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30c5114 32b6dbd 30c5114 32b6dbd 30c5114 ed89f9e 30c5114 ed89f9e 30c5114 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | import numpy as np
from torchvision.transforms import functional as TF
from torchvision.transforms import transforms
from PIL import Image
import cv2
import logging
def to_hsv(image):
"""Convert PIL image to HSV color space"""
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2HSV)
return TF.to_pil_image(image)
# Define the preprocessing function for a single image
def preprocess_image(image):
logging.info("Preprocessing image")
# Load the image
img = Image.open(image)
# Ensure the image is in RGB format
if isinstance(img, Image.Image):
img = img.convert("RGB")
elif isinstance(img, (np.ndarray, np.generic)):
if img.shape[-1] == 1: # Grayscale image
img = np.stack([img, img, img], axis=-1)
elif img.shape[-1] != 3: # Not RGB or Grayscale
logging.error("Input image must be in RGB or Grayscale format")
raise ValueError("Input image must be in RGB or Grayscale format")
else:
logging.error("Unsupported image type")
raise ValueError("Unsupported image type")
# Define the transformations
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
# Apply the transformations to the image
preprocessed_img = transform(img)
# Add batch dimension and reorder dimensions to [1, 224, 224, 3]
preprocessed_img = preprocessed_img.unsqueeze(0).permute(0, 2, 3, 1)
return preprocessed_img |