Edge Recognition - Grayscale Image

Example Results

Run the example code in this section [Source Code/06.cv_lite/7.grayscale_find_edges.py]

In this section, we will use the cv_lite extension module to implement grayscale image edge detection (Canny algorithm) on an embedded device.

Code Overview

Importing Modules

import time, os, sys, gc
from machine import Pin
from media.sensor import * # Camera interface
from media.display import * # Display interface
from media.media import * # Media manager
import _thread
import cv_lite # cv_lite extension module (including edge detection)
import ulab.numpy as np # NumPy-like ndarray for MicroPython

Setting the Image Size

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image_shape = [480, 640] # Height x Width

Define the image resolution to 480x640 (Height x Width). width), the camera and display module will be initialized based on this size later.

Initialize the camera

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sensor = Sensor(id=2, width=1280, height=960, fps=90)
sensor.reset()
sensor.set_framesize(width=image_shape[1], height=image_shape[0])
sensor.set_pixformat(Sensor.GRAYSCALE) # Set to grayscale image output / Grayscale mode

• Initialize the camera, set the resolution to 1280x960 and the frame rate to 90 FPS.
• Resize the output frame to 640x480 and set it to grayscale mode (single-channel image, saves memory and computing resources, suitable for edge detection).

Initialize the display module

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Display.init(Display.ST7701, width=image_shape[1], height=image_shape[0],
             to_ide=True, quality=50)

Initialize the display module, using the ST7701 driver chip, with a resolution consistent with the image size. to_ide=True indicates that the image will be simultaneously transmitted to the IDE for virtual display, and quality=50 sets the image transmission quality.

Initialize the media manager and start the camera

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MediaManager.init()
 Corner Recognition - Color Image
Edge Recognition - Color Image