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import numpy as np
import io
from PIL import Image, ImageFilter, ImageChops
from torchvision import transforms
def softmax(vector):
e = np.exp(vector - np.max(vector)) # for numerical stability
return e / e.sum()
def augment_image(img_pil, methods, rotate_degrees=0, noise_level=0, sharpen_strength=1):
for method in methods:
if method == "rotate":
img_pil = img_pil.rotate(rotate_degrees)
elif method == "add_noise":
noise = np.random.normal(0, noise_level, img_pil.size[::-1] + (3,)).astype(np.uint8)
img_pil = Image.fromarray(np.clip(np.array(img_pil) + noise, 0, 255).astype(np.uint8))
elif method == "sharpen":
img_pil = img_pil.filter(ImageFilter.UnsharpMask(radius=2, percent=sharpen_strength, threshold=3))
return img_pil, img_pil
def convert_pil_to_bytes(image, format='JPEG'):
img_byte_arr = io.BytesIO()
image.save(img_byte_arr, format=format)
img_byte_arr = img_byte_arr.getvalue()
return img_byte_arr
def ELA(img_pil, scale=77, alpha=0.66):
# Error Level Analysis for basic image forensics
original = img_pil.copy() # open up the input image
temp_path = 'temp.jpg' # temporary image name to save the ELA to
original.save(temp_path, quality=95) # re-save the image with a quality of 95%
temporary = Image.open(temp_path) # open up the re-saved image
diff = ImageChops.difference(original, temporary) # load in the images to look at pixel by pixel differences
d = diff.load() # load the image into a variable
WIDTH, HEIGHT = diff.size # set the size into a tuple
for x in range(WIDTH): # row by row
for y in range(HEIGHT): # column by column
d[x, y] = tuple(k * scale for k in d[x, y]) # set the pixels to their x,y & color based on error
new_img = ImageChops.blend(temporary, diff, alpha) # blend the original w/ the ELA @ a set alpha/transparency
return new_img
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