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| htmlcov | ||
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| cache/ | ||
| local/cache/ | ||
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167 changes: 167 additions & 0 deletions
167
cm-mlops/script/get-preprocessed-dataset-generic/src/preprocess_object_detection_dataset.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,167 @@ | ||
| #!/usr/bin/env python3 | ||
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| import os | ||
| import json | ||
| import numpy as np | ||
| from PIL import Image | ||
| import torch | ||
| import torchvision | ||
|
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| SUPPORTED_EXTENSIONS = ['jpeg', 'jpg', 'gif', 'png'] | ||
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| def load_image(image_path, target_size, data_type='uint8', convert_to_bgr=False, | ||
| normalize_data=False, normalize_lower=-1, normalize_upper=1, | ||
| subtract_mean=False, given_channel_means='', given_channel_stds='', | ||
| quantize=0, quant_scale=1, quant_offset=0, convert_to_unsigned=0): | ||
| if not convert_to_bgr: | ||
| image = Image.open(image_path).convert('RGB') | ||
| else: | ||
| image = Image.open(image_path).convert('BGR') | ||
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| tensor_image = torchvision.transforms.functional.to_tensor(image) | ||
| mean = torch.as_tensor(given_channel_means) | ||
| std = torch.as_tensor(given_channel_stds) | ||
| normalized_image = (tensor_image - mean[:, None, None]) / std[:, None, None] | ||
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| resized_image = torch.nn.functional.interpolate(normalized_image[None], | ||
| size=(target_size, target_size), | ||
| mode='bilinear')[0].numpy() | ||
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| if quantize == 1: | ||
| resized_image = quantize_to_uint8(resized_image, quant_scale, quant_offset) | ||
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| original_height, original_width, _ = resized_image.shape | ||
| batch_shape = (1, target_size, target_size, 3) | ||
| batch_data = resized_image.reshape(batch_shape) | ||
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| return batch_data, original_width, original_height | ||
|
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| def quantize_to_uint8(image, scale, offset): | ||
| quantized_image = (image.astype(np.float64) / scale + offset).astype(np.float64) | ||
| output = np.round_(quantized_image) | ||
| output = np.clip(output, 0, 255) | ||
| return output.astype(np.uint8) | ||
|
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| def preprocess_files(selected_filenames, source_dir, destination_dir, square_side, | ||
| data_type, convert_to_bgr, normalize_data, normalize_lower, | ||
| normalize_upper, subtract_mean, given_channel_means, | ||
| given_channel_stds, quantize, quant_scale, quant_offset, | ||
| convert_to_unsigned, new_file_extension): | ||
| output_signatures = [] | ||
|
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| for current_idx, input_filename in enumerate(selected_filenames): | ||
| full_input_path = os.path.join(source_dir, input_filename) | ||
| image_data, original_width, original_height = load_image( | ||
| image_path=full_input_path, | ||
| target_size=square_side, | ||
| data_type=data_type, | ||
| convert_to_bgr=convert_to_bgr, | ||
| normalize_data=normalize_data, | ||
| normalize_lower=normalize_lower, | ||
| normalize_upper=normalize_upper, | ||
| subtract_mean=subtract_mean, | ||
| given_channel_means=given_channel_means, | ||
| given_channel_stds=given_channel_stds, | ||
| quantize=quantize, | ||
| quant_scale=quant_scale, | ||
| quant_offset=quant_offset, | ||
| convert_to_unsigned=convert_to_unsigned | ||
| ) | ||
|
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| output_filename = f"{input_filename.rsplit('.', 1)[0]}.{new_file_extension}" if new_file_extension else input_filename | ||
| full_output_path = os.path.join(destination_dir, output_filename) | ||
| image_data.tofile(full_output_path) | ||
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| print(f"[{current_idx+1}]: Stored {full_output_path}") | ||
| output_signatures.append(f'{output_filename};{original_width};{original_height}') | ||
|
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| return output_signatures | ||
|
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| def preprocess(): | ||
| source_directory = os.environ['CM_DATASET_PATH'] | ||
| destination_directory = os.environ['CM_DATASET_PREPROCESSED_PATH'] | ||
|
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| intermediate_data_type = os.environ.get('CM_DATASET_INTERMEDIATE_DATA_TYPE', np.float32) | ||
| square_side = int(os.environ['CM_DATASET_INPUT_SQUARE_SIDE']) | ||
| crop_percentage = float(os.environ['CM_DATASET_CROP_FACTOR']) | ||
| inter_size = int(os.getenv('CM_DATASET_INTERMEDIATE_SIZE', 0)) | ||
| convert_to_bgr = int(os.getenv('CM_DATASET_CONVERT_TO_BGR', 0)) | ||
| offset = int(os.getenv('CM_DATASET_SUBSET_OFFSET', 0)) | ||
| volume = int(os.environ['CM_DATASET_SIZE']) | ||
| fof_name = os.getenv('CM_DATASET_SUBSET_FOF', 'files.txt') | ||
| data_type = os.getenv('CM_DATASET_DATA_TYPE_INPUT', 'float32') | ||
| input_data_type = os.getenv('CM_DATASET_DATA_TYPE_INPUT', 'float32') | ||
| data_layout = os.getenv('CM_DATASET_DATA_LAYOUT', '').lower() | ||
| new_file_extension = os.getenv('CM_DATASET_PREPROCESSED_EXTENSION', '') | ||
| normalize_data = int(os.getenv('CM_DATASET_NORMALIZE_DATA', '0')) | ||
| subtract_mean = int(os.getenv('CM_DATASET_SUBTRACT_MEANS', '0')) | ||
| given_channel_means = os.getenv('CM_DATASET_GIVEN_CHANNEL_MEANS', '') | ||
| given_channel_stds = os.getenv('CM_DATASET_GIVEN_CHANNEL_STDS', '') | ||
| quant_scale = float(os.environ['CM_DATASET_QUANT_SCALE']) | ||
| quant_offset = float(os.environ['CM_DATASET_QUANT_OFFSET']) | ||
| quantize = int(os.environ['CM_DATASET_QUANTIZE']) # 1 for quantize to int8 | ||
| convert_to_unsigned = int(os.environ['CM_DATASET_CONVERT_TO_UNSIGNED']) # 1 for int8 to uint8 | ||
|
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| images_list = os.getenv('CM_DATASET_IMAGES_LIST') | ||
| interpolation_method = os.getenv('CM_DATASET_INTERPOLATION_METHOD', '') | ||
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| annotations_filepath = os.environ['CM_DATASET_ANNOTATIONS_FILE_PATH'] | ||
| is_calibration = os.environ['CM_DATASET_TYPE'] == "calibration" | ||
| image_file = os.getenv('CM_IMAGE_FILE', '') | ||
|
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| normalize_lower = float(os.getenv('CM_DATASET_NORMALIZE_LOWER', -1.0)) | ||
| normalize_upper = float(os.getenv('CM_DATASET_NORMALIZE_UPPER', 1.0)) | ||
|
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| if given_channel_means: | ||
| given_channel_means = np.fromstring(given_channel_means, dtype=np.float32, sep=' ').astype(intermediate_data_type) | ||
| if convert_to_bgr: | ||
| given_channel_means = given_channel_means[::-1] | ||
|
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| given_channel_stds = os.getenv('CM_DATASET_GIVEN_CHANNEL_STDS', '') | ||
| if given_channel_stds: | ||
| given_channel_stds = np.fromstring(given_channel_stds, dtype=np.float32, sep=' ').astype(intermediate_data_type) | ||
| if convert_to_bgr: | ||
| given_channel_stds = given_channel_stds[::-1] | ||
|
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| print(f"From: {source_directory}, To: {destination_directory}, Size: {square_side}, Crop: {crop_percentage}, InterSize: {inter_size}, 2BGR: {convert_to_bgr}, " + | ||
| f"OFF: {offset}, VOL: '{volume}', FOF: {fof_name}, DTYPE: {data_type}, DLAYOUT: {data_layout}, EXT: {new_file_extension}, " + | ||
| f"NORM: {normalize_data}, SMEAN: {subtract_mean}, GCM: {given_channel_means}, GSTD: {given_channel_stds}, QUANTIZE: {quantize}, QUANT_SCALE: {quant_scale}, " + | ||
| f"QUANT_OFFSET: {quant_offset}, CONV_UNSIGNED: {convert_to_unsigned}, INTER: {interpolation_method}") | ||
|
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| if image_file: | ||
| source_directory = os.path.dirname(image_file) | ||
| selected_filenames = [os.path.basename(image_file)] | ||
| else: | ||
| if annotations_filepath and not is_calibration: | ||
| with open(annotations_filepath, "r") as annotations_fh: | ||
| annotations_struct = json.load(annotations_fh) | ||
| ordered_filenames = [image_entry['file_name'] for image_entry in annotations_struct['images']] | ||
| elif os.path.isdir(source_directory): | ||
| ordered_filenames = [filename for filename in sorted(os.listdir(source_directory)) if any(filename.lower().endswith(extension) for extension in SUPPORTED_EXTENSIONS)] | ||
| else: | ||
| raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), source_directory) | ||
|
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| total_volume = len(ordered_filenames) | ||
|
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| if offset < 0: | ||
| offset += total_volume | ||
|
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| if not volume: | ||
| volume = total_volume - offset | ||
|
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| selected_filenames = ordered_filenames[offset:offset + volume] | ||
|
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| output_signatures = preprocess_files(selected_filenames, source_directory, destination_directory, square_side, data_type, | ||
| convert_to_bgr, normalize_data, normalize_lower, normalize_upper, | ||
| subtract_mean, given_channel_means, given_channel_stds, quantize, | ||
| quant_scale, quant_offset, convert_to_unsigned, new_file_extension) | ||
|
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| fof_full_path = os.path.join(destination_directory, fof_name) | ||
| with open(fof_full_path, 'w') as fof_file: | ||
| for filename in output_signatures: | ||
| fof_file.write(f'{filename}\n') | ||
|
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||
| if __name__ == "__main__": | ||
| preprocess() | ||
|
|
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