caffe2_tutorials中的图像预处理代码

需要学习的是：

1.将HWC转变为CHW 。

如果我们使用的是GPU，就需要将图片的维度转变为CHW，因为这是cuDNN所期望的维度顺序，这样可以加速网络训练的过程。

# switch to CHW (HWC --> CHW)
img = img.swapaxes(1, 2).swapaxes(0, 1)

 

2.将RGB转变为BGR。

因为Caffe中支持的图片通道顺序是BGR，而不是我们通常所知道的RGB。（OpenCV中也是支持BGR）

# switch to BGR (RGB --> BGR)
img = img[(2, 1, 0), :, :]

 

3.增加一个Batch维度

# add batch size axis which completes the formation of the NCHW shaped input that we want
img = img[np.newaxis, :, :, :].astype(np.float32)

 

# Function to crop the center cropX x cropY pixels from the input image
def crop_center(img,cropx,cropy):
    y,x,c = img.shape
    startx = x//2-(cropx//2)
    starty = y//2-(cropy//2)    
    return img[starty:starty+cropy,startx:startx+cropx]

# Function to rescale the input image to the desired height and/or width. This function will preserve
#   the aspect ratio of the original image while making the image the correct scale so we can retrieve
#   a good center crop. This function is best used with center crop to resize any size input images into
#   specific sized images that our model can use.
def rescale(img, input_height, input_width):
    # Get original aspect ratio
    aspect = img.shape[1]/float(img.shape[0])
    if(aspect>1):
        # landscape orientation - wide image
        res = int(aspect * input_height)
        imgScaled = skimage.transform.resize(img, (input_width, res))
    if(aspect<1):
        # portrait orientation - tall image
        res = int(input_width/aspect)
        imgScaled = skimage.transform.resize(img, (res, input_height))
    if(aspect == 1):
        imgScaled = skimage.transform.resize(img, (input_width, input_height))
    return imgScaled

# Load the image as a 32-bit float
#    Note: skimage.io.imread returns a HWC ordered RGB image of some size
img = skimage.img_as_float(skimage.io.imread(IMAGE_LOCATION)).astype(np.float32)
print("Original Image Shape: " , img.shape)

# Rescale the image to comply with our desired input size. This will not make the image 227x227
#    but it will make either the height or width 227 so we can get the ideal center crop.
img = rescale(img, INPUT_IMAGE_SIZE, INPUT_IMAGE_SIZE)
print("Image Shape after rescaling: " , img.shape)
pyplot.figure()
pyplot.imshow(img)
pyplot.title('Rescaled image')

# Crop the center 227x227 pixels of the image so we can feed it to our model
img = crop_center(img, INPUT_IMAGE_SIZE, INPUT_IMAGE_SIZE)
print("Image Shape after cropping: " , img.shape)
pyplot.figure()
pyplot.imshow(img)
pyplot.title('Center Cropped')

# switch to CHW (HWC --> CHW)
img = img.swapaxes(1, 2).swapaxes(0, 1)
print("CHW Image Shape: " , img.shape)

pyplot.figure()
for i in range(3):
    # For some reason, pyplot subplot follows Matlab's indexing
    # convention (starting with 1). Well, we'll just follow it...
    pyplot.subplot(1, 3, i+1)
    pyplot.imshow(img[i])
    pyplot.axis('off')
    pyplot.title('RGB channel %d' % (i+1))

# switch to BGR (RGB --> BGR)
img = img[(2, 1, 0), :, :]

# remove mean for better results
img = img * 255 - mean

# add batch size axis which completes the formation of the NCHW shaped input that we want
img = img[np.newaxis, :, :, :].astype(np.float32)

Reference

https://caffe2.ai/docs/tutorial-image-pre-processing.html