performance_test.py

import os

from qreader import QReader
import cv2
from time import time
import numpy as np
from tqdm import tqdm

SAMPLE_IMG_1 = os.path.join(os.path.dirname(__file__), 'documentation', 'resources', '64x64.png')
SAMPLE_IMG_2 = os.path.join(os.path.dirname(__file__), 'documentation', 'resources', '512x512.jpeg')
SAMPLE_IMG_3 = os.path.join(os.path.dirname(__file__), 'documentation', 'resources', '1024x1024.jpeg')

PERFORMANCE_TEST_IAMGES = {
    '64x64': SAMPLE_IMG_1,
    #'512x512': SAMPLE_IMG_2,
    #'1024x1024': SAMPLE_IMG_3
}
RUNS_TO_AVERAGE, WARMUP_ITERATIONS = 5, 5

if __name__ == '__main__':
    results = {}
    for shape, img_path in tqdm(PERFORMANCE_TEST_IAMGES.items()):
        # Read the image
        img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
        # Run the performance test over 100 iterations (warm-up included)
        detect_times, detect_and_decode_times = [], []
        for _ in range(RUNS_TO_AVERAGE + WARMUP_ITERATIONS):
            start = time()
            QReader().detect(image=img)
            detect_times.append(time() - start)
            start = time()
            QReader().detect_and_decode(image=img)
            detect_and_decode_times.append(time() - start)
        # Save the results
        results[shape] = {
            'detect': np.mean(detect_times[WARMUP_ITERATIONS:]),
            'detect_and_decode': np.mean(detect_and_decode_times[WARMUP_ITERATIONS:])
        }
    # Print the results
    print('Performance test results:')
    for shape, times in results.items():
        print(f"Image shape: {shape} -> Detect: {times['detect']}. Detect and decode: {times['detect_and_decode']}.")