import cv2
import numpy as np
import cupy as cp
import time


def op_image(image):
    start_time = time.time()
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = cv2.resize(image, (640, 480))
    image_mean = np.array([127, 127, 127])
    image = (image - image_mean) / 128
    image = np.transpose(image, [2, 0, 1])
    image = np.expand_dims(image, axis=0)
    image = image.astype(np.float32)
    print("Opencv End time : ",time.time() - start_time)


def Cu_image(image):
    start_time = time.time()
    image = cp.array(image)
    image = cp.ascontiguousarray(image[:, :, ::-1])
    image = cp.resize(image, (640, 480,3))
    image_mean = cp.array([127, 127, 127])
    image = (image - image_mean) / 128
    image = cp.transpose(image, (2, 0, 1))
    image = cp.expand_dims(image, axis=0)
    image = cp.expand_dims(image, axis=0)
    image = image.astype(cp.float32)
    print("Cupy End time : ",time.time() - start_time)


image = cv2.imread("/home/nextbrain-ssd/Vision/cctv-server/assets/event_created_images/camera_0_9_509_2024-05-23T04:14:32.931834Z.jpg")
op_image(image=image)
image = cv2.imread("/home/nextbrain-ssd/Vision/cctv-server/assets/event_created_images/camera_0_9_509_2024-05-23T04:14:32.931834Z.jpg")
Cu_image(image=image)

Output:

Opencv End time :  0.00825643539428711
Cupy End time :  0.10329556465148926

The above code preprocesses an image using two different libraries: OpenCV (CPU) and CuPy (GPU). Surprisingly, CuPy takes more time compared to OpenCV. OpenCV processes the image using the CPU, while CuPy uses the GPU. I expected CuPy to be faster, but it’s the opposite. Why does CuPy take longer than OpenCV?