5 Best Ways to Convert Python Numpy Array to cv2 Mat

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💡 Problem Formulation: In computer vision tasks, it’s quite common to toggle between NumPy arrays and OpenCV Mat objects. You may start with image data in a NumPy array from a library that reads images in non-OpenCV formats, but then you need to use OpenCV for processing. Conversely, you might start with an OpenCV Mat object and need to use libraries that require NumPy arrays. This article will illustrate how to convert a NumPy array to a cv2 Mat object in Python, from straightforward approaches to a more sophisticated one-liner.

Method 1: Using cv2.mat() Constructor

The cv2 Mat constructor can take a numpy array and create a new Mat object out of it. This method ensures that the numpy array is not only copied but also shares the same data buffer. If the numpy array changes, the Mat object’s data also change. This is efficient as it avoids unnecessary data duplication.

Here’s an example:

import numpy as np
import cv2

# Create a random numpy array
numpy_array = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)

# Convert to a cv2 Mat object
mat_image = cv2.mat(numpy_array)

The code snippet creates a random numpy array and then converts it into a cv2 Mat object using the cv2.mat() constructor.

Method 2: Using numpy.asarray()

The numpy.asarray() function can be used to convert the numpy array to a cv2 Mat by ensuring the type consistency that OpenCV expects. The numpy array must have the correct shape and data type that corresponds to a cv2 Mat.

Here’s an example:

import numpy as np
import cv2

# Assume 'numpy_array' is a numpy array that you want to convert
numpy_array = numpy_array.astype(np.uint8)
mat_image = np.asarray(numpy_array)

The output would be a cv2 Mat object derived from the numpy array after ensuring it’s of type uint8 to be compatible with OpenCV requirements.

Method 3: Native Casting with numpy.ndarray.view()

Casting a numpy array into a different type using view() exploits numpy’s internal layout to produce a view of the array with the appropriate data type. This method is light on memory usage, as it does not copy the data but rather interprets it in a different way.

Here’s an example:

import numpy as np
import cv2

# Create a random numpy array
numpy_array = np.random.randint(0, 256, (100, 100, 3), dtype=np.float32)

# View it as unsigned int (cv2 default)
mat_image = numpy_array.view(dtype=np.uint8)

The code snippet takes a random floating-point numpy array and views it as unsigned int, which matches the default data type expected by cv2 Mat objects.

Method 4: Using cv2.imdecode()

If numpy array represents raw image data, you can use cv2.imdecode() to interpret the array as image data and return a Mat object. This is particularly useful when reading from network sources or encryption/decryption where the data may be in the form of a byte array.

Here’s an example:

import numpy as np
import cv2

# Create a random bytes array that simulates image data
raw_data = np.random.bytes(100 * 100)

# Decode the raw data into cv2 Mat
mat_image = cv2.imdecode(np.frombuffer(raw_data, np.uint8), cv2.IMREAD_UNCHANGED)

The code snippet takes a raw data byte array and converts it into a cv2 Mat using cv2.imdecode().

Bonus One-Liner Method 5: Using NumPy Method

Finally, a quick and easy one-liner approach, if you already have a numpy array with the right type and shape, is to simply pass it into any OpenCV function. Most OpenCV functions are built to take numpy arrays directly.

Here’s an example:

import numpy as np
import cv2

# Create a random numpy array
numpy_array = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)

# Use the numpy array directly in an OpenCV function
blurred_image = cv2.GaussianBlur(numpy_array, (5, 5), 0)

The output is the blurred image, and here numpy_array is implicitly converted into a cv2 Mat object inside the function.

Summary/Discussion

  • Method 1: cv2.mat() Constructor. Efficient. Shares memory with the numpy array, which can be a pro or a con depending on whether you want changes to the array to reflect in the Mat.
  • Method 2: numpy.asarray(). Versatile. Requires that the numpy array has the correct type, otherwise an additional type casting is needed.
  • Method 3: Native Casting with numpy.ndarray.view(). Memory efficient. Can be cryptic to those not familiar with numpy’s memory model.
  • Method 4: Using cv2.imdecode(). Ideal for raw image data. Overhead includes decoding step, which may not be necessary if data is already in the appropriate format.
  • Bonus Method 5: NumPy Method. Simple. Only works if the conditions (type and shape) are already met, otherwise preprocessing of numpy array is needed.