def gray2rgb(input_image,
with_color=True,
cmap='jet',
warning=True,
debug=True):
'''
convert a grayscale image (1-channel) to a rgb image
parameters:
input_image: an pil or numpy image
with_color: add false colormap
output:
rgb_image: an uint8 rgb numpy image
'''
np_image, _ = safe_image(input_image, warning=warning, debug=debug)
if isfloatimage(np_image): np_image = (np_image * 255.).astype('uint8')
if debug:
assert isgrayimage_dimension(
np_image), 'the input numpy image is not correct: {}'.format(
np_image.shape)
assert isuintimage(
np_image
), 'the input numpy image should be uint8 image in order to use opencv'
if with_color:
if cmap == 'jet':
rgb_image = cv2.applyColorMap(np_image, cv2.COLORMAP_JET)
else:
assert False, 'cmap %s is not supported' % cmap
else:
rgb_image = cv2.cvtColor(np_image, cv2.COLOR_GRAY2RGB)
return rgb_image
def image_clahe(input_image,
clip_limit=2.0,
grid_size=8,
warning=True,
debug=True):
'''
do contrast limited adative histogram equalization for an image: could be a color image or gray image
the color space used for histogram equalization is LAB
parameters:
input_image: a pil or numpy image
outputs:
clahe_image: an uint8 numpy image (rgb or gray)
'''
np_image, _ = safe_image(input_image, warning=warning, debug=debug)
if isfloatimage(np_image):
np_image = (np_image.astype('float32') * 255.).astype('uint8')
if debug:
assert isuintimage(np_image), 'the input image should be a uint8 image'
clahe = cv2.createCLAHE(clipLimit=clip_limit,
tileGridSize=(grid_size, grid_size))
if iscolorimage_dimension(np_image):
lab_image = rgb2lab(np_image, warning=warning, debug=debug)
input_data = lab_image[:, :, 0] # extract the value channel
clahe_lab_image = clahe.apply(input_data)
lab_image[:, :, 0] = clahe_lab_image
clahe_image = lab2rgb(lab_image, warning=warning, debug=debug)
elif isgrayimage_dimension(np_image):
clahe_image = clahe.apply(np_image)
else:
assert False, 'the input image is neither a color image or a grayscale image'
return clahe_image
def image_hist_equalization_hsv(input_image, warning=True, debug=True):
'''
do histogram equalization for an image: could be a color image or gray image
the color space used for histogram equalization is HSV
parameters:
input_image: a pil or numpy image
outputs:
equalized_image: an uint8 numpy image (rgb or gray)
'''
np_image, _ = safe_image(input_image, warning=warning, debug=debug)
if isuintimage(np_image): np_image = np_image.astype('float32') / 255.
if debug:
assert isfloatimage(
np_image), 'the input image should be a float image'
if iscolorimage_dimension(np_image):
hsv_image = rgb2hsv(np_image, warning=warning, debug=debug)
input_data = hsv_image[:, :, 2] # extract the value channel
equalized_hsv_image = (
hist_equalization(input_data, num_bins=256, debug=debug) *
255.).astype('uint8')
hsv_image[:, :, 2] = equalized_hsv_image
equalized_image = hsv2rgb(hsv_image, warning=warning, debug=debug)
elif isgrayimage_dimension(np_image):
equalized_image = (
hist_equalization(np_image, num_bins=256, debug=debug) *
255.).astype('uint8')
else:
assert False, 'the input image is neither a color image or a grayscale image'
return equalized_image
def convolve(self, input_image):
'''
convolve the kernel with the input image, whatever the input image format is. If the input image
is a color image, the filter is expanded to a 3D shape
parameters:
input_image: an pil or numpy, gray or color image
outputs:
filtered_image: a float32 numpy image, shape is same as before
'''
np_image, _ = safe_image(input_image,
warning=self.warning,
debug=self.debug)
if isuintimage(np_image): np_image = np_image.astype('float32') / 255.
if self.debug:
assert isfloatimage(
np_image), 'the input image should be a float image'
self.weights is not None, 'the kernel is not defined yet'
if iscolorimage_dimension(np_image):
self.weights = self.expand_3d(
) # expand the filter to 3D for color image
elif isgrayimage_dimension(np_image):
np_image = np_image.reshape(
np_image.shape[0],
np_image.shape[1]) # squeeze the image dimension to 2
filtered_image = ndimage.filters.convolve(np_image, self.weights)
return filtered_image
def test_gray2rgb():
image_path = '../lena.jpg'
img = Image.open(image_path).convert('L')
img = np.array(img)
print('input grayscale image has dimension of: '),
print(img.shape)
assert isgrayimage_dimension(img), 'the input image is not a gray image'
visualize_image(img, vis=True)
img_rgb = gray2rgb(img, with_color=True)
print('converted rgb image has dimension of: '),
print(img_rgb.shape)
assert iscolorimage_dimension(
img_rgb), 'the converted image is not a color image'
visualize_image(img_rgb, vis=True)
# test when input image is float image
test_floatimage = (img.astype('float32')) / 255.
img_rgb = gray2rgb(test_floatimage, with_color=True)
assert iscolorimage_dimension(
img_rgb), 'the converted image is not a color image'
visualize_image(img_rgb, vis=True)
# test when input image is PIL image
test_pil_format_image = Image.fromarray(img)
img_rgb = gray2rgb(test_pil_format_image, with_color=True)
assert iscolorimage_dimension(
img_rgb), 'the converted image is not a color image'
print('\n\nDONE! SUCCESSFUL!!\n')
def visualize_image(input_image, bgr2rgb=False, save_path=None, vis=False, warning=True, debug=True, closefig=True): ''' visualize an image parameters: input_image: a pil or numpy image bgr2rgb: true if the image needs to be converted from bgr to rgb save_path: a path to save. Do not save if it is None closefig: False if you want to add more elements on the image outputs: fig, ax: figure handle for future use ''' np_image, _ = safe_image(input_image, warning=warning, debug=debug) width, height = np_image.shape[1], np_image.shape[0] fig, ax = get_fig_ax_helper(fig=None, ax=None, width=width, height=height, frameon=False) ax = fig.add_axes([0, 0, 1, 1]) ax.set_axis_off() fig.axes[0].get_xaxis().set_visible(False) fig.axes[0].get_yaxis().set_visible(False) fig.axes[1].get_xaxis().set_visible(False) fig.axes[1].get_yaxis().set_visible(False) # display image if iscolorimage_dimension(np_image): if bgr2rgb: np_image = image_bgr2rgb(np_image) ax.imshow(np_image, interpolation='nearest') elif isgrayimage_dimension(np_image): np_image = np_image.reshape(np_image.shape[0], np_image.shape[1]) ax.imshow(np_image, interpolation='nearest', cmap='gray') else: assert False, 'unknown image type' ax.set(xlim=[0, width], ylim=[height, 0], aspect=1) return save_vis_close_helper(fig=fig, ax=ax, vis=vis, save_path=save_path, debug=debug, warning=warning, closefig=closefig)
def visualize_nearest_neighbor(featuremap_dict,
num_neighbor=5,
top_number=5,
vis=True,
save_csv=False,
csv_save_path=None,
save_vis=False,
save_img=False,
save_thumb_name='nearest_neighbor.png',
img_src_folder=None,
ext_filter='.jpg',
nn_save_folder=None,
debug=True):
'''
visualize nearest neighbor for featuremap from images
parameter:
featuremap_dict: a dictionary contains image path as key, and featuremap as value, the featuremap needs to be numpy array with any shape. No flatten needed
num_neighbor: number of neighbor to visualize, the first nearest is itself
top_number: number of top to visualize, since there might be tons of featuremap (length of dictionary), we choose the top ten with lowest distance with their nearest neighbor
csv_save_path: path to save .csv file which contains indices and distance array for all elements
nn_save_folder: save the nearest neighbor images for top featuremap
return:
all_sorted_nearest_id: a 2d matrix, each row is a feature followed by its nearest neighbor in whole feature dataset, the column is sorted by the distance of all nearest neighbor each row
selected_nearest_id: only top number of sorted nearest id
'''
print('processing feature map to nearest neightbor.......')
if debug:
assert isdict(featuremap_dict), 'featuremap should be dictionary'
assert all(
isnparray(featuremap_tmp) for featuremap_tmp in featuremap_dict.
values()), 'value of dictionary should be numpy array'
assert isinteger(
num_neighbor
) and num_neighbor > 1, 'number of neighborhodd is an integer larger than 1'
if save_csv and csv_save_path is not None:
assert is_path_exists_or_creatable(
csv_save_path), 'path to save .csv file is not correct'
if save_vis or save_img:
if nn_save_folder is not None: # save image directly
assert isstring(ext_filter), 'extension filter is not correct'
assert is_path_exists(
img_src_folder), 'source folder for image is not correct'
assert all(
isstring(path_tmp) for path_tmp in featuremap_dict.keys()
) # key should be the path for the image
assert is_path_exists_or_creatable(
nn_save_folder
), 'folder to save top visualized images is not correct'
assert isstring(
save_thumb_name), 'name of thumbnail is not correct'
if ext_filter.find('.') == -1:
ext_filter = '.%s' % ext_filter
# flatten the feature map
nn_feature_dict = dict()
for key, featuremap_tmp in featuremap_dict.items():
nn_feature_dict[key] = featuremap_tmp.flatten()
num_features = len(nn_feature_dict)
# nearest neighbor
featuremap = np.array(nn_feature_dict.values())
nearbrs = NearestNeighbors(n_neighbors=num_neighbor,
algorithm='ball_tree').fit(featuremap)
distances, indices = nearbrs.kneighbors(featuremap)
if debug:
assert featuremap.shape[
0] == num_features, 'shape of feature map is not correct'
assert indices.shape == (
num_features, num_neighbor), 'shape of indices is not correct'
assert distances.shape == (
num_features, num_neighbor), 'shape of indices is not correct'
# convert the nearest indices for all featuremap to the key accordingly
id_list = nn_feature_dict.keys()
max_length = len(max(
id_list, key=len)) # find the maximum length of string in the key
nearest_id = np.chararray(indices.shape, itemsize=max_length + 1)
for x in range(nearest_id.shape[0]):
for y in range(nearest_id.shape[1]):
nearest_id[x, y] = id_list[indices[x, y]]
if debug:
assert list(nearest_id[:,
0]) == id_list, 'nearest neighbor has problem'
# sort the feature based on distance
print('sorting the feature based on distance')
featuremap_distance = np.sum(distances, axis=1)
if debug:
assert featuremap_distance.shape == (
num_features, ), 'distance is not correct'
sorted_indices = np.argsort(featuremap_distance)
all_sorted_nearest_id = nearest_id[sorted_indices, :]
# save to the csv file
if save_csv and csv_save_path is not None:
print('Saving nearest neighbor result as .csv to path: %s' %
csv_save_path)
with open(csv_save_path, 'w+') as file:
np.savetxt(file, distances, delimiter=',', fmt='%f')
np.savetxt(file, all_sorted_nearest_id, delimiter=',', fmt='%s')
file.close()
# choose the best to visualize
selected_sorted_indices = sorted_indices[0:top_number]
if debug:
for i in range(num_features - 1):
assert featuremap_distance[
sorted_indices[i]] < featuremap_distance[sorted_indices[
i + 1]], 'feature map is not well sorted based on distance'
selected_nearest_id = nearest_id[selected_sorted_indices, :]
if save_vis:
fig, axarray = plt.subplots(top_number, num_neighbor)
for index in range(top_number):
for nearest_index in range(num_neighbor):
img_path = os.path.join(
img_src_folder, '%s%s' %
(selected_nearest_id[index, nearest_index], ext_filter))
if debug:
print('loading image from %s' % img_path)
img = imread(img_path)
if isgrayimage_dimension(img):
axarray[index, nearest_index].imshow(img, cmap='gray')
elif iscolorimage_dimension(img):
axarray[index, nearest_index].imshow(img)
else:
assert False, 'unknown error'
axarray[index, nearest_index].axis('off')
save_thumb = os.path.join(nn_save_folder, save_thumb_name)
fig.savefig(save_thumb)
if vis:
plt.show()
plt.close(fig)
# save top visualization to the folder
if save_img and nn_save_folder is not None:
for top_index in range(top_number):
file_list = selected_nearest_id[top_index]
save_subfolder = os.path.join(nn_save_folder, file_list[0])
mkdir_if_missing(save_subfolder)
for file_tmp in file_list:
file_src = os.path.join(img_src_folder,
'%s%s' % (file_tmp, ext_filter))
save_path = os.path.join(save_subfolder,
'%s%s' % (file_tmp, ext_filter))
if debug:
print('saving %s to %s' % (file_src, save_path))
shutil.copyfile(file_src, save_path)
return all_sorted_nearest_id, selected_nearest_id
def image_find_peaks(input_image,
percent_threshold=0.5,
warning=True,
debug=True):
'''
this function find all strict local peaks and a strict global peak from a grayscale image
the strict local maximum means that the pixel value must be larger than all nearby pixel values
parameters:
input_image: a pil or numpy grayscale image
percent_threshold: determine to what pixel value to be smoothed out.
e.g., when 0.4, all pixel values less than 0.4 * np.max(input_image) are smoothed out to be 0
outputs:
peak_array: a numpy float32 array, 3 x num_peaks, (x, y, score)
peak_global: a numpy float32 array, 3 x 1: (x, y, score)
'''
np_image, _ = safe_image_like(input_image, warning=warning, debug=debug)
if isuintimage(np_image): np_image = np_image.astype('float32') / 255.
if debug:
assert isgrayimage_dimension(np_image) and isfloatimage(
np_image), 'the input image is not a grayscale and float image'
assert isscalar(
percent_threshold
) and percent_threshold >= 0 and percent_threshold <= 1, 'the percent_threshold is not correct'
max_value = np.max(np_image)
np_image[np_image < percent_threshold * max_value] = 0.0
height, width = np_image.shape[0], np_image.shape[1]
npimage_center, npimage_top, npimage_bottom, npimage_left, npimage_right = np.zeros(
[height + 2, width + 2]), np.zeros([height + 2, width + 2]), np.zeros([
height + 2, width + 2
]), np.zeros([height + 2,
width + 2]), np.zeros([height + 2, width + 2])
# shift in different directions to find local peak, only works for convex blob
npimage_center[1:-1, 1:-1] = np_image
npimage_left[1:-1, 0:-2] = np_image
npimage_right[1:-1, 2:] = np_image
npimage_top[0:-2, 1:-1] = np_image
npimage_bottom[2:, 1:-1] = np_image
# compute pixels larger than its shifted version of heatmap
right_bool = npimage_center > npimage_right
left_bool = npimage_center > npimage_left
bottom_bool = npimage_center > npimage_bottom
top_bool = npimage_center > npimage_top
# the strict local maximum must be bigger than all nearby pixel values
peakMap = np.logical_and(
np.logical_and(np.logical_and(right_bool, left_bool), top_bool),
bottom_bool)
peakMap = peakMap[1:-1, 1:-1]
peak_location_tuple = np.nonzero(peakMap) # find true
num_peaks = len(peak_location_tuple[0])
if num_peaks == 0:
if warning: print('No single local peak found')
return np.zeros((3, 0), dtype='float32'), np.zeros((3, 0),
dtype='float32')
# convert to a numpy array format
peak_array = np.zeros((3, num_peaks), dtype='float32')
peak_array[0, :], peak_array[
1, :] = peak_location_tuple[1], peak_location_tuple[0]
for peak_index in range(num_peaks):
peak_array[2, peak_index] = np_image[int(peak_array[1, peak_index]),
int(peak_array[0, peak_index])]
# find the global peak from all local peaks
global_peak_index = np.argmax(peak_array[2, :])
peak_global = peak_array[:, global_peak_index].reshape((3, 1))
return peak_array, peak_global
