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- from datetime import datetime
- from PIL import Image
- import numpy as np
- import matplotlib.pyplot as plt
- def get_image_date(img_path: str) -> datetime:
- """Returns the date from the image EXIF data.
- Args:
- img_path (str): path to image
- Returns:
- datetime: datetime extracted from EXIF data
- """
- img = Image.open(img_path)
- date_raw = img.getexif()[306]
- return datetime.strptime(date_raw, "%Y:%m:%d %H:%M:%S")
- def display_images(images: list, titles: list, colorbar=False, size=(8, 5), row_size=2, **imshowargs):
- """Displays the given images next to each other.
- Args:
- images (list of np.ndarray): list of image arrays
- titles (list of str): list of titles
- colorbar (bool, optional): Display colorbars. Defaults to False.
- size (tuple of ints, optional): plt size (width, height) per image. Defaults to (8, 5).
- """
- num_imgs = len(images)
- num_cols = row_size
- num_rows = (num_imgs - 1) // num_cols + 1
- plt.figure(figsize=(num_cols * size[0], num_rows * size[1]))
- for i, image, title in zip(range(num_imgs), images, titles):
- plt.subplot(num_rows, num_cols, i + 1)
- plt.imshow(image, **imshowargs)
- plt.title(title)
- if colorbar:
- plt.colorbar()
- plt.tight_layout()
- plt.show()
- def is_daytime(img, threshold=50) -> bool:
- return np.mean([abs(img[:,:,0] - img[:,:,1]), abs(img[:,:,1] - img[:,:,2]), abs(img[:,:,2] - img[:,:,0])]) > threshold
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