Data Process


This module provides some image processing methods, which requires opencv to be installed.


To read or write images files, use imread or imwrite.

import mmcv

img = mmcv.imread('test.jpg')
img = mmcv.imread('test.jpg', flag='grayscale')
img_ = mmcv.imread(img)  # nothing will happen, img_ = img
mmcv.imwrite(img, 'out.jpg')

To read images from bytes

with open('test.jpg', 'rb') as f:
    data =
img = mmcv.imfrombytes(data)

To show an image file or a loaded image

# this is equivalent to

for i in range(10):
    img = np.random.randint(256, size=(100, 100, 3), dtype=np.uint8)
    mmcv.imshow(img, win_name='test image', wait_time=200)

Color space conversion

Supported conversion methods:

  • bgr2gray

  • gray2bgr

  • bgr2rgb

  • rgb2bgr

  • bgr2hsv

  • hsv2bgr

img = mmcv.imread('tests/data/color.jpg')
img1 = mmcv.bgr2rgb(img)
img2 = mmcv.rgb2gray(img1)
img3 = mmcv.bgr2hsv(img)


There are three resize methods. All imresize_* methods have an argument return_scale, if this argument is False, then the return value is merely the resized image, otherwise is a tuple (resized_img, scale).

# resize to a given size
mmcv.imresize(img, (1000, 600), return_scale=True)

# resize to the same size of another image
mmcv.imresize_like(img, dst_img, return_scale=False)

# resize by a ratio
mmcv.imrescale(img, 0.5)

# resize so that the max edge no longer than 1000, short edge no longer than 800
# without changing the aspect ratio
mmcv.imrescale(img, (1000, 800))


To rotate an image by some angle, use imrotate. The center can be specified, which is the center of original image by default. There are two modes of rotating, one is to keep the image size unchanged so that some parts of the image will be cropped after rotating, the other is to extend the image size to fit the rotated image.

img = mmcv.imread('tests/data/color.jpg')

# rotate the image clockwise by 30 degrees.
img_ = mmcv.imrotate(img, 30)

# rotate the image counterclockwise by 90 degrees.
img_ = mmcv.imrotate(img, -90)

# rotate the image clockwise by 30 degrees, and rescale it by 1.5x at the same time.
img_ = mmcv.imrotate(img, 30, scale=1.5)

# rotate the image clockwise by 30 degrees, with (100, 100) as the center.
img_ = mmcv.imrotate(img, 30, center=(100, 100))

# rotate the image clockwise by 30 degrees, and extend the image size.
img_ = mmcv.imrotate(img, 30, auto_bound=True)


To flip an image, use imflip.

img = mmcv.imread('tests/data/color.jpg')

# flip the image horizontally

# flip the image vertically
mmcv.imflip(img, direction='vertical')


imcrop can crop the image with one or some regions, represented as (x1, y1, x2, y2).

import mmcv
import numpy as np

img = mmcv.imread('tests/data/color.jpg')

# crop the region (10, 10, 100, 120)
bboxes = np.array([10, 10, 100, 120])
patch = mmcv.imcrop(img, bboxes)

# crop two regions (10, 10, 100, 120) and (0, 0, 50, 50)
bboxes = np.array([[10, 10, 100, 120], [0, 0, 50, 50]])
patches = mmcv.imcrop(img, bboxes)

# crop two regions, and rescale the patches by 1.2x
patches = mmcv.imcrop(img, bboxes, scale_ratio=1.2)


There are two methods impad and impad_to_multiple to pad an image to the specific size with given values.

img = mmcv.imread('tests/data/color.jpg')

# pad the image to (1000, 1200) with all zeros
img_ = mmcv.impad(img, shape=(1000, 1200), pad_val=0)

# pad the image to (1000, 1200) with different values for three channels.
img_ = mmcv.impad(img, shape=(1000, 1200), pad_val=[100, 50, 200])

# pad the image on left, right, top, bottom borders with all zeros
img_ = mmcv.impad(img, padding=(10, 20, 30, 40), pad_val=0)

# pad the image on left, right, top, bottom borders with different values
# for three channels.
img_ = mmcv.impad(img, padding=(10, 20, 30, 40), pad_val=[100, 50, 200])

# pad an image so that each edge is a multiple of some value.
img_ = mmcv.impad_to_multiple(img, 32)


This module provides the following functionalities.

  • A VideoReader class with friendly apis to read and convert videos.

  • Some methods for editing (cut, concat, resize) videos.

  • Optical flow read/write/warp.


The VideoReader class provides sequence like apis to access video frames. It will internally cache the frames which have been visited.

video = mmcv.VideoReader('test.mp4')

# obtain basic information
print(video.width, video.height, video.resolution, video.fps)

# iterate over all frames
for frame in video:

# read the next frame
img =

# read a frame by index
img = video[100]

# read some frames
img = video[5:10]

To convert a video to images or generate a video from a image directory.

# split a video into frames and save to a folder
video = mmcv.VideoReader('test.mp4')

# generate video from frames
mmcv.frames2video('out_dir', 'test.avi')

Editing utils

There are also some methods for editing videos, which wraps the commands of ffmpeg.

# cut a video clip
mmcv.cut_video('test.mp4', 'clip1.mp4', start=3, end=10, vcodec='h264')

# join a list of video clips
mmcv.concat_video(['clip1.mp4', 'clip2.mp4'], 'joined.mp4', log_level='quiet')

# resize a video with the specified size
mmcv.resize_video('test.mp4', 'resized1.mp4', (360, 240))

# resize a video with a scaling ratio of 2
mmcv.resize_video('test.mp4', 'resized2.mp4', ratio=2)

Optical flow

mmcv provides the following methods to operate on optical flows.

  • IO

  • Visualization

  • Flow warpping

We provide two options to dump optical flow files: uncompressed and compressed. The uncompressed way just dumps the floating numbers to a binary file. It is lossless but the dumped file has a larger size. The compressed way quantizes the optical flow to 0-255 and dumps it as a jpeg image. The flow of x-dim and y-dim will be concatenated into a single image.

  1. IO

flow = np.random.rand(800, 600, 2).astype(np.float32)
# dump the flow to a flo file (~3.7M)
mmcv.flowwrite(flow, 'uncompressed.flo')
# dump the flow to a jpeg file (~230K)
# the shape of the dumped image is (800, 1200)
mmcv.flowwrite(flow, 'compressed.jpg', quantize=True, concat_axis=1)

# read the flow file, the shape of loaded flow is (800, 600, 2) for both ways
flow = mmcv.flowread('uncompressed.flo')
flow = mmcv.flowread('compressed.jpg', quantize=True, concat_axis=1)
  1. Visualization

It is possible to visualize optical flows with mmcv.flowshow().



  1. Flow warpping

img1 = mmcv.imread('img1.jpg')
flow = mmcv.flowread('flow.flo')
warpped_img2 = mmcv.flow_warp(img1, flow)

img1 (left) and img2 (right)

raw images

optical flow (img2 -> img1)

optical flow

warpped image and difference with ground truth

warpped image

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