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dl-desktop
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ssd_keras-master/eval_utils/coco_utils.py
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ssd_keras-master/eval_utils/coco_utils.py
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'''
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A few utilities that are useful when working with the MS COCO datasets.
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Copyright (C) 2018 Pierluigi Ferrari
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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'''
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import json
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from tqdm import trange
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from math import ceil
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import sys
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from data_generator.object_detection_2d_geometric_ops import Resize
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from data_generator.object_detection_2d_patch_sampling_ops import RandomPadFixedAR
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from data_generator.object_detection_2d_photometric_ops import ConvertTo3Channels
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from ssd_encoder_decoder.ssd_output_decoder import decode_detections
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from data_generator.object_detection_2d_misc_utils import apply_inverse_transforms
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def get_coco_category_maps(annotations_file):
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'''
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Builds dictionaries that map between MS COCO category IDs, transformed category IDs, and category names.
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The original MS COCO category IDs are not consecutive unfortunately: The 80 category IDs are spread
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across the integers 1 through 90 with some integers skipped. Since we usually use a one-hot
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class representation in neural networks, we need to map these non-consecutive original COCO category
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IDs (let's call them 'cats') to consecutive category IDs (let's call them 'classes').
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Arguments:
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annotations_file (str): The filepath to any MS COCO annotations JSON file.
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Returns:
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1) cats_to_classes: A dictionary that maps between the original (keys) and the transformed category IDs (values).
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2) classes_to_cats: A dictionary that maps between the transformed (keys) and the original category IDs (values).
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3) cats_to_names: A dictionary that maps between original category IDs (keys) and the respective category names (values).
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4) classes_to_names: A list of the category names (values) with their indices representing the transformed IDs.
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'''
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with open(annotations_file, 'r') as f:
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annotations = json.load(f)
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cats_to_classes = {}
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classes_to_cats = {}
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cats_to_names = {}
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classes_to_names = []
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classes_to_names.append('background') # Need to add the background class first so that the indexing is right.
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for i, cat in enumerate(annotations['categories']):
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cats_to_classes[cat['id']] = i + 1
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classes_to_cats[i + 1] = cat['id']
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cats_to_names[cat['id']] = cat['name']
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classes_to_names.append(cat['name'])
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return cats_to_classes, classes_to_cats, cats_to_names, classes_to_names
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def predict_all_to_json(out_file,
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model,
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img_height,
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img_width,
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classes_to_cats,
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data_generator,
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batch_size,
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data_generator_mode='resize',
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model_mode='training',
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confidence_thresh=0.01,
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iou_threshold=0.45,
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top_k=200,
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pred_coords='centroids',
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normalize_coords=True):
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'''
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Runs detection predictions over the whole dataset given a model and saves them in a JSON file
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in the MS COCO detection results format.
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Arguments:
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out_file (str): The file name (full path) under which to save the results JSON file.
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model (Keras model): A Keras SSD model object.
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img_height (int): The input image height for the model.
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img_width (int): The input image width for the model.
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classes_to_cats (dict): A dictionary that maps the consecutive class IDs predicted by the model
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to the non-consecutive original MS COCO category IDs.
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data_generator (DataGenerator): A `DataGenerator` object with the evaluation dataset.
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batch_size (int): The batch size for the evaluation.
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data_generator_mode (str, optional): Either of 'resize' or 'pad'. If 'resize', the input images will
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be resized (i.e. warped) to `(img_height, img_width)`. This mode does not preserve the aspect ratios of the images.
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If 'pad', the input images will be first padded so that they have the aspect ratio defined by `img_height`
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and `img_width` and then resized to `(img_height, img_width)`. This mode preserves the aspect ratios of the images.
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model_mode (str, optional): The mode in which the model was created, i.e. 'training', 'inference' or 'inference_fast'.
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This is needed in order to know whether the model output is already decoded or still needs to be decoded. Refer to
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the model documentation for the meaning of the individual modes.
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confidence_thresh (float, optional): A float in [0,1), the minimum classification confidence in a specific
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positive class in order to be considered for the non-maximum suppression stage for the respective class.
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A lower value will result in a larger part of the selection process being done by the non-maximum suppression
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stage, while a larger value will result in a larger part of the selection process happening in the confidence
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thresholding stage.
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iou_threshold (float, optional): A float in [0,1]. All boxes with a Jaccard similarity of greater than `iou_threshold`
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with a locally maximal box will be removed from the set of predictions for a given class, where 'maximal' refers
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to the box score.
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top_k (int, optional): The number of highest scoring predictions to be kept for each batch item after the
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non-maximum suppression stage. Defaults to 200, following the paper.
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input_coords (str, optional): The box coordinate format that the model outputs. Can be either 'centroids'
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for the format `(cx, cy, w, h)` (box center coordinates, width, and height), 'minmax' for the format
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`(xmin, xmax, ymin, ymax)`, or 'corners' for the format `(xmin, ymin, xmax, ymax)`.
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normalize_coords (bool, optional): Set to `True` if the model outputs relative coordinates (i.e. coordinates in [0,1])
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and you wish to transform these relative coordinates back to absolute coordinates. If the model outputs
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relative coordinates, but you do not want to convert them back to absolute coordinates, set this to `False`.
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Do not set this to `True` if the model already outputs absolute coordinates, as that would result in incorrect
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coordinates. Requires `img_height` and `img_width` if set to `True`.
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Returns:
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None.
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'''
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convert_to_3_channels = ConvertTo3Channels()
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resize = Resize(height=img_height,width=img_width)
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if data_generator_mode == 'resize':
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transformations = [convert_to_3_channels,
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resize]
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elif data_generator_mode == 'pad':
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random_pad = RandomPadFixedAR(patch_aspect_ratio=img_width/img_height, clip_boxes=False)
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transformations = [convert_to_3_channels,
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random_pad,
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resize]
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else:
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raise ValueError("Unexpected argument value: `data_generator_mode` can be either of 'resize' or 'pad', but received '{}'.".format(data_generator_mode))
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# Set the generator parameters.
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generator = data_generator.generate(batch_size=batch_size,
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shuffle=False,
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transformations=transformations,
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label_encoder=None,
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returns={'processed_images',
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'image_ids',
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'inverse_transform'},
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keep_images_without_gt=True)
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# Put the results in this list.
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results = []
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# Compute the number of batches to iterate over the entire dataset.
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n_images = data_generator.get_dataset_size()
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print("Number of images in the evaluation dataset: {}".format(n_images))
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n_batches = int(ceil(n_images / batch_size))
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# Loop over all batches.
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tr = trange(n_batches, file=sys.stdout)
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tr.set_description('Producing results file')
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for i in tr:
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# Generate batch.
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batch_X, batch_image_ids, batch_inverse_transforms = next(generator)
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# Predict.
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y_pred = model.predict(batch_X)
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# If the model was created in 'training' mode, the raw predictions need to
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# be decoded and filtered, otherwise that's already taken care of.
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if model_mode == 'training':
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# Decode.
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y_pred = decode_detections(y_pred,
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confidence_thresh=confidence_thresh,
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iou_threshold=iou_threshold,
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top_k=top_k,
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input_coords=pred_coords,
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normalize_coords=normalize_coords,
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img_height=img_height,
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img_width=img_width)
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else:
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# Filter out the all-zeros dummy elements of `y_pred`.
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y_pred_filtered = []
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for i in range(len(y_pred)):
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y_pred_filtered.append(y_pred[i][y_pred[i,:,0] != 0])
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y_pred = y_pred_filtered
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# Convert the predicted box coordinates for the original images.
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y_pred = apply_inverse_transforms(y_pred, batch_inverse_transforms)
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# Convert each predicted box into the results format.
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for k, batch_item in enumerate(y_pred):
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for box in batch_item:
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class_id = box[0]
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# Transform the consecutive class IDs back to the original COCO category IDs.
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cat_id = classes_to_cats[class_id]
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# Round the box coordinates to reduce the JSON file size.
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xmin = float(round(box[2], 1))
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ymin = float(round(box[3], 1))
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xmax = float(round(box[4], 1))
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ymax = float(round(box[5], 1))
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width = xmax - xmin
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height = ymax - ymin
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bbox = [xmin, ymin, width, height]
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result = {}
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result['image_id'] = batch_image_ids[k]
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result['category_id'] = cat_id
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result['score'] = float(round(box[1], 3))
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result['bbox'] = bbox
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results.append(result)
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with open(out_file, 'w') as f:
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json.dump(results, f)
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print("Prediction results saved in '{}'".format(out_file))
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