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Running
on
Zero
File size: 4,377 Bytes
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import torch
import numpy as np
import torch
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
from PIL import Image
from PIL import Image, ExifTags
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def correct_image_orientation(image_path):
# Mở ảnh
image = Image.open(image_path)
# Kiểm tra dữ liệu Exif (nếu có)
try:
exif = image._getexif()
if exif is not None:
for tag, value in exif.items():
if ExifTags.TAGS.get(tag) == "Orientation":
# Sửa hướng dựa trên Orientation
if value == 3:
image = image.rotate(180, expand=True)
elif value == 6:
image = image.rotate(-90, expand=True)
elif value == 8:
image = image.rotate(90, expand=True)
break
except Exception as e:
print("Không thể xử lý Exif:", e)
return image
def load_image(image_file, input_size=448, max_num=12):
try:
print("Loading image:", image_file)
image = correct_image_orientation(image_file).convert('RGB')
print("Image size:", image.size)
transform = build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
print("Number of images:", len(images))
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
print("Image loaded successfully.")
except Exception as e:
print("Error loading image:", e)
pixel_values = None
return pixel_values |