You can try example.py by cloning this repo for a quick start.

git clone https://github.com/rentainhe/visualization.git python example.py

download the example.jpg to any folder you like

$ wget https://github.com/rentainhe/visualization/blob/master/visualize/test_data/example.jpg

import numpy as np from visualize import visualizeregionattention

img_path="path/to/example.jpg" save_path="example" attention_retio=1.0 boxes = np.array([[14, 25, 100, 200], [56, 75, 245, 300]], dtype='int') boxes_attention = [0.36, 0.64] visualizeregionattention(img_path,                            savepath=savepath,                             boxes=boxes,                             boxattentions=boxesattention,                             attentionratio=attentionretio,                            save_image=True,                            saveoriginimage=True,                            quality=100)

• img_path: where to load the original image
• boxes: a list of coordinates for the bounding boxes
• box_attentions: a list of attention scores for each bounding box
• attentionratio: a special param, if you set the attentionratio larger, it will make the attention map look more shallow. Just try!
• save_image=True: save the image with attention map or not, e.g., default: True.
• saveoriginalimage=True: save the original image at the same time, e.g., default: True

download the example.jpg to any folder you like

$ wget https://github.com/rentainhe/visualization/blob/master/visualize/test_data/example.jpg

build the following python script for a quick start

from visualize import visualizegridattention_v2 import numpy as np

img_path="./example.jpg" save_path="test" attention_mask = np.random.randn(14, 14) visualizegridattentionv2(imgpath,                            savepath=savepath,                            attentionmask=attentionmask,                            save_image=True,                            saveoriginalimage=True,                            quality=100)

• img_path: where the image you want to put an attention mask on.
• save_path: where to save the image.
• attention_mask: the attention mask with format numpy.ndarray, its shape is (H, W)
• save_image=True: save the image with attention map or not, e.g., default: True.
• saveoriginalimage=True: save the original image at the same time, e.g., default: True

Note that you can check Grid Attention Visualization here for more details

build the following python script for a quick start

from visualize import drawlinechart

test data
data1 = {"data": [13.15, 14.64, 15.83, 17.99], "name": "data 1"}
data2 = {"data": [14.16, 14.81, 16.11, 18.62], "name": "data 2"}
data_list = []
data_list.append(data1["data"])
data_list.append(data2["data"])
name_list = []
name_list.append(data1["name"])
name_list.append(data2["name"])
drawlinechart(datalist=datalist,
                labels=name_list,
                xlabel="test_x",
                ylabel="test_y",
                save_path="./test.jpg",
                legend={"loc": "upper left", "frameon": True, "fontsize": 12},
                title="example")

• data_list: a list of data to draw.
• labels: the label corresponds to each data in data_list.
• xlabel: label of x-axis.
• ylabel: label of y-axis.
• save_path: the path to save image.
• legend: the params of legend.
• title: the title of the saved image.