def do_option(self, transmission_array, transmission_array_name,
result_queue):
if OPTION != Options.GET_HISTOGRAM_WITH_CLOSE_RGB:
return
haze_arr = option_get_haze_array_with_transmission_name(
transmission_array_name)
_, alpha, _ = dt.trans_get_alpha_beta(transmission_array_name)
pq = queue.PriorityQueue()
shape = np.shape(haze_arr)
H = shape[0]
W = shape[1]
expected_number = 0
for h in range(H):
for w in range(W):
if abs(haze_arr[h][w][0] - haze_arr[h][w][1]) <= constant.OPTIONS_THRESHOLD and abs(
haze_arr[h][w][1] - haze_arr[h][w][2]) <= constant.OPTIONS_THRESHOLD and abs(
haze_arr[h][w][0] - haze_arr[h][w][2]) \
<= constant.OPTIONS_THRESHOLD:
if haze_arr[h][w][0] >= constant.OPTIONS_LOWER_BOUNDARY:
# pq.put((haze_arr[h][w][0] + haze_arr[h][w][1] + haze_arr[h][w][2]) / 3)
pq.put(haze_arr[h][w][0])
if transmission_array[h][
w] < constant.OPTIONS_TRANSMISSION_THRESHOLD:
expected_number += 1
result_queue.put((alpha, pq, expected_number, transmission_array_name))
def run(self):
while True:
# Read clear image, result image and their corresponding transmission image.
# Get the three corresponding matrices for a single image.
result_image_name = self.result_queue.get()
if result_image_name is None:
RESULT_IMAGE_QUEUE.put(None)
self.task_queue.put(None)
break
result_single_dir = os.path.join(constant.STATS_RESULT_DIR,
result_image_name)
clear_index = result_image_name[0:constant.STATS_CLEAR_INDEX_BIT]
trans_index = result_image_name[0:constant.STATS_TRANS_INDEX_BIT]
if clear_index not in constant.STATS_CLEAR_DICTIONARY:
raise RuntimeError(result_image_name +
' cannot find corresponding clear image.')
clear_single_dir = constant.STATS_CLEAR_DICTIONARY[clear_index]
if trans_index not in constant.STATS_TRANSMISSION_DICTIONARY:
raise RuntimeError(
result_image_name +
' cannot find corresponding transmission image.')
transmission_single_dir = constant.STATS_TRANSMISSION_DICTIONARY[
trans_index]
clear, result, transmission = sta_read_image(
clear_single_dir, result_single_dir, transmission_single_dir)
[_, filename] = os.path.split(transmission_single_dir)
_, alpha, beta = dehazenet_transmission.trans_get_alpha_beta(
filename)
current_task = ImageTask(clear, result, transmission, alpha, beta)
self.task_queue.put(current_task)
print('Statistic Producer finish')
def do_option(self, transmission_array, transmission_name, result_queue):
if OPTION != Options.GET_ESTIMATE_ALPHA:
return
_, alpha, _ = dt.trans_get_alpha_beta(transmission_name)
haze_arr = option_get_haze_array_with_transmission_name(
transmission_name)
intermediate_map = OptionMapFactory(MAP_OPTION).get_map(haze_arr)
# attenuation_depth_map = OptionGetEstimateAlpha.__option_get_depth_color_attenuation(haze_arr)
OptionGetEstimateAlpha.__estimate_alpha_with_map(
intermediate_map, haze_arr, alpha, result_queue)
def do_option(self, transmission_array, transmission_name, result_queue):
if OPTION != Options.DEHAZE_WITH_TRANSMISSION_MAP:
return
haze_arr = option_get_haze_array_with_transmission_name(
transmission_name)
# Get alpha from name
_, alpha, _ = dt.trans_get_alpha_beta(transmission_name)
do.opt_write_result_to_file(
do.opt_dehaze_with_alpha_transmission(alpha, transmission_array,
haze_arr))
def do_option(self, transmission_array, transmission_array_name,
result_queue):
if OPTION != Options.GET_TRANSMISSION_HISTOGRAM:
return
_, alpha, _ = dt.trans_get_alpha_beta(transmission_array_name)
shape = np.shape(transmission_array)
H = shape[0]
W = shape[1]
expected_number = 0
pq = queue.PriorityQueue()
for h in range(H):
for w in range(W):
pq.put(transmission_array[h][w])
if transmission_array[h][
w] < constant.OPTIONS_TRANSMISSION_THRESHOLD:
expected_number += 1
result_queue.put((alpha, pq, expected_number, transmission_array_name))
def opt_get_alpha(image_index):
hazy_image_fullname = CLEAR_IMAGE_DICTIONARY[image_index]
_, image_alpha, _ = dt.trans_get_alpha_beta(hazy_image_fullname)
return hazy_image_fullname, image_alpha