Basic image data manipulation is introduced, using OpenCV library. The sample image is obtained from PEXELS.
OpenCV is image processing library which supports
resize
and other useful image processing functionality.
To install opencv, execute
$conda install -c https://conda.binstar.org/menpo -y opencv3
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import os
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
def readRGBImage(imagepath):
image = cv2.imread(imagepath) # Height, Width, Channel
(major, minor, _) = cv2.__version__.split(".")
if major == '3':
# version 3 is used, need to convert
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
else:
# Version 2 is used, not necessary to convert
pass
return image
cv2.imread
for loading image.cv2.imwrite
for save image.plt.imshow
for plotting, and plt.savefig
for save plot image.OpenCV image format is usually 3 dimension (or 2 dimension if the image is gray scale).
1st dimension is for height, 2nd dimension is for width, 3rd dimension is for channel (RGB, YUV etc).
To convert color format cv2.cvtColor
can be used.
Details are written in next section.
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# Read image from file, save image with matplotlib using `imshow` function
basedir = './src/cnn/images'
imagepath = os.path.join(basedir, 'sample.jpeg')
#image = cv2.imread(imagepath, cv2.IMREAD_GRAYSCALE)
image = readRGBImage(imagepath)
# Width and Height shows pixel size of this image
# Channel=3 indicates the RGB channel
print('image.shape (Height, Width, Channel) = ', image.shape)
# Save image with openCV
# This may be blue image because the color format RGB is opposite.
cv2.imwrite('./src/cnn/images/out.jpg', image)
# bgr_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# cv2.imwrite('./src/cnn/images/out.jpg', bgr_image)
# Plotting
plt.imshow(image)
plt.savefig('./src/cnn/images/out_plt.jpg')
cv2.cvtColor
for converting color format.Note that openCV version 3 reads the image color in the order B, G, R.
However, matplotlib deals with the image color in the corder R, G, B.
So you need to convert color order, refer readRGBImage
function.
If the image is gray scale, the image is 2 dimensional array
1st dimension is for height, 2nd dimension is for width.
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gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Gray scale image is 2 dimension, No channel dimension.
print('gray_image.shape (Height, Width) = ', gray_image.shape)
cv2.imwrite('./src/cnn/images/out_gray.jpg', gray_image)
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%matplotlib inline
print('image.shape (Height, Width, Channel) = ', image.shape)
# Resize image to half size
height, width = image.shape[:2]
half_image = cv2.resize(image, (width//2, height//2)) # size must be int
print('half_image.shape (Height, Width, Channel) = ', half_image.shape)
plt.imshow(half_image)
plt.savefig('./src/cnn/images/out_half.jpg')
# Resize image by specifying longer side size
def resize_longedge(image, pixel):
"""Resize the input image
Longer edge size will be `pixel`, and aspect ratio doesn't change
"""
height, width = image.shape[:2]
longer_side = max(height, width)
ratio = float(pixel) / longer_side
return cv2.resize(image, None, fx=ratio, fy=ratio) # size must be int
resized128_image = resize_longedge(image, 128)
print('resized128_image.shape (Height, Width, Channel) = ', resized128_image.shape)
plt.imshow(resized128_image)
plt.savefig('./src/cnn/images/out_resized128.jpg')
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%matplotlib inline
# Crop center of half_image
height, width = half_image.shape[:2]
crop_length = min(height, width)
height_start = (height - crop_length) // 2
width_start = (width - crop_length) // 2
cropped_image = half_image[
height_start:height_start+crop_length,
width_start:width_start+crop_length,
:]
print('cropped_image.shape (Height, Width, Channel) = ', cropped_image.shape)
plt.imshow(cropped_image)
plt.savefig('./src/cnn/images/out_cropped.jpg')
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%matplotlib inline
# Show RGB channel separately in gray scale
fig, axes = plt.subplots(1, 3)
# image[:, :, 0] is R channel.
axes[0].set_title('R channel')
axes[0].imshow(image[:, :, 0], cmap='gray')
# image[:, :, 1] is G channel.
axes[1].set_title('G channel')
axes[1].imshow(image[:, :, 1], cmap='gray')
# image[:, :, 2] is B channel.
axes[2].set_title('B channel')
axes[2].imshow(image[:, :, 2], cmap='gray')
plt.savefig(os.path.join(basedir, 'RGB_gray.jpg'))
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# Show RGB channel separately in color
fig, axes = plt.subplots(1, 3)
# image[:, :, 0] is R channel, replace the rest by 0.
imageR = image.copy()
imageR[:, :, 1:3] = 0
axes[0].set_title('R channel')
axes[0].imshow(imageR)
# image[:, :, 1] is G channel, replace the rest by 0.
imageG = image.copy()
imageG[:, :, [0, 2]] = 0
axes[1].set_title('G channel')
axes[1].imshow(imageG)
# image[:, :, 2] is B channel, replace the rest by 0.
imageB = image.copy()
imageB[:, :, 0:2] = 0
axes[2].set_title('B channel')
axes[2].imshow(imageB)
plt.savefig(os.path.join(basedir, 'RGB_color.jpg'))
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