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# How to Use OpenCV with ZED in Python

## Introduction

In this tutorial, you will learn how to capture and display color and depth images using OpenCV and the ZED SDK in Python.

![image](/_fern-img/026c2de5f4daf91d579d96e546b82a260d614f4d6751ec99d4ab61d22699fcfc.webp)

## Code Example

The sample code is available on [GitHub](https://github.com/stereolabs/zed-opencv/tree/master/python). Make sure the [ZED Python API](https://github.com/stereolabs/zed-python-api) is installed before launching the sample.

## Sharing image data between ZED SDK and OpenCV Python

In Python, OpenCV stores images in NumPy arrays. Since the ZED SDK uses its own `sl.Mat` class to store image data, we provide a function `get_data()` to convert the `sl.Mat` matrix into a NumPy array.

**`Python`**

```python Python
# Create an RGBA sl.Mat object
image_zed = sl.Mat(zed.get_camera_information().camera_configuration.resolution.width, zed.get_camera_information().camera_configuration.resolution.height, sl.MAT_TYPE.U8_C4)
# Retrieve data in a numpy array with get_data()
image_ocv = image_zed.get_data()
```

## Capturing Video

To capture video, use `grab()` and `retrieve_image()`. Then use `get_data()` to retrieve the `sl.Mat` data into a NumPy array. Display the video using `cv2.imshow()`.

**`Python`**

```python Python
if zed.grab() == sl.ERROR_CODE.SUCCESS:
    # Retrieve the left image in sl.Mat
    zed.retrieve_image(image_zed, sl.VIEW.LEFT)
    # Use get_data() to get the numpy array
    image_ocv = image_zed.get_data()
    # Display the left image from the numpy array
    cv2.imshow("Image", image_ocv)
```

## Capturing Depth

A depth map is a 1-channel matrix with 32-bit float values for each pixel. Each value expresses the distance of a pixel in the scene. The depth map can be retrieved using `retrieve_measure()` and loaded with `get_data()` into a NumPy array. Please refer to the [Depth API](/docs/development/zed-sdk/modules/depth-sensing/) for more information.

**`Python`**

```python Python
# Create a sl.Mat with float type (32-bit)
depth_zed = sl.Mat(zed.get_camera_information().camera_configuration.resolution.width, zed.get_camera_information().camera_configuration.resolution.height, sl.MAT_TYPE.F32_C1)

if zed.grab() == sl.ERROR_CODE.SUCCESS:
    # Retrieve depth data (32-bit)
    zed.retrieve_measure(depth_zed, sl.MEASURE.DEPTH)
    # Load depth data into a numpy array
    depth_ocv = depth_zed.get_data()
    # Print the depth value at the center of the image
    print(depth_ocv[int(len(depth_ocv)/2)][int(len(depth_ocv[0])/2)])
```

## Displaying Depth

A NumPy array with 32-bit float values can't be displayed with `cv2.imshow`. To display the depth map, we need to normalize the depth values between 0 and 255 (8-bit) and create a black-and-white representation. Do not use this representation for other purposes than displaying the image.

**`Python`**

```python Python
# Create an RGBA sl.Mat object
image_depth_zed = sl.Mat(zed.get_camera_information().camera_configuration.resolution.width, zed.get_camera_information().camera_configuration.resolution.height, sl.MAT_TYPE.U8_C4)

if zed.grab() == sl.ERROR_CODE.SUCCESS:
    # Retrieve the normalized depth image
    zed.retrieve_image(image_depth_zed, sl.VIEW.DEPTH)
    # Use get_data() to get the numpy array
    image_depth_ocv = image_depth_zed.get_data()
    # Display the depth view from the numpy array
    cv2.imshow("Image", image_depth_ocv)
```

## UVC Capture

You can also use the ZED as a standard UVC camera in OpenCV to capture raw stereo video using the code snippet below. To get rectified images and calibration with OpenCV, use the native (Python) capture sample available on [GitHub](https://github.com/stereolabs/zed-opencv-native).

**`Python`**

```python Python
import cv2
import numpy

# Open the ZED camera
cap = cv2.VideoCapture(0)
if cap.isOpened() == 0:
    exit(-1)

# Set the video resolution to HD720 (2560*720)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 2560)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

while True:
    # Get a new frame from camera
    retval, frame = cap.read()
    # Extract left and right images from side-by-side
    left_right_image = numpy.split(frame, 2, axis=1)
    # Display images
    cv2.imshow("frame", frame)
    cv2.imshow("left", left_right_image[0])
    cv2.imshow("right", left_right_image[1])
    if cv2.waitKey(30) >= 0:
        break

exit(0)
```