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Image classification guide for Python

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The MediaPipe Image Classifier task lets you perform classification on images. You can use this task to identify what an image represents among a set of categories defined at training time. These instructions show you how to use the Image Classifier with Python.

You can see this task in action by viewing the Web demo. For more information about the capabilities, models, and configuration options of this task, see the Overview.

Code example

The example code for Image Classifier provides a complete implementation of this task in Python for your reference. This code helps you test this task and get started on building your own image classifier. You can view, run, and edit the Image Classifier example code using just your web browser.

Setup

This section describes key steps for setting up your development environment and code projects specifically to use Image Classifier. For general information on setting up your development environment for using MediaPipe tasks, including platform version requirements, see the Setup guide for Python.

Packages

The Image Classifier task the mediapipe pip package. You can install the dependency with the following:

$ python -m pip install mediapipe

Imports

Import the following classes to access the Image Classifier task functions:

import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision

Model

The MediaPipe Image Classifier task requires a trained model that is compatible with this task. For more information on available trained models for Image Classifier, see the task overview Models section.

Select and download a model, and then store it in a local directory. You can use the recommended EfficientNet-Lite0 model.

model_path = '/absolute/path/to/efficientnet_lite0_int8_2.tflite'

Specify the path of the model within the Model Name parameter, as shown below:

base_options = BaseOptions(model_asset_path=model_path)

Create the task

Use the create_from_options function to create the task. The create_from_options function accepts configuration options including running mode, display names locale, max number of results, confidence threshold, category allow list, and deny list. For more information on configuration options, see Configuration Overview.

The Image Classifier task supports 3 input data types: still images, video files and live video streams. Choose the tab corresponding to your input data type to see how to create the task and run inference.

Image

import mediapipe as mp

BaseOptions = mp.tasks.BaseOptions
ImageClassifier = mp.tasks.vision.ImageClassifier
ImageClassifierOptions = mp.tasks.vision.ImageClassifierOptions
VisionRunningMode = mp.tasks.vision.RunningMode

options = ImageClassifierOptions(
    base_options=BaseOptions(model_asset_path='/path/to/model.tflite'),
    max_results=5,
    running_mode=VisionRunningMode.IMAGE)

with ImageClassifier.create_from_options(options) as classifier:
  # The classifier is initialized. Use it here.
  # ...
    

Video

import mediapipe as mp

BaseOptions = mp.tasks.BaseOptions
ImageClassifier = mp.tasks.vision.ImageClassifier
ImageClassifierOptions = mp.tasks.vision.ImageClassifierOptions
VisionRunningMode = mp.tasks.vision.RunningMode

options = ImageClassifierOptions(
    base_options=BaseOptions(model_asset_path='/path/to/model.tflite'),
    max_results=5,
    running_mode=VisionRunningMode.VIDEO)

with ImageClassifier.create_from_options(options) as classifier:
  # The classifier is initialized. Use it here.
  # ...
    

Live stream

import mediapipe as mp

BaseOptions = mp.tasks.BaseOptions
ImageClassifierResult = mp.tasks.vision.ImageClassifier.ImageClassifierResult
ImageClassifier = mp.tasks.vision.ImageClassifier
ImageClassifierOptions = mp.tasks.vision.ImageClassifierOptions
VisionRunningMode = mp.tasks.vision.RunningMode

def print_result(result: ImageClassifierResult, output_image: mp.Image, timestamp_ms: int):
    print('ImageClassifierResult result: {}'.format(result))

options = ImageClassifierOptions(
    base_options=BaseOptions(model_asset_path='/path/to/model.tflite'),
    running_mode=VisionRunningMode.LIVE_STREAM,
    max_results=5,
    result_callback=print_result)

with ImageClassifier.create_from_options(options) as classifier:
  # The classifier is initialized. Use it here.
  # ...
    

For a complete example of creating a Image Classifier for use with an image, see the code example.

Configuration options

This task has the following configuration options for Python applications:

Option Name Description Value Range Default Value
running_mode Sets the running mode for the task. Image Classifier has three modes:

IMAGE: The mode for performing classification on single image inputs.

VIDEO: The mode for performing classification on the decoded frames of a video.

LIVE_STREAM: The mode for performing classification on a live stream of input data, such as from a camera. In this mode, resultListener must be called to set up a listener to receive the classification results asynchronously.
{IMAGE, VIDEO, LIVE_STREAM} IMAGE
display_names_locale Sets the language of labels to use for display names provided in the metadata of the task's model, if available. Default is en for English. You can add localized labels to the metadata of a custom model using the TensorFlow Lite Metadata Writer API Locale code en
max_results Sets the optional maximum number of top-scored classification results to return. If < 0, all available results will be returned. Any positive numbers -1
score_threshold Sets the prediction score threshold that overrides the one provided in the model metadata (if any). Results below this value are rejected. Any float Not set
category_allowlist Sets the optional list of allowed category names. If non-empty, classification results whose category name is not in this set will be filtered out. Duplicate or unknown category names are ignored. This option is mutually exclusive with category_denylist and using both results in an error. Any strings Not set
category_denylist Sets the optional list of category names that are not allowed. If non-empty, classification results whose category name is in this set will be filtered out. Duplicate or unknown category names are ignored. This option is mutually exclusive with category_allowlist and using both results in an error. Any strings Not set
result_callback Sets the result listener to receive the classification results asynchronously when the Image Classifier is in the live stream mode. Can only be used when running mode is set to LIVE_STREAM N/A Not set

Prepare data

Prepare your input as an image file or a numpy array, then convert it to a mediapipe.Image object. If your input is a video file or live stream from a webcam, you can use an external library such as OpenCV to load your input frames as numpy arrays.

The following examples explain and show how to prepare data for processing for each of the available data types

Image

import mediapipe as mp

# Load the input image from an image file.
mp_image = mp.Image.create_from_file('/path/to/image')

# Load the input image from a numpy array.
mp_image = mp.Image(format=ImageFormat.SRGB, data=numpy_image)
    

Video

import mediapipe as mp

# Use OpenCV’s VideoCapture to load the input video.

# Load the frame rate of the video using OpenCV’s CV_CAP_PROP_FPS
# You’ll need it to calculate the timestamp for each frame.

# Loop through each frame in the video using VideoCapture#read()

# Convert the frame received from OpenCV to a MediaPipe’s Image object.
mp_image = mp.Image(format=ImageFormat.SRGB, data=numpy_frame_from_opencv)
    

Live stream

import mediapipe as mp

# Use OpenCV’s VideoCapture to start capturing from the webcam.

# Create a loop to read the latest frame from the camera using VideoCapture#read()

# Convert the frame received from OpenCV to a MediaPipe’s Image object.
mp_image = mp.Image(format=ImageFormat.SRGB, data=numpy_frame_from_opencv)
    

Run the task

You can call the classify function corresponding to your running mode to trigger inferences. The Image Classifier API will return the possible categories for the object within the input image or frame.

Image

# Perform image classification on the provided single image.
classification_result = classifier.classify(mp_image)
    

Video

# Calculate the timestamp of the current frame
frame_timestamp_ms = 1000 * frame_index / video_file_fps

# Perform image classification on the video frame.
classification_result = classifier.classify_for_video(mp_image, frame_timestamp_ms)
    

Live stream


# Send the latest frame to perform image classification.
# Results are sent to the `result_callback` provided in the `ImageClassifierOptions`.
classifier.classify_async(mp_image, frame_timestamp_ms)
    

Note the following:

  • When running in the video mode or the live stream mode, you must also provide the Image Classifier task the timestamp of the input frame.
  • When running in the image or the video model, the Image Classifier task will block the current thread until it finishes processing the input image or frame.
  • When running in the live stream mode, the Image Classifier task doesn’t block the current thread but returns immediately. It will invoke its result listener with the classification result every time it has finished processing an input frame. If the classifyAsync function is called when the Image Classifier task is busy processing another frame, the task ignores the new input frame.

For a complete example of creating a Image Classifier for use with an image, see the code example.

Handle and display results

Upon running inference, the Image Classifier task returns an ImageClassifierResult object which contains the list of possible categories for the objects within the input image or frame.

The following shows an example of the output data from this task:

ImageClassifierResult:
 Classifications #0 (single classification head):
  head index: 0
  category #0:
   category name: "/m/01bwb9"
   display name: "Passer domesticus"
   score: 0.91406
   index: 671
  category #1:
   category name: "/m/01bwbt"
   display name: "Passer montanus"
   score: 0.00391
   index: 670

This result has been obtained by running the Bird Classifier on:

The Image Classifier example code demonstrates how to display the classification results returned from the task, see the code example for details.