Images in Python

How to add images to charts as background images or logos.

New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Add a Background Image¶

In this page we explain how to add static, non-interactive images as background, logo or annotation images to a figure. For exploring image data in interactive charts, see the tutorial on displaying image data.

A background image can be added to the layout of a figure with fig.add_layout_image or by setting the images parameter of go.Layout. The source attribute of a go.layout.Image can be the URL of an image, or a PIL Image object (from PIL import Image; img = Image.open('filename.png')).

In [1]:

import plotly.graph_objects as go

# Create figure
fig = go.Figure()

# Add trace
fig.add_trace(
    go.Scatter(x=[0, 0.5, 1, 2, 2.2], y=[1.23, 2.5, 0.42, 3, 1])
)

# Add images
fig.add_layout_image(
        dict(
            source="https://images.plot.ly/language-icons/api-home/python-logo.png",
            xref="x",
            yref="y",
            x=0,
            y=3,
            sizex=2,
            sizey=2,
            sizing="stretch",
            opacity=0.5,
            layer="below")
)

# Set templates
fig.update_layout(template="plotly_white")

fig.show()

Add a Logo¶

See more examples of adding logos to charts!

In [2]:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=["-35.3", "-15.9", "-15.8", "-15.6", "-11.1",
           "-9.6", "-9.2", "-3.5", "-1.9", "-0.9",
           "1.0", "1.4", "1.7", "2.0", "2.8", "6.2",
           "8.1", "8.5", "8.5", "8.6", "11.4", "12.5",
           "13.3", "13.7", "14.4", "17.5", "17.7",
           "18.9", "25.1", "28.9", "41.4"],
        y=["Designers, musicians, artists, etc.",
           "Secretaries and administrative assistants",
           "Waiters and servers", "Archivists, curators, and librarians",
           "Sales and related", "Childcare workers, home car workers, etc.",
           "Food preparation occupations", "Janitors, maids, etc.",
           "Healthcare technicians, assistants. and aides",
           "Counselors, social and religious workers",
           "Physical, life and social scientists", "Construction",
           "Factory assembly workers", "Machinists, repairmen, etc.",
           "Media and communications workers", "Teachers",
           "Mechanics, repairmen, etc.", "Financial analysts and advisers",
           "Farming, fishing and forestry workers",
           "Truck drivers, heavy equipment operator, etc.", "Accountants and auditors",
           "Human resources, management analysts, etc.", "Managers",
           "Lawyers and judges", "Engineers, architects and surveyors",
           "Nurses", "Legal support workers",
           "Computer programmers and system admin.", "Police officers and firefighters",
           "Chief executives", "Doctors, dentists and surgeons"],
        marker=go.bar.Marker(
            color="rgb(253, 240, 54)",
            line=dict(color="rgb(0, 0, 0)",
                      width=2)
        ),
        orientation="h",
    )
)

# Add image
fig.add_layout_image(
    dict(
        source="https://raw.githubusercontent.com/cldougl/plot_images/add_r_img/vox.png",
        xref="paper", yref="paper",
        x=1, y=1.05,
        sizex=0.2, sizey=0.2,
        xanchor="right", yanchor="bottom"
    )
)

# update layout properties
fig.update_layout(
    autosize=False,
    height=800,
    width=700,
    bargap=0.15,
    bargroupgap=0.1,
    barmode="stack",
    hovermode="x",
    margin=dict(r=20, l=300, b=75, t=125),
    title=("Moving Up, Moving Down<br>" +
           "<i>Percentile change in income between childhood and adulthood</i>"),
)

fig.show()

Label Spectroscopy Data by Adding Multiple Images¶

In [3]:

import plotly.graph_objects as go

import numpy as np
np.random.seed(1)
from scipy.signal import savgol_filter

# Simulate spectroscopy data
def simulated_absorption(mu, sigma, intensity):
    data = [np.random.normal(mu[i], sigma[i], intensity[i]) for i in range(len(mu))]
    hists = [np.histogram(d, 1000, range=(200, 500), density=True) for d in data]
    ys = [y for y, x in hists]
    s = savgol_filter(np.max(ys, axis=0), 41, 3)
    return hists[0][1], s

mus = [[290, 240, 260], [330, 350]]
sigmas = [[4, 6, 10], [5, 4]]
intensities = [[100000, 300000, 700000], [40000, 20000]]
simulated_absorptions = [simulated_absorption(m, s, i) for m, s, i in
                         zip(mus, sigmas, intensities)]

# Create figure
fig = go.Figure()

# Create traces from data
names = ["Benzene", "Naphthalene"]
for (x, y), n in zip(simulated_absorptions, names):
    fig.add_trace(go.Scatter(x=x, y=y, name=n))

# Add images
fig.add_layout_image(
    dict(
        source="https://raw.githubusercontent.com/michaelbabyn/plot_data/master/benzene.png",
        x=0.75,
        y=0.65,
    ))
fig.add_layout_image(dict(
        source="https://raw.githubusercontent.com/michaelbabyn/plot_data/master/naphthalene.png",
        x=0.9,
        y=0.3,
        )
)
fig.update_layout_images(dict(
        xref="paper",
        yref="paper",
        sizex=0.3,
        sizey=0.3,
        xanchor="right",
        yanchor="bottom"
))

# Add annotations
fig.update_layout(
    annotations=[
        dict(
            x=93.0 / 300,
            y=0.07 / 0.1,
            xref="paper",
            yref="paper",
            showarrow=True,
            arrowhead=0,
            opacity=0.5,
            ax=250,
            ay=-40,
        ),
        dict(
            x=156.0 / 300,
            y=0.04 / 0.1,
            xref="paper",
            yref="paper",
            showarrow=True,
            arrowhead=0,
            opacity=0.5,
            ax=140,
            ay=-10,
        )
    ]
)

# Configure axes
fig.update_xaxes(title_text="Wavelength")
fig.update_yaxes(title_text="Absorption", hoverformat=".3f")

# Configure other layout properties
fig.update_layout(
    title_text="Absorption Frequencies of Benzene and Naphthalene",
    height=500,
    width=900,
    template="plotly_white"
)

fig.show()

Zoom on Static Images¶

In [4]:

import plotly.graph_objects as go

# Create figure
fig = go.Figure()

# Constants
img_width = 1600
img_height = 900
scale_factor = 0.5

# Add invisible scatter trace.
# This trace is added to help the autoresize logic work.
fig.add_trace(
    go.Scatter(
        x=[0, img_width * scale_factor],
        y=[0, img_height * scale_factor],
        mode="markers",
        marker_opacity=0
    )
)

# Configure axes
fig.update_xaxes(
    visible=False,
    range=[0, img_width * scale_factor]
)

fig.update_yaxes(
    visible=False,
    range=[0, img_height * scale_factor],
    # the scaleanchor attribute ensures that the aspect ratio stays constant
    scaleanchor="x"
)

# Add image
fig.add_layout_image(
    dict(
        x=0,
        sizex=img_width * scale_factor,
        y=img_height * scale_factor,
        sizey=img_height * scale_factor,
        xref="x",
        yref="y",
        opacity=1.0,
        layer="below",
        sizing="stretch",
        source="https://raw.githubusercontent.com/michaelbabyn/plot_data/master/bridge.jpg")
)

# Configure other layout
fig.update_layout(
    width=img_width * scale_factor,
    height=img_height * scale_factor,
    margin={"l": 0, "r": 0, "t": 0, "b": 0},
)

# Disable the autosize on double click because it adds unwanted margins around the image
# More detail: https://plotly.com/python/configuration-options/
fig.show(config={'doubleClick': 'reset'})

Annotating layout image with shapes¶

introduced in plotly 4.7

It can be useful to add shapes to a layout image, for highlighting an object, drawing bounding boxes as part of a machine learning training set, or identifying seeds for a segmentation algorithm.

In order to enable shape drawing, you need to

define a dragmode corresponding to a drawing tool ('drawline','drawopenpath', 'drawclosedpath', 'drawcircle', or 'drawrect')
add modebar buttons corresponding to the drawing tools you wish to use.

The style of new shapes is specified by the newshape layout attribute. Shapes can be selected and modified after they have been drawn. More details and examples are given in the tutorial on shapes.

Drawing or modifying a shape triggers a relayout event, which can be captured by a callback inside a Dash application.

In [5]:

import plotly.graph_objects as go
fig = go.Figure()
# Add image
img_width = 1600
img_height = 900
scale_factor = 0.5
fig.add_layout_image(
        x=0,
        sizex=img_width,
        y=0,
        sizey=img_height,
        xref="x",
        yref="y",
        opacity=1.0,
        layer="below",
        source="https://raw.githubusercontent.com/michaelbabyn/plot_data/master/bridge.jpg"
)
fig.update_xaxes(showgrid=False, range=(0, img_width))
fig.update_yaxes(showgrid=False, scaleanchor='x', range=(img_height, 0))
# Line shape added programatically
fig.add_shape(
    type='line', xref='x', yref='y',
    x0=650, x1=1080, y0=380, y1=180, line_color='cyan'
)
# Set dragmode and newshape properties; add modebar buttons
fig.update_layout(
    dragmode='drawrect',
    newshape=dict(line_color='cyan'),
    title_text='Drag to add annotations - use modebar to change drawing tool'
)
fig.show(config={'modeBarButtonsToAdd':['drawline',
                                        'drawopenpath',
                                        'drawclosedpath',
                                        'drawcircle',
                                        'drawrect',
                                        'eraseshape'
                                       ]})

Images Placed Relative to Axes¶

Using xref='x domain' or yref='y domain', images can be placed relative to axes. As an example, the following shows how to put an image in the top corner of a subplot (try panning and zooming the resulting figure):

In [6]:

import plotly.express as px

df = px.data.iris()
fig = px.scatter(df, x="sepal_length", y="sepal_width", facet_col="species")
# sources of images
sources = [
    "https://upload.wikimedia.org/wikipedia/commons/thumb/f/fe/Iris_setosa_var._setosa_%282595031014%29.jpg/360px-Iris_setosa_var._setosa_%282595031014%29.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Iris_versicolor_quebec_1.jpg/320px-Iris_versicolor_quebec_1.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/thumb/f/f8/Iris_virginica_2.jpg/480px-Iris_virginica_2.jpg",
]
# add images
for col, src in enumerate(sources):
    fig.add_layout_image(
        row=1,
        col=col + 1,
        source=src,
        xref="x domain",
        yref="y domain",
        x=1,
        y=1,
        xanchor="right",
        yanchor="top",
        sizex=0.2,
        sizey=0.2,
    )

fig.show()

Reference¶

See https://plotly.com/python/reference/layout/images/ for more information and chart attribute options!

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter