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| 1 | +#------------------------------------------------------------------------------- |
| 2 | +# SPDX-License-Identifier: MIT |
| 3 | +# |
| 4 | +# Copyright (c) 2025 SparkFun Electronics |
| 5 | +#------------------------------------------------------------------------------- |
| 6 | +# ex05_performance.py |
| 7 | +# |
| 8 | +# This example |
| 9 | +#------------------------------------------------------------------------------- |
| 10 | + |
| 11 | +# Import OpenCV and hardware initialization module |
| 12 | +import cv2 as cv |
| 13 | +from cv2_hardware_init import * |
| 14 | + |
| 15 | +# Import NumPy |
| 16 | +from ulab import numpy as np |
| 17 | + |
| 18 | +# Import time for frame rate calculation |
| 19 | +import time |
| 20 | + |
| 21 | +# Initialize a loop timer to calculate processing speed in FPS |
| 22 | +loop_time = time.ticks_us() |
| 23 | + |
| 24 | +# Open the camera |
| 25 | +camera.open() |
| 26 | + |
| 27 | +# The `read()` method of OpenCV camera drivers can optionally take an output |
| 28 | +# image as an argument. When it's not provided, the camera driver must allocate |
| 29 | +# a whole new image for the frame, which can be slow and waste memory. If the |
| 30 | +# image argument is provided, then the camera driver will write the data to the |
| 31 | +# provided image. The image must be a NumPy array with the same shape and data |
| 32 | +# type as the camera's |
| 33 | +success, frame = camera.read() |
| 34 | + |
| 35 | +# Prompt the user to press a key to continue |
| 36 | +print("Press any key to continue") |
| 37 | + |
| 38 | +# Loop to continuously read frames from the camera and display them |
| 39 | +while True: |
| 40 | + # Read a frame from the camera |
| 41 | + success, frame = camera.read() |
| 42 | + if not success: |
| 43 | + print("Failed to read frame from camera") |
| 44 | + break |
| 45 | + |
| 46 | + # Now we'll |
| 47 | + |
| 48 | + # It's a good idea to measure the frame rate of the main loop to see how |
| 49 | + # fast the entire pipeline is running. This will include not only the |
| 50 | + # processing steps, but also any overhead from the hardware drivers and |
| 51 | + # other code. We can calculate the FPS with the loop timer and draw it on |
| 52 | + # the frame for visualization |
| 53 | + current_time = time.ticks_us() |
| 54 | + fps = 1_000_000 / (current_time - loop_time) |
| 55 | + loop_time = current_time |
| 56 | + frame = cv.putText(frame, f"FPS: {fps:.2f}", (10, 30), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) |
| 57 | + |
| 58 | + # Display the frame |
| 59 | + cv.imshow(display, frame) |
| 60 | + |
| 61 | + # Check for key presses |
| 62 | + key = cv.waitKey(1) |
| 63 | + |
| 64 | + # If any key is pressed, exit the loop |
| 65 | + if key != -1: |
| 66 | + break |
| 67 | + |
| 68 | +# Release the camera |
| 69 | +camera.release() |
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