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"""
Author: Roman Solovyev, IPPM RAS
URL: https://github.com/ZFTurbo
Code based on: https://github.com/fizyr/keras-retinanet/blob/master/keras_retinanet/utils/eval.py
"""
import numpy as np
import pandas as pd
try:
import pyximport
pyximport.install(setup_args={"include_dirs": np.get_include()}, reload_support=False)
from .compute_overlap import compute_overlap
except:
print("Couldn't import fast version of function compute_overlap, will use slow one. Check cython intallation")
from .compute_overlap_slow import compute_overlap
def get_real_annotations(table):
res = dict()
ids = table['ImageID'].values.astype(np.str)
labels = table['LabelName'].values.astype(np.str)
xmin = table['XMin'].values.astype(np.float32)
xmax = table['XMax'].values.astype(np.float32)
ymin = table['YMin'].values.astype(np.float32)
ymax = table['YMax'].values.astype(np.float32)
for i in range(len(ids)):
id = ids[i]
label = labels[i]
if id not in res:
res[id] = dict()
if label not in res[id]:
res[id][label] = []
box = [xmin[i], ymin[i], xmax[i], ymax[i]]
res[id][label].append(box)
return res
def get_detections(table):
res = dict()
ids = table['ImageID'].values.astype(np.str)
labels = table['LabelName'].values.astype(np.str)
scores = table['Conf'].values.astype(np.float32)
xmin = table['XMin'].values.astype(np.float32)
xmax = table['XMax'].values.astype(np.float32)
ymin = table['YMin'].values.astype(np.float32)
ymax = table['YMax'].values.astype(np.float32)
for i in range(len(ids)):
id = ids[i]
label = labels[i]
if id not in res:
res[id] = dict()
if label not in res[id]:
res[id][label] = []
box = [xmin[i], ymin[i], xmax[i], ymax[i], scores[i]]
res[id][label].append(box)
return res
def _compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves.
Code originally from https://github.com/rbgirshick/py-faster-rcnn.
# Arguments
recall: The recall curve (list).
precision: The precision curve (list).
# Returns
The average precision as computed in py-faster-rcnn.
"""
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], recall, [1.]))
mpre = np.concatenate(([0.], precision, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def mean_average_precision_for_boxes(ann, pred, iou_threshold=0.5, exclude_not_in_annotations=False, verbose=True):
"""
:param ann: path to CSV-file with annotations or numpy array of shape (N, 6)
:param pred: path to CSV-file with predictions (detections) or numpy array of shape (N, 7)
:param iou_threshold: IoU between boxes which count as 'match'. Default: 0.5
:param exclude_not_in_annotations: exclude image IDs which are not exist in annotations. Default: False
:param verbose: print detailed run info. Default: True
:return: tuple, where first value is mAP and second values is dict with AP for each class.
"""
if isinstance(ann, str):
valid = pd.read_csv(ann)
else:
valid = pd.DataFrame(ann, columns=['ImageID', 'LabelName', 'XMin', 'XMax', 'YMin', 'YMax'])
if isinstance(pred, str):
preds = pd.read_csv(pred)
else:
preds = pd.DataFrame(pred, columns=['ImageID', 'LabelName', 'Conf', 'XMin', 'XMax', 'YMin', 'YMax'])
ann_unique = valid['ImageID'].unique()
preds_unique = preds['ImageID'].unique()
if verbose:
print('Number of files in annotations: {}'.format(len(ann_unique)))
print('Number of files in predictions: {}'.format(len(preds_unique)))
# Exclude files not in annotations!
if exclude_not_in_annotations:
preds = preds[preds['ImageID'].isin(ann_unique)]
preds_unique = preds['ImageID'].unique()
if verbose:
print('Number of files in detection after reduction: {}'.format(len(preds_unique)))
unique_classes = valid['LabelName'].unique().astype(np.str)
if verbose:
print('Unique classes: {}'.format(len(unique_classes)))
all_detections = get_detections(preds)
all_annotations = get_real_annotations(valid)
if verbose:
print('Detections length: {}'.format(len(all_detections)))
print('Annotations length: {}'.format(len(all_annotations)))
average_precisions = {}
for zz, label in enumerate(sorted(unique_classes)):
# Negative class
if str(label) == 'nan':
continue
false_positives = []
true_positives = []
scores = []
num_annotations = 0.0
for i in range(len(ann_unique)):
detections = []
annotations = []
id = ann_unique[i]
if id in all_detections:
if label in all_detections[id]:
detections = all_detections[id][label]
if id in all_annotations:
if label in all_annotations[id]:
annotations = all_annotations[id][label]
if len(detections) == 0 and len(annotations) == 0:
continue
num_annotations += len(annotations)
detected_annotations = []
annotations = np.array(annotations, dtype=np.float64)
for d in detections:
scores.append(d[4])
if len(annotations) == 0:
false_positives.append(1)
true_positives.append(0)
continue
overlaps = compute_overlap(np.expand_dims(np.array(d, dtype=np.float64), axis=0), annotations)
assigned_annotation = np.argmax(overlaps, axis=1)
max_overlap = overlaps[0, assigned_annotation]
if max_overlap >= iou_threshold and assigned_annotation not in detected_annotations:
false_positives.append(0)
true_positives.append(1)
detected_annotations.append(assigned_annotation)
else:
false_positives.append(1)
true_positives.append(0)
if num_annotations == 0:
average_precisions[label] = 0, 0
continue
false_positives = np.array(false_positives)
true_positives = np.array(true_positives)
scores = np.array(scores)
# sort by score
indices = np.argsort(-scores)
false_positives = false_positives[indices]
true_positives = true_positives[indices]
# compute false positives and true positives
false_positives = np.cumsum(false_positives)
true_positives = np.cumsum(true_positives)
# compute recall and precision
recall = true_positives / num_annotations
precision = true_positives / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps)
# compute average precision
average_precision = _compute_ap(recall, precision)
average_precisions[label] = average_precision, num_annotations
if verbose:
s1 = "{:30s} | {:.6f} | {:7d}".format(label, average_precision, int(num_annotations))
print(s1)
present_classes = 0
precision = 0
for label, (average_precision, num_annotations) in average_precisions.items():
if num_annotations > 0:
present_classes += 1
precision += average_precision
if present_classes > 0:
mean_ap = precision / present_classes
else:
mean_ap = 0
if verbose:
print('mAP: {:.6f}'.format(mean_ap))
return mean_ap, average_precisions
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