Im working on to transform images using X-skew using the following code
from PIL import Image
def x_skew_image(input_path, output_path, skew_factor):
# Open the input image
input_image = Image.open(input_path)
# Get the image dimensions
width, height = input_image.size
# Calculate the new width after skewing
new_width = int(width + abs(skew_factor) * height)
# Create a new image with the calculated width and the same height
output_image = Image.new("RGB", (new_width, height))
# Apply the skew transformation
for y in range(height):
x_offset = int(skew_factor * y)
for x in range(width):
if 0 <= x + x_offset < new_width:
output_image.putpixel((x + x_offset, y), input_image.getpixel((x, y)))
# Save the skewed image
output_image.save(output_path)
# Replace these paths and skew_factor as needed
input_path = r'input_path' # Replace with the path to your input image
output_path = r'output_path' # Replace with the desired output path
skew_factor = -0.4 # Adjust the skew factor as needed
x_skew_image(input_path, output_path, skew_factor)
However, Im facing issue when trying to with negative X-skewing (change skew_factor to negative value) and it seems that the image get cropped. How can i modify the code to solve the issue?
Your code works if you change it to this:
#!/usr/bin/env python3
from PIL import Image
def x_skew_image(input_path, output_path, skew_factor):
# Open the input image
input_image = Image.open(input_path)
# Get the image dimensions
width, height = input_image.size
# Calculate the new width after skewing
new_width = int(width + abs(skew_factor) * height)
# Create a new image with the calculated width and the same height
output_image = Image.new("RGB", (new_width, height))
# Apply the skew transformation
for y in range(height):
x_offset = int(skew_factor * y)
if skew_factor < 0:
x_offset = int(-skew_factor * (height - y))
for x in range(width):
new_x = x + x_offset
output_image.putpixel((new_x, y), input_image.getpixel((x, y)))
# Save the skewed image
output_image.save(output_path)
# Replace these paths and skew_factor as needed
input_path = 'CrazyCat.jpg'
output_path = 'result.jpg'
skew_factor = -0.4 # Adjust the skew factor as needed
x_skew_image(input_path, output_path, skew_factor)
With skew_factor=0.4
:
With skew_factor=-0.4
:
However, I would really advise AGAINST using for
loops for image processing in Python - they are slow and error-prone. There is a noticeable delay when running your code versus using the built-in affine transform method which is instantaneous and simpler:
#!/usr/bin/env python3
from PIL import Image
input_path = 'CrazyCat.jpg'
output_path = 'result.jpg'
im = Image.open(input_path)
w , h = im.size
shear_factor = 0.4
new_width = int(w + abs(shear_factor)*h)
if shear_factor > 0:
res = im.transform((new_width,h), Image.AFFINE, (1, shear_factor, -shear_factor*h, 0, 1, 0))
else:
res = im.transform((new_width,h), Image.AFFINE, (1, shear_factor, 0, 0, 1, 0))
res.save('result.jpg')
Referring to your secondary question about filling the surrounding black area, you can try this for a solid greenish colour:
res = im.transform((new_width,h), Image.AFFINE, (1, shear_factor, -shear_factor*h, 0, 1, 0), fillcolor='#506050')
If you want to fill the undefined parts of your sheared image with a blurred version of your original image, you can do that like this - your tastes on blur radius and so on may vary but you should get the idea:
#!/usr/bin/env python3
from PIL import Image, ImageFilter
input_path = 'CrazyCat.jpg'
output_path = 'result.jpg'
# Open image and get dimensions
im = Image.open(input_path)
w , h = im.size
# Generate same-size mask for choosing where to paste sheared image onto blurred background
mask = Image.new('L', (w,h), 'white')
mask.save('DEBUG-mask.jpg') # debug only
# Shear using affine transform
shear_factor = 0.4
new_width = int(w + abs(shear_factor)*h)
if shear_factor > 0:
res = im.transform((new_width,h), Image.AFFINE, (1, shear_factor, -shear_factor*h, 0, 1, 0))
mask = mask.transform((new_width,h), Image.AFFINE, (1, shear_factor, -shear_factor*h, 0, 1, 0))
else:
res = im.transform((new_width,h), Image.AFFINE, (1, shear_factor, 0, 0, 1, 0))
mask = mask.transform((new_width,h), Image.AFFINE, (1, shear_factor, 0, 0, 1, 0))
# Generate blurred, extended backgound
bg = im.copy().resize((new_width,h)).filter(ImageFilter.GaussianBlur(50))
bg.save('DEBUG-bg.jpg') # debug only
# Paste sheared original over blurred background using mask to select which image to source from
bg.paste(res, mask)
bg.save('result.jpg')