PyQt怎样可视化network网络图，并通过鼠标事件获取节点信息，用plotly dash会更方便一些吗
我有一个较大的network graph, 大概五六七和节点，数千条边，并且所有的节点和边都有很多attributes,诸如姓名、颜色、地址等等，这些节点和边以及各自的attributes保存在各个csv文件中，后来我使用networkx将这个网络图的所有的信息写入了一个gexf文件。
现在想要用PyQt5或者6构建一个GUI，并且将这个网络图可视化，根据不同节点的权重以及不同的属性设置这些节点的颜色及大小。同时可以通过鼠标滑轮来对图片进行放大缩小，并且不影响每个节点的清晰度（因为这个图真的很大）
示例：
原大小 ：
通过鼠标放大后：
然后我希望当我在这个界面搜索某个节点名称时，或者当我将鼠标悬浮于某个节点上方时，可以将以该节点为中心的部分网络（大概50-100个节点）放大，并且可以在另一个窗口出现该节点的所有attributes以及相邻节点的列表。
示例是我的一个学长用java做的另一个项目，但是那个java真的太复杂了，而且我也不太懂java，完全看不明白，更没办法从中得出思路再用PyQt做出来。希望有朋友可以给我提供一些思路和具体的方法。我最近刚刚开始学PyQt6,目前掌握的东西还是很基础的，希望朋友们也可以大概告诉我一下，想要做到以上的要求，学起来会很耗时吗？
另外今天看到关于plotly dash的一些资料，请问我这个需求用dash会更好做一些吗？因为好像我需要不少交互、互动之类的。
可以上b站看看关于这方面的视频,或者直接上GitHub上面找对应项目学习
lb1.resize(300,300) # 重设Label大小
lb1.setScaledContents(True) # 设置图片自适应窗口大小
lb1.resize(300,300) # 重设Label大小。lb1.setScaledContents(True) # 设置图片自适应窗口大小
lb1.resize(300,300) lb1.setScaledContents(True) ；
coding:utf-8
'''
V3.0A版本，尝试实现摄像头识别
'''
import numpy as np
import cv2
import os
import os.path
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import pylab
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
class UiForm():
openfile_name_pb = ''
openfile_name_pbtxt = ''
openpic_name = ''
num_class = 0
def setupUi(self, Form):
Form.setObjectName("Form")
Form.resize(600, 690)
Form.setMinimumSize(QtCore.QSize(600, 690))
Form.setMaximumSize(QtCore.QSize(600, 690))
self.frame = QtWidgets.QFrame(Form)
self.frame.setGeometry(QtCore.QRect(20, 20, 550, 100))
self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame.setObjectName("frame")
self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.frame)
self.horizontalLayout_2.setObjectName("horizontalLayout_2")
# 加载模型文件按钮
self.btn_add_file = QtWidgets.QPushButton(self.frame)
self.btn_add_file.setObjectName("btn_add_file")
self.horizontalLayout_2.addWidget(self.btn_add_file)
# 加载pbtxt文件按钮
self.btn_add_pbtxt = QtWidgets.QPushButton(self.frame)
self.btn_add_pbtxt.setObjectName("btn_add_pbtxt")
self.horizontalLayout_2.addWidget(self.btn_add_pbtxt)
# 输入检测类别数目按钮
self.btn_enter = QtWidgets.QPushButton(self.frame)
self.btn_enter.setObjectName("btn_enter")
self.horizontalLayout_2.addWidget(self.btn_enter)
# 打开摄像头
self.btn_opencam = QtWidgets.QPushButton(self.frame)
self.btn_opencam.setObjectName("btn_objdec")
self.horizontalLayout_2.addWidget(self.btn_opencam)
# 开始识别按钮
self.btn_objdec = QtWidgets.QPushButton(self.frame)
self.btn_objdec.setObjectName("btn_objdec")
self.horizontalLayout_2.addWidget(self.btn_objdec)
# 退出按钮
self.btn_exit = QtWidgets.QPushButton(self.frame)
self.btn_exit.setObjectName("btn_exit")
self.horizontalLayout_2.addWidget(self.btn_exit)
# 显示识别后的画面
self.lab_rawimg_show = QtWidgets.QLabel(Form)
self.lab_rawimg_show.setGeometry(QtCore.QRect(50, 140, 500, 500))
self.lab_rawimg_show.setMinimumSize(QtCore.QSize(500, 500))
self.lab_rawimg_show.setMaximumSize(QtCore.QSize(500, 500))
self.lab_rawimg_show.setObjectName("lab_rawimg_show")
self.lab_rawimg_show.setStyleSheet(("border:2px solid red"))
self.retranslateUi(Form)
# 这里将按钮和定义的动作相连，通过click信号连接openfile槽？
self.btn_add_file.clicked.connect(self.openpb)
# 用于打开pbtxt文件
self.btn_add_pbtxt.clicked.connect(self.openpbtxt)
# 用于用户输入类别数
self.btn_enter.clicked.connect(self.enter_num_cls)
# 打开摄像头
self.btn_opencam.clicked.connect(self.opencam)
# 开始识别
# ~ self.btn_objdec.clicked.connect(self.object_detection)
# 这里是将btn_exit按钮和Form窗口相连，点击按钮发送关闭窗口命令
self.btn_exit.clicked.connect(Form.close)
QtCore.QMetaObject.connectSlotsByName(Form)
def retranslateUi(self, Form):
_translate = QtCore.QCoreApplication.translate
Form.setWindowTitle(_translate("Form", "目标检测"))
self.btn_add_file.setText(_translate("Form", "加载模型文件"))
self.btn_add_pbtxt.setText(_translate("Form", "加载pbtxt文件"))
self.btn_enter.setText(_translate("From", "指定识别类别数"))
self.btn_opencam.setText(_translate("Form", "打开摄像头"))
self.btn_objdec.setText(_translate("From", "开始识别"))
self.btn_exit.setText(_translate("Form", "退出"))
self.lab_rawimg_show.setText(_translate("Form", "识别效果"))
def openpb(self):
global openfile_name_pb
openfile_name_pb, _ = QFileDialog.getOpenFileName(self.btn_add_file,'选择pb文件','/home/kanghao/','pb_files(*.pb)')
print('加载模型文件地址为：' + str(openfile_name_pb))
def openpbtxt(self):
global openfile_name_pbtxt
openfile_name_pbtxt, _ = QFileDialog.getOpenFileName(self.btn_add_pbtxt,'选择pbtxt文件','/home/kanghao/','pbtxt_files(*.pbtxt)')
print('加载标签文件地址为：' + str(openfile_name_pbtxt))
def opencam(self):
self.camcapture = cv2.VideoCapture(0)
self.timer = QtCore.QTimer()
self.timer.start()
self.timer.setInterval(100) # 0.1s刷新一次
self.timer.timeout.connect(self.camshow)
def camshow(self):
global camimg
_ , camimg = self.camcapture.read()
print(_)
camimg = cv2.resize(camimg, (512, 512))
camimg = cv2.cvtColor(camimg, cv2.COLOR_BGR2RGB)
print(type(camimg))
#strcamimg = camimg.tostring()
showImage = QtGui.QImage(camimg.data, camimg.shape[1], camimg.shape[0], QtGui.QImage.Format_RGB888)
self.lab_rawimg_show.setPixmap(QtGui.QPixmap.fromImage(showImage))
def enter_num_cls(self):
global num_class
num_class, okPressed = QInputDialog.getInt(self.btn_enter,'指定训练类别数','你的目标有多少类？',1,1,28,1)
if okPressed:
print('识别目标总类为：' + str(num_class))
def img2pixmap(self, image):
Y, X = image.shape[:2]
self._bgra = np.zeros((Y, X, 4), dtype=np.uint8, order='C')
self._bgra[..., 0] = image[..., 2]
self._bgra[..., 1] = image[..., 1]
self._bgra[..., 2] = image[..., 0]
qimage = QtGui.QImage(self._bgra.data, X, Y, QtGui.QImage.Format_RGB32)
pixmap = QtGui.QPixmap.fromImage(qimage)
return pixmap
def object_detection(self):
sys.path.append("..")
from object_detection.utils import ops as utils_ops
if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')
from utils import label_map_util
from utils import visualization_utils as vis_util
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = openfile_name_pb
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = openfile_name_pbtxt
NUM_CLASSES = num_class
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
TEST_IMAGE_PATHS = camimg
print(TEST_IMAGE_PATHS)
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
def run_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes', 'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
tensor_name)
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes, image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict[
'detection_classes'][0].astype(np.uint8)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
if 'detection_masks' in output_dict:
output_dict['detection_masks'] = output_dict['detection_masks'][0]
return output_dict
#image = Image.open(TEST_IMAGE_PATHS)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = load_image_into_numpy_array(TEST_IMAGE_PATHS)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
output_dict = run_inference_for_single_image(image_np, detection_graph)
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)
#plt.savefig(str(TEST_IMAGE_PATHS)+".jpg")
用于显示ui界面的命令
if name == "main":
app = QtWidgets.QApplication(sys.argv)
Window = QtWidgets.QWidget()
# ui为根据类Ui_From()创建的实例
ui = UiForm()
ui.setupUi(Window)
Window.show()
sys.exit(app.exec_())