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I am trying to import, resize, and conditionally rotate an image with OpenCV but I'm running into some trouble. To bring in the image and resize it I use:
def draw_plane(self):
# Import image for plane
plane_path = 'planes/' + self.plane + '.jpg'
plane = Image.open(plane_path)
# Get image size
bg_height = plane.size[1]
bg_width = plane.size[0]
# Resize and crop image
if bg_height > bg_width:
# Resize
ratio = bg_width/bg_height
img_width = self.p_width
img_height = int(self.p_height/ratio)
plane_resized = plane.resize((img_width,img_height))
# Crop
top = int((img_height-self.p_height)/2)
bottom = int(((img_height-self.p_height)/2)+self.p_height)
plane_cropped = plane_resized.crop((0,top,self.p_width,bottom))
print('top:',top,'\nbottom:',bottom)
self.plane_img = plane_cropped
if bg_height < bg_width:
# Resize
ratio = bg_height/bg_width
img_width = int(self.p_width/ratio)
img_height = self.p_height
plane_resized = plane.resize((img_width,img_height))
# Crop
left = int((img_width-self.p_width)/2)
right = int(((img_width-self.p_width)/2)+self.p_width)
plane_cropped = plane_resized.crop((left,0,right,self.p_height))
self.plane_img = plane_cropped
else:
pass
If the name of an image being used as a frame for plane is in a list I call the following method and if the first item in a list of attributes for the final composition is "Polaroid" I want it to rotate plane.
def adjust_plane(self):
if a.attr[0] == 'polaroid':
plane = self.plane_img
height, width = plane.shape[:2] <----
center = (width/2, height/2)
rotate_matrix = cv2.getRotationMatrix2D(center=center, angle=-30, scale=1)
rotated_plane = cv2.warpAffine(plane, rotate_matrix, (width, height))
self.plane_img = rotated_plane
But when I run the code I get: "AttributeError: shape" on the line I noted in the code block. This is all taking place in the same class, including the conditional that triggers adjust_plane().
I admit that I am at a point in learning to program that I am just beginning to wrap my head around objects as a concept. Is there maybe some issue that this is no longer an image but is an "image object", if there is such a thing? Any help is appreciated, I've been chewing on this error for far too long.
It appears that the issue is that OpenCV and PIL images don't play well together. Someone else could better explain exactly why.
I brought in OpenCV to rotate the image because I thought PIL could not rotate an image by specific degree rather than just 90° steps, but I was wrong about that. The code below accomplishes what I wanted with the PIL library and I was able to do away with OpenCV.
plane.rotate(30, Image.NEAREST, expand = 1)
How can I crop images that looks like this and save as 3 different images?
The issue is that images are different in size and non-proportional, so I want to make a code that dynamically cuts black borders but not the black part which is inside the picture.
Here is the desired outcome:
Below is the sample code I've made which works only for one specific image.
from PIL import Image
im = Image.open(r"image.jpg")
# Setting the points for cropped image1
# im1 = im.crop((left, top, right, bottom))
im1 = im.crop((...))
im2 = im.crop((...))
im3 = im.crop((...))
im1 = im1.save(r"image1.jpg")
im2 = im2.save(r"image2.jpg")
im3 = im3.save(r"image3.jpg")
Finally I've found the solution. Here is what I did:
from PIL import Image, ImageChops
def RemoveBlackBorders(img):
bg = Image.new(img.mode, img.size, img.getpixel((0,0)))
diff = ImageChops.difference(img, bg)
diff = ImageChops.add(diff, diff, 2.0, -100)
bbox = diff.getbbox()
if bbox:
return img.crop(bbox)
# Opens a image in RGB mode
im = Image.open(r"C:\Path\Image.jpg")
# removing borders
im = RemoveBlackBorders(im)
# getting midpoint from size
width, height = im.size
mwidth = width/2
# assign shape of figure from the midpoint
#crop((x,y of top left, x, y of bottom right))
im1 = im.crop((0, 0, mwidth-135, height))
im2 = im.crop((mwidth-78, 0, mwidth+84, height))
im3 = im.crop((mwidth+135, 0, width, height))
The function to remove borders I've found from here.
Although the solution is not completely dynamic, it still solves my problem with ~90% accuracy. But I believe there should be a more universal approach for this problem.
If the areas have always the same size and the same top and bottom coordinates the following should work:
The coordinates for the crops can be retrieved by calculating the sums per rows and per columns, then analyzing them.
import cv2
import numpy as np
im = cv2.imread(image_path)
sum_of_rows = np.sum(im, axis=(1,2))
sum_of_cols = np.sum(im, axis=(0,2))
The top and bottom can be calculated by calculating the sum for each row (each sum value being calculated R+G+B, the value should be zero for black). Then looking for the first value being different form zero and the last value being different than zero. Both indicating the top and bottom.
top = np.argmax(sum_of_rows > 0)
bottom = top + np.argmax(sum_of_rows[top:]==0)
The same can be done for the sum for each column, but here checking for multiple left and right values.
I have a large number of images of a fixed size (say 500*500). I want to write a python script which will resize them to a fixed size (say 800*800) but will keep the original image at the center and fill the excess area with a fixed color (say black).
I am using PIL. I can resize the image using the resize function now, but that changes the aspect ratio. Is there any way to do this?
You can create a new image with the desired new size, and paste the old image in the center, then saving it. If you want, you can overwrite the original image (are you sure? ;o)
import Image
old_im = Image.open('someimage.jpg')
old_size = old_im.size
new_size = (800, 800)
new_im = Image.new("RGB", new_size) ## luckily, this is already black!
box = tuple((n - o) // 2 for n, o in zip(new_size, old_size))
new_im.paste(old_im, box)
new_im.show()
# new_im.save('someimage.jpg')
You can also set the color of the new border with a third argument of Image.new() (for example: Image.new("RGB", new_size, "White"))
Yes, there is.
Make something like this:
from PIL import Image, ImageOps
ImageOps.expand(Image.open('original-image.png'),border=300,fill='black').save('imaged-with-border.png')
You can write the same at several lines:
from PIL import Image, ImageOps
img = Image.open('original-image.png')
img_with_border = ImageOps.expand(img,border=300,fill='black')
img_with_border.save('imaged-with-border.png')
And you say that you have a list of images. Then you must use a cycle to process all of them:
from PIL import Image, ImageOps
for i in list-of-images:
img = Image.open(i)
img_with_border = ImageOps.expand(img,border=300,fill='black')
img_with_border.save('bordered-%s' % i)
Alternatively, if you are using OpenCV, they have a function called copyMakeBorder that allows you to add padding to any of the sides of an image. Beyond solid colors, they've also got some cool options for fancy borders like reflecting or extending the image.
import cv2
img = cv2.imread('image.jpg')
color = [101, 52, 152] # 'cause purple!
# border widths; I set them all to 150
top, bottom, left, right = [150]*4
img_with_border = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
Sources: OpenCV border tutorial and
OpenCV 3.1.0 Docs for copyMakeBorder
PIL's crop method can actually handle this for you by using numbers that are outside the bounding box of the original image, though it's not explicitly stated in the documentation. Negative numbers for left and top will add black pixels to those edges, while numbers greater than the original width and height for right and bottom will add black pixels to those edges.
This code accounts for odd pixel sizes:
from PIL import Image
with Image.open('/path/to/image.gif') as im:
old_size = im.size
new_size = (800, 800)
if new_size > old_size:
# Set number of pixels to expand to the left, top, right,
# and bottom, making sure to account for even or odd numbers
if old_size[0] % 2 == 0:
add_left = add_right = (new_size[0] - old_size[0]) // 2
else:
add_left = (new_size[0] - old_size[0]) // 2
add_right = ((new_size[0] - old_size[0]) // 2) + 1
if old_size[1] % 2 == 0:
add_top = add_bottom = (new_size[1] - old_size[1]) // 2
else:
add_top = (new_size[1] - old_size[1]) // 2
add_bottom = ((new_size[1] - old_size[1]) // 2) + 1
left = 0 - add_left
top = 0 - add_top
right = old_size[0] + add_right
bottom = old_size[1] + add_bottom
# By default, the added pixels are black
im = im.crop((left, top, right, bottom))
Instead of the 4-tuple, you could instead use a 2-tuple to add the same number of pixels on the left/right and top/bottom, or a 1-tuple to add the same number of pixels to all sides.
It is important to consider old dimension, new dimension and their difference here. If the difference is odd (not even), you will need to specify slightly different values for left, top, right and bottom borders.
Assume the old dimension is ow,oh and new one is nw,nh.
So, this would be the answer:
import Image, ImageOps
img = Image.open('original-image.png')
deltaw=nw-ow
deltah=nh-oh
ltrb_border=(deltaw/2,deltah/2,deltaw-(deltaw/2),deltah-(deltah/2))
img_with_border = ImageOps.expand(img,border=ltrb_border,fill='black')
img_with_border.save('imaged-with-border.png')
You can load the image with scipy.misc.imread as a numpy array. Then create an array with the desired background with numpy.zeros((height, width, channels)) and paste the image at the desired location:
import numpy as np
import scipy.misc
im = scipy.misc.imread('foo.jpg', mode='RGB')
height, width, channels = im.shape
# make canvas
im_bg = np.zeros((height, width, channels))
im_bg = (im_bg + 1) * 255 # e.g., make it white
# Your work: Compute where it should be
pad_left = ...
pad_top = ...
im_bg[pad_top:pad_top + height,
pad_left:pad_left + width,
:] = im
# im_bg is now the image with the background.
ximg = Image.open(qpath)
xwid,xhgt = func_ResizeImage(ximg)
qpanel_3 = tk.Frame(Body,width=xwid+10,height=xhgt+10,bg='white',bd=5)
ximg = ximg.resize((xwid,xhgt),Image.ANTIALIAS)
ximg = ImageTk.PhotoImage(ximg)
panel = tk.Label(qpanel_3,image=ximg)
panel.image = ximg
panel.grid(row = 2)
from PIL import Image
from PIL import ImageOps
img = Image.open("dem.jpg").convert("RGB")
This part will add black borders at the sides (10% of width)
img_side = ImageOps.expand(img, border=(int(0.1*img.size[0]),0,int(0.1*img.size[0]),0), fill=(0,0,0))
img_side.save("sunset-sides.jpg")
This part will add black borders at the bottom & top (10% of height)
img_updown = ImageOps.expand(img, border=(0,int(0.1*img.size[1]),0,int(0.1*img.size[1])), fill=(0,0,0))
img_updown.save("sunset-top_bottom.jpg")
This part will add black borders at the bottom,top & sides (10% of height-width)
img_updown_side = ImageOps.expand(img, border=(int(0.1*img.size[0]),int(0.1*img.size[1]),int(0.1*img.size[0]),int(0.1*img.size[1])), fill=(0,0,0))
img_updown_side.save("sunset-all_sides.jpg")
img.close()
img_side.close()
img_updown.close()
img_updown_side.close()
I know there is a thumbnail method in PIL. What I want to make differently is how it resizes the original image. Assume I have a 300x360px vertical image. I want to resize it to a constrained box that is 150x100px horizontal image. So I need to find the smallest side of the original image, resize to it, and then crop the rest to the center from the largest side.
How can I do it?
from PIL import Image
width = 150
height = 100
infile = Image.open(in_filename)
im = infile.copy()
if im.size[0] >= im.size[1]:
im = im.resize((height * im.size[0]/im.size[1], height))
im = im.crop(((im.size[0] - width)/2, 0, (im.size[0] + width)/2, height))
else:
im = im.resize((width, width * im.size[1]/im.size[0]))
im = im.crop((0, (im.size[1] - height)/2, width, (im.size[1] + height)/2))
im.save(out_filename)
There might be a faster way to do this, but this should work.
I'm using PIL.
im = im.rotate(angle=-90, expand = True)
When I do this, it adds a greyish border to my image.
Why?
Here's m full code. Note that if I don't rotate, it adds no borders
def fixRotation(f, quality=96, image_type="JPEG"):
#http://sylvana.net/jpegcrop/exif_orientation.html
d =getEXIF(f)
if d:
orientation = int(d['Orientation'])
im = Image.open(StringIO(f))
if orientation == 6:
im = im.rotate(angle=-90, expand = True)
elif orientation == 3:
im = im.rotate(angle=-180, expand=True)
elif orientation == 8:
im = im.rotate(angle=-270, expand=True)
else:
#It doesn't add a border here.
im = im.rotate(0, expand=True)
res = StringIO()
im.save(res, image_type, quality=quality)
res.seek(0)
return res
else:
return StringIO(f)
I made some experiment and indeed image size is altered but I didn't understad the exact behavior. To me looks like a bug in PIL... you should report it.
If you only need k*90 degrees then to do the rotation you can also use numpy...
img = Image.fromarray(numpy.rot90(numpy.array(img), n))
n is the number of times to rotate by 90 degrees.
Note the following (CPython 2.6, PIL 1.1.7):
import Image
i= Image.open("someimage.jpg")
ir0= i.rotate(-90)
ir1= i.rotate(-90, expand=1)
for img in i, ir0, ir1:
print(img.size)
# output follows
(720, 400)
(400, 720)
(401, 721)
When the angle is a multiple of 90, expand is not needed.
expand in the case of right-angle multiples seems to activate a bug
So, if all you care about is 90°-180°-270° rotations, just drop the expand=1 argument; even better, use the transpose method (see Geometrical Transforms in the PIL tutorial )
When rotating, the algorithm (essentially) averages each pixel with the "next" pixel value. For all pixels "inside" the image, the next pixel is defined. For all pixels at the edge of the image, that pixel is undefined.
So the grey is the average between the known perimeter pixel and an undefined exterior pixel.