def _featurize(self, img):
'''Featurizes the input image using VGG-16.
The image is resized to 224 x 224 internally, if necessary.
Args:
img: the input image
Returns:
the feature vector, a 1D array of length 4096
'''
if etai.is_gray(img):
img = etai.gray_to_rgb(img)
elif etai.has_alpha(img):
img = img[:, :, :3]
imgs = [etai.resize(img, 224, 224)]
return self.vgg16.evaluate(imgs, layer=self.vgg16.fc2l)[0]
def _perform_convolution(convolution_config):
'''Performs convolution of an input image with a kernel specified
by the configuration parameters, and writes the result to the
path specified by "filtered_image".
Args:
convolution_config: the configuration file for the module
'''
kernel_type = convolution_config.parameters.kernel_type
if kernel_type == "x_derivative":
kernel = _create_x_derivative_kernel()
elif kernel_type == "y_derivative":
kernel = _create_y_derivative_kernel()
elif kernel_type == "sobel_vertical":
kernel = _create_sobel_vertical_kernel()
elif kernel_type == "sobel_horizontal":
kernel = _create_sobel_horizontal_kernel()
else:
# this will be the Gaussian kernel
# (make sure to remove this comment!)
kernel = _create_gaussian_kernel(
convolution_config.parameters.gaussian_sigma)
for data in convolution_config.data:
in_img = etai.read(data.input_image)
if convolution_config.parameters.image_type == "grayscale":
in_img = etai.rgb_to_gray(in_img)
else:
# if the image should be a color image, convert the grayscale
# image to color (simply converts the image into a 3-channel
# image)
if etai.is_gray(in_img):
in_img = etai.gray_to_rgb(in_img)
if convolution_config.parameters.image_max_range == 1:
in_img = (in_img.astype(float)) / 255.0
filtered_image = _convolve(kernel, in_img)
if data.filtered_matrix:
etau.ensure_basedir(data.filtered_matrix)
np.savez(data.filtered_matrix, filtered_matrix=filtered_image)
if data.filtered_image:
etau.ensure_basedir(data.filtered_image)
etai.write(filtered_image, data.filtered_image)
def _visualize_corners(in_img, corner_locations):
'''Creates an image that shows the detected corners.
Args:
in_img: the original image
corner_locations: the locations of each detected corner in the
image, stored in an Nx2 matrix
Returns:
out_img: an image with the detected corners colored in red
'''
if etai.is_gray(in_img):
out_img = etai.gray_to_rgb(in_img)
else:
out_img = in_img.copy()
for i in range(corner_locations.shape[0]):
cv2.circle(out_img,
(int(corner_locations[i][0]), int(corner_locations[i][1])),
2, (255, 0, 0), -1)
return out_img