10 examples of 'python crop image' in Python

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def test_crop_zero(self):
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    im = Image.new("RGB", (0, 0), "white")
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    cropped = im.crop((0, 0, 0, 0))
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    self.assertEqual(cropped.size, (0, 0))
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    cropped = im.crop((10, 10, 20, 20))
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    self.assertEqual(cropped.size, (10, 10))
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    self.assertEqual(cropped.getdata()[0], (0, 0, 0))
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    im = Image.new("RGB", (0, 0))
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    cropped = im.crop((10, 10, 20, 20))
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    self.assertEqual(cropped.size, (10, 10))
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    self.assertEqual(cropped.getdata()[2], (0, 0, 0))

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def crop_image(self, image, x, y):
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    return image[y: y+self.target_rows, x: x+self.target_cols,...] if self.use_crop else image

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def crop(image, x, y, width, height):
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    return image.copy(x, y, width, height)

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@tests.test_if(not skip_slow_tests)
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def crop():
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    """Crops in-place."""
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    with Image(filename=asset('croptest.png')) as img:
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        with img.clone() as cropped:
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            assert cropped.size == img.size
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            cropped.crop(100, 100, 200, 200)
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            assert cropped.size == (100, 100)
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            with Color('#000') as black:
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                for row in cropped:
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                    for col in row:
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                        assert col == black
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        with img.clone() as cropped:
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            assert cropped.size == img.size
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            cropped.crop(100, 100, width=100, height=100)
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            assert cropped.size == (100, 100)
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        with img.clone() as cropped:
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            assert cropped.size == img.size
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            cropped.crop(left=150, bottom=150)
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            assert cropped.size == (150, 150)
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        with img.clone() as cropped:
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            assert cropped.size == img.size
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            cropped.crop(left=150, height=150)
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            assert cropped.size == (150, 150)
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        with img.clone() as cropped:
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            assert cropped.size == img.size
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            cropped.crop(-200, -200, -100, -100)
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            assert cropped.size == (100, 100)
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        with img.clone() as cropped:
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            assert cropped.size == img.size
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            cropped.crop(top=100, bottom=200)
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            assert cropped.size == (300, 100)
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        with raises(ValueError):
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            img.crop(0, 0, 500, 500)
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        with raises(ValueError):
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            img.crop(290, 290, 50, 50)
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        with raises(ValueError):
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            img.crop(290, 290, width=0, height=0)

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def crop_to(image_to_crop, reference_image):
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    """
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    Crops image to the size of a reference image. This function assumes that the relevant image is located in the center
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    and you want to crop away equal sizes on both the left and right as well on both the top and bottom.
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    :param image_to_crop
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    :param reference_image
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    :return: image cropped to the size of the reference image
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    """
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    reference_size = reference_image.size
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    current_size = image_to_crop.size
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    dx = current_size[0] - reference_size[0]
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    dy = current_size[1] - reference_size[1]
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    left = dx / 2
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    upper = dy / 2
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    right = dx / 2 + reference_size[0]
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    lower = dy / 2 + reference_size[1]
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    return image_to_crop.crop(
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        box=(
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            int(left),
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            int(upper),
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            int(right),
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            int(lower)))

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def get_image_crop(img, x, y, width, height, crop_size=224, padding=16):
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    """
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    Get the image crop for the object specified in the COCO annotations.
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    We crop in such a way that in the final resized image, there is `context padding` amount of image data around the object.
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    This is the same as is used in RCNN to allow for additional image context.
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    :param img: The image ndarray
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    :param x: The x coordinate for the start of the bounding box
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    :param y: The y coordinate for the start of the bounding box
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    :param width: The width of the bounding box
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    :param height: The height of the bounding box
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    :param crop_size: The final size of the cropped image. Needed to calculate the amount of context padding.
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    :param padding: The amount of context padding needed in the image.
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    :return:
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    """
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    # Scale used to compute the new bbox for the image such that there is surrounding context.
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    # The way it works is that we find what is the scaling factor between the crop and the crop without the padding
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    # (which would be the original tight bounding box).
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    # `crop_size` is the size of the crop with context padding.
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    # The denominator is the size of the crop if we applied the same transform with the original tight bounding box.
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    scale = crop_size / (crop_size - padding * 2)
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    # Calculate semi-width and semi-height
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    semi_width = width / 2
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    semi_height = height / 2
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    # Calculate the center of the crop
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    centerx = x + semi_width
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    centery = y + semi_height
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    img_width, img_height = img.size
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    # We get the crop using the semi- height and width from the center of the crop.
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    # The semi- height and width are scaled accordingly.
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    # We also ensure the numbers are valid
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    upper = max(0, centery - (semi_height * scale))
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    lower = min(img_height, centery + (semi_height * scale))
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    left = max(0, centerx - (semi_width * scale))
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    right = min(img_width, centerx + (semi_width * scale))
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    crop_img = img.crop((left, upper, right, lower))
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    if 0 in crop_img.size:
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        print(img.size)
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        print("lowx {0}\nlowy {1}\nhighx {2}\nhighy {3}".format(
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            left, upper, right, lower))
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    return crop_img

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def manual_crop(self, sides=(0, 0, 0, 0)):  # (top, right, bottom, left)
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    """
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    Relative sizes!
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    :param sides:
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    :return:
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    """
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    self._image = self._image.crop(sides)
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    return self

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def crop(image, x1, y1, x2, y2):
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    """
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    Crop image.
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    >>> import numpy as np
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    >>> image = np.reshape(np.arange(16, dtype='uint8'), (4, 4))
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    >>> crop(image, 1, 2, 5, 5)
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    array([[ 9, 10, 11],
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           [13, 14, 15]], dtype=uint8)
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    :param numpy array image: Numpy array.
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    :param int x1: x-coordinate of left upper corner of crop (inclusive)
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    :param int y1: y-coordinate of left upper corner of crop (inclusive)
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    :param int x2: x-coordinate of right lower corner of crop (exclusive)
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    :param int y2: y-coordinate of right lower corner of crop (exclusive)
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    :return: Cropped image
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    :rtype: numpy array
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    """
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    return image[y1:y2, x1:x2]

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def do_crop_transform(self,item,params):
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    item.image=item.image.crop(params['pixel_rect'])

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def crop(inputFile,inputSizeX,inputSizeY,outputFolder='.'):
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	'''Crops images into subimages of size 'inputSize'x'inputSize'''
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	x=0
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	y=0
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	img = Image.open(inputFile)
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	width, height = img.size
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	print width,height
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	while not ((x+inputSizeX>width) or (y+inputSizeY>height)):
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		img_small = img.crop((x,y,x+inputSizeX,y+inputSizeY))
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		saveFile=outputFolder+'/'+os.path.split(inputFile)[-1][:-4]+"___"+str(x)+'_'+str(y)+".png"
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		img_small.save(saveFile)
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		if x+2*inputSizeX>width:
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			x=0
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			y=y+inputSizeY
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		else:
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			x = x + inputSizeX
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	image_index=find_before_convert(os.path.split(inputFile)[-1],'.png')
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	with open(imagesizes,'a+') as f:
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		f.write(image_index+','+str(width)+','+str(height)+'''
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''')