def test_tf_get_all_colors_without_black(self):
# 1) Get sample image as tf
test_file_name: str = "lenna.png"
test_image_fullpath: str = os.path.join(
self.test_path, self.test_resource_folder_name, test_file_name
)
tf_image = tf_images.decode_png(test_image_fullpath, 3)
# 2) Calculate colors in image
tf_image_colors = tf_images.tf_get_all_colors(tf_image, black_first=False)
self.assertTrue(
tf.math.reduce_all(tf.math.not_equal(tf_image_colors[0], [0, 0, 0]))
)
def test_decode_image(self):
# Expected Result
sample_image_shape = [180, 180, 3]
result = tf.constant(sample_image_shape)
# 1) Get sample image as tf
test_file_name: str = "sample.png"
test_image_fullpath: str = os.path.join(
self.test_path, self.test_resource_folder_name, test_file_name
)
tf_image = tf_images.decode_png(test_image_fullpath, 3)
# 2) Calculate shape
self.assertTrue(tf.math.reduce_all(tf.math.equal(tf.shape(tf_image), result)))
def test_tf_img_to_minmax(self):
# Expected Result
threshold = 127
min_maxed_unique_values = (255, 0)
number_of_black_color = 31760
# 1) Get sample image as tf
test_file_name: str = "sample.png"
test_image_fullpath: str = os.path.join(
self.test_path, self.test_resource_folder_name, test_file_name
)
grayscale_tf_image = tf_images.decode_png(test_image_fullpath, 1)
# 2) Calculate min max
min_maxed_grayscale_tf_image = tf_images.tf_img_to_minmax(
grayscale_tf_image, threshold, (0, 255)
)
# check unique
reshaped_min_maxed_grayscale_tf_image = tf.reshape(
min_maxed_grayscale_tf_image, (-1, 1)
)
unique_min_max_values = gen_array_ops.unique_v2(
reshaped_min_maxed_grayscale_tf_image, axis=[-2]
)[0]
self.assertTrue(
tf.math.reduce_all(
tf.math.equal(
unique_min_max_values,
tf.cast(
tf.expand_dims(tf.constant(min_maxed_unique_values), axis=-1),
tf.float32,
),
)
)
)
self.assertTrue(
tf.math.equal(
tf.math.count_nonzero(min_maxed_grayscale_tf_image),
number_of_black_color,
)
)
def test_tf_get_all_colors(self):
# Expected Result
sample_image_colors = [
[0.0, 0.0, 0.0],
[245.0, 245.0, 245.0],
[255.0, 145.0, 77.0],
[71.0, 71.0, 71.0],
[72.0, 72.0, 72.0],
]
result = tf.constant(sample_image_colors)
# 1) Get sample image as tf
test_file_name: str = "sample.png"
test_image_fullpath: str = os.path.join(
self.test_path, self.test_resource_folder_name, test_file_name
)
tf_image = tf_images.decode_png(test_image_fullpath, 3)
# 2) Calculate colors in image
tf_image_colors = tf_images.tf_get_all_colors(tf_image)
print(tf_image_colors)
self.assertTrue(tf.math.reduce_all(tf.math.equal(tf_image_colors, result)))
def test_tf_generate_color_map(self):
sample_indexes = [0.0, 1.0, 2.0, 3.0, 4.0]
sample_image_colors = [
[0.0, 0.0, 0.0],
[245.0, 245.0, 245.0],
[255.0, 145.0, 77.0],
[71.0, 71.0, 71.0],
[72.0, 72.0, 72.0],
]
result = tf.constant(sample_image_colors)
# 1) Get sample image as tf
test_file_name: str = "sample.png"
test_image_fullpath: str = os.path.join(
self.test_path, self.test_resource_folder_name, test_file_name
)
tf_image = tf_images.decode_png(test_image_fullpath, 3)
color_map = tf_images.tf_generate_color_map(tf_image)
print(color_map)
self.assertTrue(tf.math.reduce_all(tf.math.equal(color_map[0], sample_indexes)))
self.assertTrue(tf.math.reduce_all(tf.math.equal(color_map[1], result)))
def test_tf_get_sorted_unique_color(self):
sample_image_colors = [
[88.0, 18.0, 60.0],
[176.0, 67.0, 79.0],
[205.0, 96.0, 97.0],
[178.0, 69.0, 80.0],
[206.0, 100.0, 94.0],
]
result = tf.constant(sample_image_colors)
# 1) Get sample image as tf
test_file_name: str = "lenna.png"
test_image_fullpath: str = os.path.join(
self.test_path, self.test_resource_folder_name, test_file_name
)
tf_image = tf_images.decode_png(test_image_fullpath, 3)
self.assertTrue(
tf.math.reduce_all(
tf.math.equal(
tf_images.tf_get_sorted_unique_color(tf_image)[:5], result
)
)
)
def test_tf_image_detach_with_id_color_list(self):
# 1) Get sample image as tf
test_file_name: str = "sample.png"
test_image_fullpath: str = os.path.join(
self.test_path, self.test_resource_folder_name, test_file_name
)
tf_image = tf_images.decode_png(test_image_fullpath, 3)
# 2) ID Color list
# id_color_list = tf_images.tf_generate_random_color_map(
# tf_image, shuffle_exclude_first=1, bin_size=30
# )
id_color_list = tf_images.tf_generate_color_map(tf_image)
print(tf.shape(tf_image))
print(id_color_list)
print(
tf.shape(
tf_images.tf_image_detach_with_id_color_list(
tf_image, id_color_list=id_color_list, bin_num=30
)
)
)