caffe2：機械学習では画像データの下準備が重要(PARTⅡ)

# Model is expecting 224 x 224, so resize/crop needed.
# First, let's resize the image to 256*256
orig_h, orig_w, _ = img.shape
print("Original image's shape is {}x{}".format(orig_h, orig_w))
input_height, input_width = 224, 224
print("Model's input shape is {}x{}".format(input_height, input_width))
img256 = skimage.transform.resize(img, (256, 256))

# Plot original and resized images for comparison
f, axarr = pyplot.subplots(1,2)
axarr[0].imshow(img)
axarr[0].set_title("Original Image (" + str(orig_h) + "x" + str(orig_w) + ")")
axarr[0].axis('on')
axarr[1].imshow(img256)
axarr[1].axis('on')
axarr[1].set_title('Resized image to 256x256')
pyplot.tight_layout()

print("New image shape:" + str(img256.shape))

print("Original image shape:" + str(img.shape) + " and remember it should be in H, W, C!")
print("Model's input shape is {}x{}".format(input_height, input_width))
aspect = img.shape[1]/float(img.shape[0])
print("Orginal aspect ratio: " + str(aspect))
if(aspect>1):
    # landscape orientation - wide image
    res = int(aspect * input_height)
    imgScaled = skimage.transform.resize(img, (input_height, res))
if(aspect<1):
    # portrait orientation - tall image
    res = int(input_width/aspect)
    imgScaled = skimage.transform.resize(img, (res, input_width))
if(aspect == 1):
    imgScaled = skimage.transform.resize(img, (input_height, input_width))
pyplot.figure()
pyplot.imshow(imgScaled)
pyplot.axis('on')
pyplot.title('Rescaled image')
print("New image shape:" + str(imgScaled.shape) + " in HWC")

# Compare the images and cropping strategies
# Try a center crop on the original for giggles
print("Original image shape:" + str(img.shape) + " and remember it should be in H, W, C!")
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]
# yes, the function above should match resize and take a tuple...

pyplot.figure()
# Original image
imgCenter = crop_center(img,224,224)
pyplot.subplot(1,3,1)
pyplot.imshow(imgCenter)
pyplot.axis('on')
pyplot.title('Original')

# Now let's see what this does on the distorted image
img256Center = crop_center(img256,224,224)
pyplot.subplot(1,3,2)
pyplot.imshow(img256Center)
pyplot.axis('on')
pyplot.title('Squeezed')

# Scaled image
imgScaledCenter = crop_center(imgScaled,224,224)
pyplot.subplot(1,3,3)
pyplot.imshow(imgScaledCenter)
pyplot.axis('on')
pyplot.title('Scaled')

pyplot.tight_layout()

imgTiny = "images/Cellsx128.png"
imgTiny = skimage.img_as_float(skimage.io.imread(imgTiny)).astype(np.float32)
print("Original image shape: ", imgTiny.shape)
imgTiny224 = skimage.transform.resize(imgTiny, (224, 224))
print("Upscaled image shape: ", imgTiny224.shape)
# Plot original
pyplot.figure()
pyplot.subplot(1, 2, 1)
pyplot.imshow(imgTiny)
pyplot.axis('on')
pyplot.title('128x128')
# Plot upscaled
pyplot.subplot(1, 2, 2)
pyplot.imshow(imgTiny224)
pyplot.axis('on')
pyplot.title('224x224')

imgTiny = "images/Cellsx128.png"
imgTiny = skimage.img_as_float(skimage.io.imread(imgTiny)).astype(np.float32)
print("Image shape before HWC --> CHW conversion: ", imgTiny.shape)
# swapping the axes to go from HWC to CHW
# uncomment the next line and run this block!
imgTiny = imgTiny.swapaxes(1, 2).swapaxes(0, 1)
print("Image shape after HWC --> CHW conversion: ", imgTiny.shape)
imgTiny224 = skimage.transform.resize(imgTiny, (224, 224))
print("Image shape after resize: ", imgTiny224.shape)
# we know this is going to go wrong, so...
try:
    # Plot original
    pyplot.figure()
    pyplot.subplot(1, 2, 1)
    pyplot.imshow(imgTiny)
    pyplot.axis('on')
    pyplot.title('128x128')
except:
    print("Here come bad things!")
    # hands up if you want to see the error (uncomment next line)
    #raise 

imgTiny = "images/Cellsx128.png"
imgTiny = skimage.img_as_float(skimage.io.imread(imgTiny)).astype(np.float32)
imgTinySlice = crop_center(imgTiny, 128, 56)
# Plot original
pyplot.figure()
pyplot.subplot(2, 1, 1)
pyplot.imshow(imgTiny)
pyplot.axis('on')
pyplot.title('Original')
# Plot slice
pyplot.figure()
pyplot.subplot(2, 2, 1)
pyplot.imshow(imgTinySlice)
pyplot.axis('on')
pyplot.title('128x56')
# Upscale?
print("Slice image shape: ", imgTinySlice.shape)
imgTiny224 = skimage.transform.resize(imgTinySlice, (224, 224))
print("Upscaled slice image shape: ", imgTiny224.shape)
# Plot upscaled
pyplot.subplot(2, 2, 2)
pyplot.imshow(imgTiny224)
pyplot.axis('on')
pyplot.title('224x224')

# This next line helps with being able to rerun this section
# if you want to try the outputs of the different crop strategies above
# swap out imgScaled with img (original) or img256 (squeezed)
imgCropped = crop_center(imgScaled,224,224)
print("Image shape before HWC --> CHW conversion: ", imgCropped.shape)
# (1) Since Caffe expects CHW order and the current image is HWC,
#     we will need to change the order.
imgCropped = imgCropped.swapaxes(1, 2).swapaxes(0, 1)
print("Image shape after HWC --> CHW conversion: ", imgCropped.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(imgCropped[i], cmap=pyplot.cm.gray)
    pyplot.axis('off')
    pyplot.title('RGB channel %d' % (i+1))

# (2) Caffe uses a BGR order due to legacy OpenCV issues, so we
#     will change RGB to BGR.
imgCropped = imgCropped[(2, 1, 0), :, :]
print("Image shape after BGR conversion: ", imgCropped.shape)

# for discussion later - not helpful at this point
# (3) (Optional) We will subtract the mean image. Note that skimage loads
#     image in the [0, 1] range so we multiply the pixel values
#     first to get them into [0, 255].
#mean_file = os.path.join(CAFFE_ROOT, 'python/caffe/imagenet/ilsvrc_2012_mean.npy')
#mean = np.load(mean_file).mean(1).mean(1)
#img = img * 255 - mean[:, np.newaxis, np.newaxis]

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(imgCropped[i], cmap=pyplot.cm.gray)
    pyplot.axis('off')
    pyplot.title('BGR channel %d' % (i+1))
# (4) Finally, since caffe2 expect the input to have a batch term
#     so we can feed in multiple images, we will simply prepend a
#     batch dimension of size 1. Also, we will make sure image is
#     of type np.float32.
imgCropped = imgCropped[np.newaxis, :, :, :].astype(np.float32)
print('Final input shape is:', imgCropped.shape)