# Copyright (C) 2019-2020 Intel Corporation # # SPDX-License-Identifier: MIT from collections import Counter from enum import Enum import numpy as np import os import os.path as osp _formats = ['simple'] import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") import tensorboardX as tb _formats.append('tensorboard') from datumaro.components.extractor import AnnotationType from datumaro.util.image import save_image Format = Enum('Formats', _formats) class DiffVisualizer: Format = Format DEFAULT_FORMAT = Format.simple _UNMATCHED_LABEL = -1 def __init__(self, comparator, save_dir, output_format=DEFAULT_FORMAT): self.comparator = comparator if isinstance(output_format, str): output_format = Format[output_format] assert output_format in Format self.output_format = output_format self.save_dir = save_dir if output_format is Format.tensorboard: logdir = osp.join(self.save_dir, 'logs', 'diff') self.file_writer = tb.SummaryWriter(logdir) if output_format is Format.simple: self.label_diff_writer = None self.categories = {} self.label_confusion_matrix = Counter() self.bbox_confusion_matrix = Counter() def save_dataset_diff(self, extractor_a, extractor_b): if self.save_dir: os.makedirs(self.save_dir, exist_ok=True) if len(extractor_a) != len(extractor_b): print("Datasets have different lengths: %s vs %s" % \ (len(extractor_a), len(extractor_b))) self.categories = {} label_mismatch = self.comparator. \ compare_dataset_labels(extractor_a, extractor_b) if label_mismatch is None: print("Datasets have no label information") elif len(label_mismatch) != 0: print("Datasets have mismatching labels:") for a_label, b_label in label_mismatch: if a_label is None: print(" > %s" % b_label.name) elif b_label is None: print(" < %s" % a_label.name) else: print(" %s != %s" % (a_label.name, b_label.name)) else: self.categories.update(extractor_a.categories()) self.categories.update(extractor_b.categories()) self.label_confusion_matrix = Counter() self.bbox_confusion_matrix = Counter() if self.output_format is Format.tensorboard: self.file_writer.reopen() ids_a = set((item.id, item.subset) for item in extractor_a) ids_b = set((item.id, item.subset) for item in extractor_b) ids = ids_a & ids_b if len(ids) != len(ids_a): print("Unmatched items in the first dataset: ") print(ids_a - ids) if len(ids) != len(ids_b): print("Unmatched items in the second dataset: ") print(ids_b - ids) for item_id, item_subset in ids: item_a = extractor_a.get(item_id, item_subset) item_b = extractor_a.get(item_id, item_subset) label_diff = self.comparator.compare_item_labels(item_a, item_b) self.update_label_confusion(label_diff) bbox_diff = self.comparator.compare_item_bboxes(item_a, item_b) self.update_bbox_confusion(bbox_diff) self.save_item_label_diff(item_a, item_b, label_diff) self.save_item_bbox_diff(item_a, item_b, bbox_diff) if len(self.label_confusion_matrix) != 0: self.save_conf_matrix(self.label_confusion_matrix, 'labels_confusion.png') if len(self.bbox_confusion_matrix) != 0: self.save_conf_matrix(self.bbox_confusion_matrix, 'bbox_confusion.png') if self.output_format is Format.tensorboard: self.file_writer.flush() self.file_writer.close() elif self.output_format is Format.simple: if self.label_diff_writer: self.label_diff_writer.flush() self.label_diff_writer.close() def update_label_confusion(self, label_diff): matches, a_unmatched, b_unmatched = label_diff for label in matches: self.label_confusion_matrix[(label, label)] += 1 for a_label in a_unmatched: self.label_confusion_matrix[(a_label, self._UNMATCHED_LABEL)] += 1 for b_label in b_unmatched: self.label_confusion_matrix[(self._UNMATCHED_LABEL, b_label)] += 1 def update_bbox_confusion(self, bbox_diff): matches, mispred, a_unmatched, b_unmatched = bbox_diff for a_bbox, b_bbox in matches: self.bbox_confusion_matrix[(a_bbox.label, b_bbox.label)] += 1 for a_bbox, b_bbox in mispred: self.bbox_confusion_matrix[(a_bbox.label, b_bbox.label)] += 1 for a_bbox in a_unmatched: self.bbox_confusion_matrix[(a_bbox.label, self._UNMATCHED_LABEL)] += 1 for b_bbox in b_unmatched: self.bbox_confusion_matrix[(self._UNMATCHED_LABEL, b_bbox.label)] += 1 @classmethod def draw_text_with_background(cls, frame, text, origin, font=None, scale=1.0, color=(0, 0, 0), thickness=1, bgcolor=(1, 1, 1)): import cv2 if not font: font = cv2.FONT_HERSHEY_SIMPLEX text_size, baseline = cv2.getTextSize(text, font, scale, thickness) cv2.rectangle(frame, tuple((origin + (0, baseline)).astype(int)), tuple((origin + (text_size[0], -text_size[1])).astype(int)), bgcolor, cv2.FILLED) cv2.putText(frame, text, tuple(origin.astype(int)), font, scale, color, thickness) return text_size, baseline def draw_detection_roi(self, frame, x, y, w, h, label, conf, color): import cv2 cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) text = '%s %.2f%%' % (label, 100.0 * conf) text_scale = 0.5 font = cv2.FONT_HERSHEY_SIMPLEX text_size = cv2.getTextSize(text, font, text_scale, 1) line_height = np.array([0, text_size[0][1]]) self.draw_text_with_background(frame, text, np.array([x, y]) - line_height * 0.5, font, scale=text_scale, color=[255 - c for c in color]) def get_label(self, label_id): cat = self.categories.get(AnnotationType.label) if cat is None: return str(label_id) return cat.items[label_id].name def draw_bbox(self, img, shape, color): x, y, w, h = shape.get_bbox() self.draw_detection_roi(img, int(x), int(y), int(w), int(h), self.get_label(shape.label), shape.attributes.get('score', 1), color) def get_label_diff_file(self): if self.label_diff_writer is None: self.label_diff_writer = \ open(osp.join(self.save_dir, 'label_diff.txt'), 'w') return self.label_diff_writer def save_item_label_diff(self, item_a, item_b, diff): _, a_unmatched, b_unmatched = diff if 0 < len(a_unmatched) + len(b_unmatched): if self.output_format is Format.simple: f = self.get_label_diff_file() f.write(item_a.id + '\n') for a_label in a_unmatched: f.write(' >%s\n' % self.get_label(a_label)) for b_label in b_unmatched: f.write(' <%s\n' % self.get_label(b_label)) elif self.output_format is Format.tensorboard: tag = item_a.id for a_label in a_unmatched: self.file_writer.add_text(tag, '>%s\n' % self.get_label(a_label)) for b_label in b_unmatched: self.file_writer.add_text(tag, '<%s\n' % self.get_label(b_label)) def save_item_bbox_diff(self, item_a, item_b, diff): _, mispred, a_unmatched, b_unmatched = diff if 0 < len(a_unmatched) + len(b_unmatched) + len(mispred): img_a = item_a.image.data.copy() img_b = img_a.copy() for a_bbox, b_bbox in mispred: self.draw_bbox(img_a, a_bbox, (0, 255, 0)) self.draw_bbox(img_b, b_bbox, (0, 0, 255)) for a_bbox in a_unmatched: self.draw_bbox(img_a, a_bbox, (255, 255, 0)) for b_bbox in b_unmatched: self.draw_bbox(img_b, b_bbox, (255, 255, 0)) img = np.hstack([img_a, img_b]) path = osp.join(self.save_dir, item_a.id) if self.output_format is Format.simple: save_image(path + '.png', img, create_dir=True) elif self.output_format is Format.tensorboard: self.save_as_tensorboard(img, path) def save_as_tensorboard(self, img, name): img = img[:, :, ::-1] # to RGB img = np.transpose(img, (2, 0, 1)) # to (C, H, W) img = img.astype(dtype=np.uint8) self.file_writer.add_image(name, img) def save_conf_matrix(self, conf_matrix, filename): import matplotlib.pyplot as plt classes = None label_categories = self.categories.get(AnnotationType.label) if label_categories is not None: classes = { id: c.name for id, c in enumerate(label_categories.items) } if classes is None: classes = { c: 'label_%s' % c for c, _ in conf_matrix } classes[self._UNMATCHED_LABEL] = 'unmatched' class_idx = { id: i for i, id in enumerate(classes.keys()) } matrix = np.zeros((len(classes), len(classes)), dtype=int) for idx_pair in conf_matrix: index = (class_idx[idx_pair[0]], class_idx[idx_pair[1]]) matrix[index] = conf_matrix[idx_pair] labels = [label for id, label in classes.items()] fig = plt.figure() fig.add_subplot(111) table = plt.table( cellText=matrix, colLabels=labels, rowLabels=labels, loc ='center') table.auto_set_font_size(False) table.set_fontsize(8) table.scale(3, 3) # Removing ticks and spines enables you to get the figure only with table plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False) plt.tick_params(axis='y', which='both', right=False, left=False, labelleft=False) for pos in ['right','top','bottom','left']: plt.gca().spines[pos].set_visible(False) for idx_pair in conf_matrix: i = class_idx[idx_pair[0]] j = class_idx[idx_pair[1]] if conf_matrix[idx_pair] != 0: if i != j: table._cells[(i + 1, j)].set_facecolor('#FF0000') else: table._cells[(i + 1, j)].set_facecolor('#00FF00') plt.savefig(osp.join(self.save_dir, filename), bbox_inches='tight', pad_inches=0.05)