QMainWindow, Ui_Form):
def __init__(self):
super(yolov3Gui, self).__init__()
self.openfile_name_image = ''
self.image = None
self.setupUi(self)
self.startbtn.clicked.connect(self.select_image)
self.startbtn.clicked.connect(self.detect)
self.startbtn.clicked.connect(self.status)
def status(self):
# if self.startbtn.isChecked():
self.status_tetx.clear()
self.status_tetx.setText("start to output")
def detect(self):
self.image_show_label.clear()
if self.image is None:
print('没有选择图片')
elif self.image is not None:
# 检测图片
predictions = run_detect(self.openfile_name_image)
# 读取检测之后的图片
img = cv2.imread('img/result/' + self.openfile_name_image.split('/')[-1])
# img = cv2.resize(img, (400, 300), interpolation=cv2.INTER_AREA)
img = cv2.resize(img, (751, 771), interpolation=cv2.INTER_AREA)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# cv2.imshow('test', img)
# cv2.waitKey(20)
# 将图片放在标签self.img_show_label中
a = QImage(img.data, img.shape[1], img.shape[0], img.shape[1] * 3, QImage.Format_RGB888)
self.image_show_label.setPixmap(QPixmap.fromImage(a))
self.output_text.clear()
'''for label, confidence, bbox in preditions:
self.output_text.setText(label + ' numbers: ' + confidence )
print(preditions)
print(label)'''
i = len(predictions)
labels = []
for n in range(i):
labels.append(predictions[n][0])
result = Counter(labels)
self.output_text.setText(str(result))
# print(result)
# print(labels)
# self.output_text.setText(predictions[n][o] + "\n")
# print(predictions[n][0])
pass
def select_image(self):
# temp为选择文件的路径 这里打开的是这个main.py函数的同级目录下的img文件夹
temp, _ = QFileDialog.getOpenFileName(self, "选择照片文件", r"./img/")
if temp is not None:
self.openfile_name_image = temp
# 读取选择的图片
self.image = cv2.imread(self.openfile_name_image)
# print(self.openfile_name_image)
def run_detect(path):
try:
image = Image.open(path)
except:
print('Open Error! Try again!')
else:
# 这里是我模型检测函数，替换成自己的即可，这个函数返回的就是检测好的图片，然后保存在本地的同级目录下的img/result
'''args = darknet_images.parser()
darknet_images.check_arguments_errors(args)'''
# args = detect.parser()
args = parser()
# detect.check_arguments_errors(args)
check_arguments_errors(args)
random.seed(3) # deterministic bbox colors
network, class_names, class_colors = darknet.load_network(
args.config_file,
args.data_file,
args.weights,
batch_size=args.batch_size
)
img_path = path
# r_image = darknet_images.image_detection(img_path,network, class_names, class_colors, args.thresh)
# img = cv2.imread(img_path)
# r_image = detect.image_detection(img_path,network, class_names, class_colors, args.thresh)
r_image, predictions = image_detection(img_path, network, class_names, class_colors, args.thresh)
# r_image.save('img/result/' + path.split('/')[-1])
cv2.imwrite('img/result/' + path.split('/')[-1], r_image)
return predictions
if __name__ == '__main__':
app = QApplication(sys.argv)
yG = yolov3Gui()
yG.setWindowTitle("yolov3Gui")
qssStyle = """
#startbtn{
background-color:orange;
border-radius:35px;
border:2px groover gray;
}
#closebtn{
background-color:orange;
border-radius:35px;
border:2px groover gray;
}
#output_text{
background-color:gray;
}
#image_show_label{
background-color:gray;
}
#status_tetx{
background-color:gray;
}
"""
yG.setStyleSheet(qssStyle)
yG.show()
sys.exit(app.exec_())
本例运行前需在检测目录，也就算darknet文件夹中新新建img文件夹，在img文件夹下新建result文件夹,之后返回darknet文件夹运行本例，即终端输入：
mkdir img
cd img
mkdir result
cd ..
python3 Callyolov3.py
运行结果如图：
本例在博主的上一篇关于yolov3的GUI界面编程的基础上加以美化，并新增了输出物品的label和number的功能，接下来准备做视频流和摄像头检测的GUI界面，如果各位有好的idea或博主有错误的地方也欢迎大家在评论区留言，大家互相帮助，共同进步！
2022.7.15更新：输出文本优化形式，将输出的output_text中的counters删除，更改格式为：
"label: " + label + " number: " + number,以下是更改后的yolov3Gui程序，大体并未改变，只对其中detect函数进行部分修改：
import sys
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from yolov3Gui3 import Ui_Form
import cv2
from PIL import Image
import random
import string
from detect import image_detection, parser, check_arguments_errors
import darknet
from collections import Counter
class yolov3Gui(QMainWindow, Ui_Form):
def __init__(self):
super(yolov3Gui, self).__init__()
self.openfile_name_image = ''
self.image = None
self.setupUi(self)
self.startbtn.clicked.connect(self.select_image)
self.startbtn.clicked.connect(self.detect)
self.startbtn.clicked.connect(self.status)
def status(self):
# if self.startbtn.isChecked():
self.status_tetx.clear()
self.status_tetx.setText("start to output")
def detect(self):
#self.image_show_label.clear()
if self.image is None:
print('没有选择图片')
elif self.image is not None:
# 检测图片
#self.image_show_label.clear()
predictions = run_detect(self.openfile_name_image)
# 读取检测之后的图片
img = cv2.imread('img/result/' + self.openfile_name_image.split('/')[-1])
# img = cv2.resize(img, (400, 300), interpolation=cv2.INTER_AREA)
img = cv2.resize(img, (751, 771), interpolation=cv2.INTER_AREA)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# cv2.imshow('test', img)
# cv2.waitKey(20)
# 将图片放在标签self.img_show_label中
a = QImage(img.data, img.shape[1], img.shape[0], img.shape[1] * 3, QImage.Format_RGB888)
self.image_show_label.setPixmap(QPixmap.fromImage(a))
self.output_text.clear()
'''for label, confidence, bbox in preditions:
self.output_text.setText(label + ' numbers: ' + confidence )
print(preditions)
print(label)'''
i = len(predictions)
labels = []
for n in range(i):
labels.append(predictions[n][0])
result = Counter(labels)
#listVal = list(result)
text = ''
for key,value in result.items():#修改部分
#print("label: " + key + " number: " + str(value) + '\n')
text = text + "label: " + key + " number: " + str(value) + '\n'
#print(text)
self.output_text.setText(text)
# print(result)
# print(labels)
# self.output_text.setText(predictions[n][o] + "\n")
# print(predictions[n][0])
pass
def select_image(self):
# temp为选择文件的路径 这里打开的是这个main.py函数的同级目录下的img文件夹
temp, _ = QFileDialog.getOpenFileName(self, "选择照片文件", r"./img/")
if temp is not None:
self.openfile_name_image = temp
# 读取选择的图片
self.image = cv2.imread(self.openfile_name_image)
# print(self.openfile_name_image)
'''a = QImage(self.image.data, self.image.shape[1], self.image.shape[0], self.image.shape[1] * 3, QImage.Format_RGB888)
self.image_show_label.setPixmap(QPixmap.fromImage(a))'''
def run_detect(path):
try:
image = Image.open(path)
except:
print('Open Error! Try again!')
else:
# 这里是我模型检测函数，替换成自己的即可，这个函数返回的就是检测好的图片，然后保存在本地的同级目录下的img/result
'''args = darknet_images.parser()
darknet_images.check_arguments_errors(args)'''
# args = detect.parser()
args = parser()
# detect.check_arguments_errors(args)
check_arguments_errors(args)
random.seed(3) # deterministic bbox colors
network, class_names, class_colors = darknet.load_network(
args.config_file,
args.data_file,
args.weights,
batch_size=args.batch_size
)
img_path = path
# r_image = darknet_images.image_detection(img_path,network, class_names, class_colors, args.thresh)
# img = cv2.imread(img_path)
# r_image = detect.image_detection(img_path,network, class_names, class_colors, args.thresh)
r_image, predictions = image_detection(img_path, network, class_names, class_colors, args.thresh)
# r_image.save('img/result/' + path.split('/')[-1])
cv2.imwrite('img/result/' + path.split('/')[-1], r_image)
return predictions
if __name__ == '__main__':
app = QApplication(sys.argv)
yG = yolov3Gui()
yG.setWindowTitle("yolov3Gui")
qssStyle = """
#startbtn{
background-color:orange;
border-radius:35px;
border:2px groover gray;
}
#closebtn{
background-color:orange;
border-radius:35px;
border:2px groover gray;
}
#output_text{
background-color:gray;
}
#image_show_label{
background-color:gray;
}
#status_tetx{
background-color:gray;
}
"""
yG.setStyleSheet(qssStyle)
yG.show()
sys.exit(app.exec_())
修改后效果如图：
接下来可能进行的修改：
1.解决out of memory问题
2.实现视频流和摄像头检测