Variables | |
| int | all_cropped_num = len(os.listdir(train_cropped_images_path))//3 |
| callbacks | |
| int | channels = 2 |
| col | |
| col_end = col+cropped_w | |
| compiled_model = model | |
| list | config_list = [(noisy_images, False), (pure_images, False), (ir_images, True)] |
| conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs) | |
| conv10 = Conv2D(channels, 1, activation='sigmoid')(conv9) | |
| conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1) | |
| conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2) | |
| conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3) | |
| conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4) | |
| conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6) | |
| conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7) | |
| conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8) | |
| conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9) | |
| crop = img.crop((col_i, row_i, col_i + w, row_i + h)) | |
| cropped_h | |
| list | cropped_image_offsets = [] |
| list | cropped_images_list = [(cropped_noisy_images, "noisy"), (cropped_pure_images, "pure")] |
| list | cropped_ir_images = [f for f in glob.glob(train_cropped_images_path + "**/left*" + IMAGE_EXTENSION, recursive=True)] |
| list | cropped_noisy_images = [f for f in glob.glob(train_cropped_images_path + "**/res*" + IMAGE_EXTENSION, recursive=True)] |
| convert cropped images to arrays More... | |
| list | cropped_pure_images = [f for f in glob.glob(train_cropped_images_path + "**/gt*" + IMAGE_EXTENSION, recursive=True)] |
| cropped_w | |
| curr_cropped_images | |
| denoised_col = cropped_w | |
| string | denoised_dir = images_path+r"/denoised" |
| denoised_image = model.predict(sample) | |
| denoised_name = os.path.basename(directory.split('/')[-1]) | |
| denoised_row = cropped_h | |
| drop4 = Dropout(0.5)(conv4) | |
| drop5 = Dropout(0.5)(conv5) | |
| epochs | |
| file_path = os.path.join(train_cropped_images_path, filename) | |
| filelist | |
| first_image = i*images_num_to_process | |
| int | frame_num = 0 |
| gpus = tf.config.experimental.list_physical_devices('GPU') | |
| gray_image = cv2.cvtColor(ii, cv2.COLOR_BGR2GRAY) | |
| h | |
| height | |
| ii = cv2.imread(file) | |
| im_and_ir = images_plt | |
| im_files = [f for f in glob.glob(directory + "**/res*" , recursive=True)] | |
| string | IMAGE_EXTENSION = '.png' |
| int | images_num_to_process = 1000 |
| string | images_path = root+r"/images" |
| list | images_plt = [cv2.imread(f, cv2.IMREAD_UNCHANGED) for f in im_files if f.endswith(IMAGE_EXTENSION)] |
| images_type | |
| img = Image.fromarray(np.array(gray_image).astype("uint16")) | |
| img_height | |
| img_width | |
| tuple | input_size = (img_width, img_height, channels) |
| inputs = Input(input_size) | |
| tuple | ir_config = (ir_images, ir_total_cropped_images, True, {}) |
| SPLIT IMAGES ##################. More... | |
| string | ir_cropped_images_file = test_cropped_images_path+r'/' |
| ir_im_files = [f for f in glob.glob(ir_cropped_images_file + "**/left*" , recursive=True)] | |
| list | ir_images = [f for f in glob.glob(train_images + "**/left*" + IMAGE_EXTENSION, recursive=True)] |
| list | ir_images_plt = [cv2.imread(f, cv2.IMREAD_UNCHANGED) for f in ir_im_files if f.endswith(IMAGE_EXTENSION)] |
| list | ir_total_cropped_images = [0]*len(ir_images) |
| is_ir | |
| iterations = all_cropped_num//images_num_to_process | |
| limit = first_image+images_num_to_process | |
| log_file = open(name, "w") | |
| logical_gpus = tf.config.experimental.list_logical_devices('GPU') | |
| string | logs_path = root+r"/logs" |
| loss | |
| merge6 = concatenate([drop4, up6], axis=3) | |
| merge7 = concatenate([conv3, up7], axis=3) | |
| merge8 = concatenate([conv2, up8], axis=3) | |
| merge9 = concatenate([conv1, up9], axis=3) | |
| metrics | |
| model = Model(inputs=inputs, outputs=conv10) | |
| model_checkpoint = ModelCheckpoint(models_path + r"/unet_membrane.hdf5", monitor='loss', verbose=1, save_best_only=True) | |
| string | model_name = 'DEPTH_' |
| string | models_path = root+r"/models" |
| string | name = logs_path+r'/loss_output_' |
| string | new_test_cropped_images_path = test_cropped_images_path+r'/' |
| tuple | noisy_config = (noisy_images, total_cropped_images, False, origin_files_index_size_path_test) |
| list | noisy_images = [f for f in glob.glob(train_images + "**/res*" + IMAGE_EXTENSION, recursive=True)] |
| noisy_input_train = img | |
| old_stdout = sys.stdout | |
| optimizer | |
| origin_file_name | |
| origin_files_index_size_path | |
| dictionary | origin_files_index_size_path_test = {} |
| string | outfile = denoised_dir+'/' |
| path = os.path.join(train_cropped_images_path, curr_cropped_images[i]) | |
| list | paths = [root, images_path, models_path, logs_path, train_images, train_cropped_images_path] |
| pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) | |
| pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) | |
| pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) | |
| pool4 = MaxPooling2D(pool_size=(2, 2))(drop4) | |
| list | pure_images = [f for f in glob.glob(train_images + "**/gt*" + IMAGE_EXTENSION, recursive=True)] |
| pure_input_train = img | |
| int | rolling_frame_num = 0 |
| string | root = r"./unet_flow" |
| row | |
| row_end = row+cropped_h | |
| sample = samples[i:i+1] | |
| samples = img | |
| string | save_model_name = models_path+'/' |
| save_to | |
| stdout | |
| steps_per_epoch = len(cropped_noisy_images)//unet_epochs | |
| t1 = time.perf_counter() | |
| t2 = time.perf_counter() | |
| string | test_cropped_images_path = images_path+r"/test_cropped" |
| string | test_images = images_path+r"/test" |
| test_img_height | |
| test_img_width | |
| test_model_name = save_model_name | |
| timestr = time.strftime("%Y%m%d-%H%M%S") | |
| list | total_cropped_images = [0]*len(noisy_images) |
| string | train_cropped_images_path = images_path+r"/train_cropped" |
| string | train_images = images_path+r"/train" |
| int | unet_epochs = 1 |
| up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5)) | |
| up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv6)) | |
| up8 | |
| up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv8)) | |
| w | |
| whole_image = np.zeros((height, width, channels), dtype="float32") | |
| width | |
| int example4 - train.all_cropped_num = len(os.listdir(train_cropped_images_path))//3 |
Definition at line 168 of file example4 - train.py.
| example4 - train.callbacks |
Definition at line 247 of file example4 - train.py.
| int example4 - train.channels = 2 |
Definition at line 34 of file example4 - train.py.
| example4 - train.col |
Definition at line 403 of file example4 - train.py.
Definition at line 407 of file example4 - train.py.
| example4 - train.compiled_model = model |
Definition at line 113 of file example4 - train.py.
| list example4 - train.config_list = [(noisy_images, False), (pure_images, False), (ir_images, True)] |
Definition at line 136 of file example4 - train.py.
| example4 - train.conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs) |
Definition at line 69 of file example4 - train.py.
Definition at line 108 of file example4 - train.py.
| example4 - train.conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1) |
Definition at line 72 of file example4 - train.py.
| example4 - train.conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2) |
Definition at line 75 of file example4 - train.py.
| example4 - train.conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3) |
Definition at line 78 of file example4 - train.py.
| example4 - train.conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4) |
Definition at line 83 of file example4 - train.py.
| example4 - train.conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6) |
Definition at line 89 of file example4 - train.py.
| example4 - train.conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7) |
Definition at line 94 of file example4 - train.py.
| example4 - train.conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8) |
Definition at line 100 of file example4 - train.py.
| example4 - train.conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9) |
Definition at line 105 of file example4 - train.py.
Definition at line 156 of file example4 - train.py.
| example4 - train.cropped_h |
Definition at line 131 of file example4 - train.py.
| list example4 - train.cropped_image_offsets = [] |
Definition at line 359 of file example4 - train.py.
| list example4 - train.cropped_images_list = [(cropped_noisy_images, "noisy"), (cropped_pure_images, "pure")] |
Definition at line 189 of file example4 - train.py.
| list example4 - train.cropped_ir_images = [f for f in glob.glob(train_cropped_images_path + "**/left*" + IMAGE_EXTENSION, recursive=True)] |
Definition at line 187 of file example4 - train.py.
| list example4 - train.cropped_noisy_images = [f for f in glob.glob(train_cropped_images_path + "**/res*" + IMAGE_EXTENSION, recursive=True)] |
convert cropped images to arrays
IMAGE TO ARRAY ##################.
Definition at line 185 of file example4 - train.py.
| list example4 - train.cropped_pure_images = [f for f in glob.glob(train_cropped_images_path + "**/gt*" + IMAGE_EXTENSION, recursive=True)] |
Definition at line 186 of file example4 - train.py.
| example4 - train.cropped_w |
Definition at line 131 of file example4 - train.py.
| example4 - train.curr_cropped_images |
Definition at line 192 of file example4 - train.py.
| example4 - train.denoised_col = cropped_w |
Definition at line 409 of file example4 - train.py.
| string example4 - train.denoised_dir = images_path+r"/denoised" |
Definition at line 266 of file example4 - train.py.
| example4 - train.denoised_image = model.predict(sample) |
Definition at line 405 of file example4 - train.py.
| example4 - train.denoised_name = os.path.basename(directory.split('/')[-1]) |
Definition at line 420 of file example4 - train.py.
| example4 - train.denoised_row = cropped_h |
Definition at line 408 of file example4 - train.py.
| example4 - train.drop4 = Dropout(0.5)(conv4) |
Definition at line 80 of file example4 - train.py.
| example4 - train.drop5 = Dropout(0.5)(conv5) |
Definition at line 85 of file example4 - train.py.
| example4 - train.epochs |
Definition at line 246 of file example4 - train.py.
| example4 - train.file_path = os.path.join(train_cropped_images_path, filename) |
Definition at line 122 of file example4 - train.py.
| example4 - train.filelist |
Definition at line 140 of file example4 - train.py.
| example4 - train.first_image = i*images_num_to_process |
Definition at line 180 of file example4 - train.py.
| int example4 - train.frame_num = 0 |
Definition at line 153 of file example4 - train.py.
| example4 - train.gpus = tf.config.experimental.list_physical_devices('GPU') |
Definition at line 42 of file example4 - train.py.
| example4 - train.gray_image = cv2.cvtColor(ii, cv2.COLOR_BGR2GRAY) |
Definition at line 148 of file example4 - train.py.
| example4 - train.h |
Definition at line 141 of file example4 - train.py.
| example4 - train.height |
Definition at line 152 of file example4 - train.py.
| example4 - train.ii = cv2.imread(file) |
Definition at line 147 of file example4 - train.py.
| example4 - train.im_and_ir = images_plt |
Definition at line 224 of file example4 - train.py.
Definition at line 193 of file example4 - train.py.
| string example4 - train.IMAGE_EXTENSION = '.png' |
Definition at line 39 of file example4 - train.py.
| example4 - train.images_num_to_process = 1000 |
Definition at line 167 of file example4 - train.py.
Definition at line 19 of file example4 - train.py.
| example4 - train.images_plt = [cv2.imread(f, cv2.IMREAD_UNCHANGED) for f in im_files if f.endswith(IMAGE_EXTENSION)] |
Definition at line 217 of file example4 - train.py.
| example4 - train.images_type |
Definition at line 192 of file example4 - train.py.
| tuple example4 - train.img = Image.fromarray(np.array(gray_image).astype("uint16")) |
Definition at line 149 of file example4 - train.py.
| example4 - train.img_height |
Definition at line 35 of file example4 - train.py.
| example4 - train.img_width |
Definition at line 35 of file example4 - train.py.
| tuple example4 - train.input_size = (img_width, img_height, channels) |
Definition at line 67 of file example4 - train.py.
| example4 - train.inputs = Input(input_size) |
Definition at line 68 of file example4 - train.py.
| tuple example4 - train.ir_config = (ir_images, ir_total_cropped_images, True, {}) |
SPLIT IMAGES ##################.
Definition at line 312 of file example4 - train.py.
| string example4 - train.ir_cropped_images_file = test_cropped_images_path+r'/' |
Definition at line 360 of file example4 - train.py.
| list example4 - train.ir_im_files = [f for f in glob.glob(ir_cropped_images_file + "**/left*" , recursive=True)] |
Definition at line 193 of file example4 - train.py.
| list example4 - train.ir_images = [f for f in glob.glob(train_images + "**/left*" + IMAGE_EXTENSION, recursive=True)] |
Definition at line 135 of file example4 - train.py.
| example4 - train.ir_images_plt = [cv2.imread(f, cv2.IMREAD_UNCHANGED) for f in ir_im_files if f.endswith(IMAGE_EXTENSION)] |
Definition at line 218 of file example4 - train.py.
Definition at line 307 of file example4 - train.py.
| example4 - train.is_ir |
Definition at line 140 of file example4 - train.py.
| example4 - train.iterations = all_cropped_num//images_num_to_process |
Definition at line 169 of file example4 - train.py.
| example4 - train.limit = first_image+images_num_to_process |
Definition at line 196 of file example4 - train.py.
| example4 - train.log_file = open(name, "w") |
Definition at line 62 of file example4 - train.py.
| example4 - train.logical_gpus = tf.config.experimental.list_logical_devices('GPU') |
Definition at line 49 of file example4 - train.py.
Definition at line 21 of file example4 - train.py.
| example4 - train.loss |
Definition at line 111 of file example4 - train.py.
Definition at line 88 of file example4 - train.py.
Definition at line 93 of file example4 - train.py.
Definition at line 99 of file example4 - train.py.
Definition at line 104 of file example4 - train.py.
| example4 - train.metrics |
Definition at line 111 of file example4 - train.py.
Definition at line 110 of file example4 - train.py.
| example4 - train.model_checkpoint = ModelCheckpoint(models_path + r"/unet_membrane.hdf5", monitor='loss', verbose=1, save_best_only=True) |
Definition at line 242 of file example4 - train.py.
| string example4 - train.model_name = 'DEPTH_' |
Definition at line 60 of file example4 - train.py.
Definition at line 20 of file example4 - train.py.
Definition at line 61 of file example4 - train.py.
| string example4 - train.new_test_cropped_images_path = test_cropped_images_path+r'/' |
Definition at line 326 of file example4 - train.py.
| tuple example4 - train.noisy_config = (noisy_images, total_cropped_images, False, origin_files_index_size_path_test) |
Definition at line 313 of file example4 - train.py.
| list example4 - train.noisy_images = [f for f in glob.glob(train_images + "**/res*" + IMAGE_EXTENSION, recursive=True)] |
Definition at line 133 of file example4 - train.py.
| example4 - train.noisy_input_train = img |
Definition at line 239 of file example4 - train.py.
| example4 - train.old_stdout = sys.stdout |
Definition at line 58 of file example4 - train.py.
| example4 - train.optimizer |
Definition at line 111 of file example4 - train.py.
| example4 - train.origin_file_name |
Definition at line 395 of file example4 - train.py.
| example4 - train.origin_files_index_size_path |
Definition at line 317 of file example4 - train.py.
| dictionary example4 - train.origin_files_index_size_path_test = {} |
Definition at line 259 of file example4 - train.py.
| string example4 - train.outfile = denoised_dir+'/' |
Definition at line 421 of file example4 - train.py.
| example4 - train.path = os.path.join(train_cropped_images_path, curr_cropped_images[i]) |
Definition at line 201 of file example4 - train.py.
| list example4 - train.paths = [root, images_path, models_path, logs_path, train_images, train_cropped_images_path] |
Definition at line 24 of file example4 - train.py.
| example4 - train.pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) |
Definition at line 71 of file example4 - train.py.
| example4 - train.pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) |
Definition at line 74 of file example4 - train.py.
| example4 - train.pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) |
Definition at line 77 of file example4 - train.py.
| example4 - train.pool4 = MaxPooling2D(pool_size=(2, 2))(drop4) |
Definition at line 81 of file example4 - train.py.
| list example4 - train.pure_images = [f for f in glob.glob(train_images + "**/gt*" + IMAGE_EXTENSION, recursive=True)] |
Definition at line 134 of file example4 - train.py.
| example4 - train.pure_input_train = img |
Definition at line 237 of file example4 - train.py.
| example4 - train.rolling_frame_num = 0 |
Definition at line 142 of file example4 - train.py.
Definition at line 18 of file example4 - train.py.
| example4 - train.row |
Definition at line 403 of file example4 - train.py.
Definition at line 406 of file example4 - train.py.
| example4 - train.sample = samples[i:i+1] |
Definition at line 402 of file example4 - train.py.
| example4 - train.samples = img |
Definition at line 393 of file example4 - train.py.
| string example4 - train.save_model_name = models_path+'/' |
Definition at line 166 of file example4 - train.py.
| example4 - train.save_to |
Definition at line 157 of file example4 - train.py.
| example4 - train.stdout |
Definition at line 63 of file example4 - train.py.
| example4 - train.steps_per_epoch = len(cropped_noisy_images)//unet_epochs |
Definition at line 243 of file example4 - train.py.
| example4 - train.t1 = time.perf_counter() |
Definition at line 399 of file example4 - train.py.
| example4 - train.t2 = time.perf_counter() |
Definition at line 418 of file example4 - train.py.
| string example4 - train.test_cropped_images_path = images_path+r"/test_cropped" |
Definition at line 265 of file example4 - train.py.
| string example4 - train.test_images = images_path+r"/test" |
Definition at line 264 of file example4 - train.py.
| example4 - train.test_img_height |
Definition at line 260 of file example4 - train.py.
| example4 - train.test_img_width |
Definition at line 260 of file example4 - train.py.
| example4 - train.test_model_name = save_model_name |
Definition at line 262 of file example4 - train.py.
Definition at line 59 of file example4 - train.py.
| example4 - train.total_cropped_images = [0]*len(noisy_images) |
Definition at line 306 of file example4 - train.py.
| string example4 - train.train_cropped_images_path = images_path+r"/train_cropped" |
Definition at line 23 of file example4 - train.py.
| string example4 - train.train_images = images_path+r"/train" |
Definition at line 22 of file example4 - train.py.
| int example4 - train.unet_epochs = 1 |
Definition at line 37 of file example4 - train.py.
| example4 - train.up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5)) |
Definition at line 87 of file example4 - train.py.
| example4 - train.up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv6)) |
Definition at line 92 of file example4 - train.py.
| example4 - train.up8 |
Definition at line 97 of file example4 - train.py.
| example4 - train.up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv8)) |
Definition at line 103 of file example4 - train.py.
| example4 - train.w |
Definition at line 141 of file example4 - train.py.
Definition at line 397 of file example4 - train.py.
| example4 - train.width |
Definition at line 152 of file example4 - train.py.