Use openCV to open images sent to ML models instead of Pillow #1708
Description
openCV is much, much faster:
image shape: (1200, 607, 3)
cv2.imdecode time: 0.010s
Image.open time: 0.028s
Image.open time (including np conversion): 0.348s
---
image shape: (400, 333, 3)
cv2.imdecode time: 0.002s
Image.open time: 0.002s
Image.open time (including np conversion): 0.113s
---
image shape: (4096, 3072, 3)
cv2.imdecode time: 0.206s
Image.open time: 0.247s
Image.open time (including np conversion): 5.123s
Script:
import io
import time
import cv2
import numpy as np
import requests
from PIL import Image
for image_url in (
"https://world.openfoodfacts.org/images/products/281/304/702/0270/1.jpg",
"https://images.openfoodfacts.org/images/products/210/010/055/9939/2.jpg",
"https://prices.openfoodfacts.org/img/0100/NLhwECq0fH.webp",
):
content_bytes = requests.get(image_url).content
start_time = time.monotonic()
image = cv2.imdecode(
np.frombuffer(content_bytes, dtype=np.uint8), cv2.IMREAD_COLOR_RGB
)
print(f"image shape: {image.shape}")
print(f"cv2.imdecode time: {time.monotonic() - start_time:.3f}s")
start_time = time.monotonic()
image = Image.open(io.BytesIO(content_bytes))
image.load() # Ensure the image is fully loaded
print(f"Image.open time: {time.monotonic() - start_time:.3f}s")
np.array(image.getdata()).reshape((image.height, image.width, 3))
print(
f"Image.open time (including np conversion): {time.monotonic() - start_time:.3f}s"
)
``