importmatplotlib.pyplotasplt# 创建字典browser_data={'Chrome':{'ratio':45,'color':'blue'},'Firefox':{'ratio':20,'color':'green'},'Edge':{'ratio':15,'color':'orange'},'Safari':{'ratio':10,'color':'red'},'Others':{'
for i, community in enumerate(communities): color = colors[i % len(colors)] nx.draw_networkx_nodes(community, node_color=color) nx.draw_networkx_edges(G) plt.show() Les Miserables Co-Appearance Network import networkx as nx import matplotlib.pyplot as plt # 加载Les Miserables Co-Appearance ...
40 + import matplotlib.pyplot as plt 41 + 42 + plt.style.use('fivethirtyeight') 43 + 44 + train_data = pd.read_csv("./cn_data/train.tsv", sep="\t") 45 + valid_data = pd.read_csv("./cn_data/dev.tsv", sep="\t") 46 + 47 + sns.countplot(x="label", data=...
1importmatplotlib.pyplot as plt2importmatplotlib.image as mpimg3importcv2#bringing in OpenCV libraries45#read in the image and convert to grayscale6image = mpimg.imread('E:/spyder/a/a/exit-ramp.jpg')7gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)#grayscale conversion8#define a kernel size ...
As you would expect, by extracting the tags, we lose certain information like color, pose, relationships between objects in the scene, and so on. Additionally, a major disadvantage of this approach is that it requires enormous volumes of labeled data to train the classifier for extracting these...
pip install tf-nightly import tensorflow as tf import os import numpy as np import matplotlib.pyplot as plt # Load MNIST dataset mnist = tf.keras.datasets.mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() # Normalize the input image so that each pixel ...
Python Pillow Edge Detection - Learn how to perform edge detection using Python Pillow. Discover techniques and code examples for efficient image processing.
importmatplotlib.pyplotasplt%matplotlib inline plt.rcParams.update({'figure.figsize':(9,9),'axes.spines.right':False,'axes.spines.left':False,'axes.spines.top':False,'axes.spines.bottom':False})importnetworkxasnximportpandasaspdimportnumpyasnp ...
# importing the moduleimportcv2# read the image and store the data in a variableimage=cv2.imread("/home/abhinav/PycharmProjects/untitled1/b.jpg")# make it grayscaleGray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)# Make canny Functioncanny=cv2.Canny(Gray,40,140)# the threshold is varies bw ...
from matplotlib import font_manager from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads, emojis, is_colab, is_docker, is_jupyter) is_colab, is_docker, is_jupyter, is_online) def is_ascii(s) -> bool: Expand Down Expand Up @...