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# 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)
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