def compute_average_precision_per_class(num_true_cases, gt_boxes,
difficult_cases, prediction_file,
iou_threshold, use_2007_metric):
with open(prediction_file) as f:
image_ids = []
boxes = []
scores = []
for line in f:
t = line.rstrip().split(" ")
image_ids.append(t[0])
scores.append(float(t[1]))
box = torch.tensor([float(v) for v in t[2:]]).unsqueeze(0)
box -= 1.0 # convert to python format where indexes start from 0
boxes.append(box)
scores = np.array(scores)
sorted_indexes = np.argsort(-scores)
boxes = [boxes[i] for i in sorted_indexes]
image_ids = [image_ids[i] for i in sorted_indexes]
true_positive = np.zeros(len(image_ids))
false_positive = np.zeros(len(image_ids))
matched = set()
### there are so many image ids that are not in gt_boxes.... this must be an error...
#print(image_ids) ## this will return image ids in form of a string
if type(image_ids[0]) == str:
print("converting image ids to int instead of str")
image_ids = list(map(int, image_ids))
assert (type(image_ids[0]) == int)
for i, image_id in enumerate(image_ids):
box = boxes[i]
if image_id not in gt_boxes:
false_positive[i] = 1
print("image_id", image_id, "not in gt_boxes!!!! skipping....")
continue
gt_box = gt_boxes[image_id]
ious = box_utils.iou_of(box, gt_box)
max_iou = torch.max(ious).item()
max_arg = torch.argmax(ious).item()
if max_iou > iou_threshold:
if difficult_cases[image_id][max_arg] == 0:
if (image_id, max_arg) not in matched:
true_positive[i] = 1
matched.add((image_id, max_arg))
else:
false_positive[i] = 1
else:
false_positive[i] = 1
true_positive = true_positive.cumsum()
false_positive = false_positive.cumsum()
precision = true_positive / (true_positive + false_positive)
recall = true_positive / num_true_cases
print("Printing stats for class...")
print("true_positive", true_positive)
print("false_positive", false_positive)
print("precision is", precision)
print("recall is", recall)
if use_2007_metric:
return measurements.compute_voc2007_average_precision(
precision, recall)
else:
return measurements.compute_average_precision(precision, recall)
def compute_average_precision_class_agnostic(num_true_casess, gt_boxess,
difficult_casess, class_names,
iou_threshold, use_2007_metric):
import os
eval_path = '/nethome/jbang36/eva/eva_storage/custom_code'
final_true_positive = np.array([])
final_false_positive = np.array([])
for class_index, class_name in enumerate(class_names):
if class_index == 0: continue #background
print(class_index, class_name)
prediction_file = os.path.join(eval_path, f"det_test_{class_name}.txt")
num_true_cases = num_true_casess[class_index]
gt_boxes = gt_boxess[class_index]
difficult_cases = difficult_casess[class_index]
##### TODO: we can't just set false_positive[i] = 1, we have to do false_positive[i] += 1 because there can be multiple answers / mistakes in a given frame
##### TODO: I don't think VOC2007 measure took care of this because there is only one object per image....
with open(prediction_file) as f:
image_ids = []
boxes = []
scores = []
for line in f:
t = line.rstrip().split(" ")
image_ids.append(t[0])
scores.append(float(t[1]))
box = torch.tensor([float(v) for v in t[2:]]).unsqueeze(0)
box -= 1.0 # convert to python format where indexes start from 0
boxes.append(box)
scores = np.array(scores)
sorted_indexes = np.argsort(-scores)
boxes = [boxes[i] for i in sorted_indexes]
image_ids = [image_ids[i] for i in sorted_indexes]
true_positive = np.zeros(len(image_ids))
false_positive = np.zeros(len(image_ids))
matched = set()
### there are so many image ids that are not in gt_boxes.... this must be an error...
#print(image_ids) ## this will return image ids in form of a string
if type(image_ids[0]) == str:
print("converting image ids to int instead of str")
image_ids = list(map(int, image_ids))
assert (type(image_ids[0]) == int)
for i, image_id in enumerate(image_ids):
box = boxes[i]
if image_id not in gt_boxes:
false_positive[i] = 1
print(
f"image_id {image_id} not in gt_boxes!!!! added {len(gt_boxes)} to false_positive array"
)
continue
gt_box = gt_boxes[image_id]
ious = box_utils.iou_of(box, gt_box)
max_iou = torch.max(ious).item()
max_arg = torch.argmax(ious).item()
if max_iou > iou_threshold:
if difficult_cases[image_id][max_arg] == 0:
if (image_id, max_arg) not in matched:
true_positive[i] = 1
matched.add((image_id, max_arg))
else:
false_positive[i] = 1
else:
false_positive[i] = 1
final_true_positive = np.concatenate(
(final_true_positive, true_positive), axis=0)
final_false_positive = np.concatenate(
(final_false_positive, false_positive), axis=0)
final_true_positive = final_true_positive.cumsum()
final_false_positive = final_false_positive.cumsum()
precision = final_true_positive / (final_true_positive +
final_false_positive)
num_true = 0
for key in num_true_casess.keys():
num_true += num_true_casess[key]
recall = final_true_positive / (num_true)
"""
true_positive = true_positive.cumsum()
false_positive = false_positive.cumsum()
precision = true_positive / (true_positive + false_positive)
recall = true_positive / num_true_cases
"""
print("Printing stats for class...")
print("true_positive", true_positive)
print("false_positive", false_positive)
print("precision is", precision)
print("recall is", recall)
if use_2007_metric:
return measurements.compute_voc2007_average_precision(
precision, recall)
else:
return measurements.compute_average_precision(precision, recall)
def compute_average_precision_per_class_modified(num_true_cases, gt_boxes,
difficult_cases,
prediction_file,
iou_threshold,
use_2007_metric):
"""
This function is modified from compute_average_precision_per_class() by taking into account that multiple answers for each frame and multiple mistakes per each frame
is accounted for....
:param num_true_cases: number of true cases
:param gt_boxes: number of ground truth boxes
:param difficult_cases: whether it is a difficult case
:param prediction_file: saved prediction file
:param iou_threshold: iou_threshold needed to be considered a proposal box
:param use_2007_metric: whether to use voc 2007 metric
:return: average precision for a given class
"""
with open(prediction_file) as f:
image_ids = []
boxes = []
scores = []
for line in f:
t = line.rstrip().split(" ")
image_ids.append(t[0])
scores.append(float(t[1]))
box = torch.tensor([float(v) for v in t[2:]]).unsqueeze(0)
box -= 1.0 # convert to python format where indexes start from 0
boxes.append(box)
scores = np.array(scores)
sorted_indexes = np.argsort(-scores)
boxes = [boxes[i] for i in sorted_indexes]
image_ids = [image_ids[i] for i in sorted_indexes]
true_positive = np.zeros(len(image_ids))
false_positive = np.zeros(len(image_ids))
matched = set()
### there are so many image ids that are not in gt_boxes.... this must be an error...
#print(image_ids) ## this will return image ids in form of a string
if type(image_ids[0]) == str:
print("converting image ids to int instead of str")
image_ids = list(map(int, image_ids))
assert (type(image_ids[0]) == int)
for i, image_id in enumerate(image_ids):
box = boxes[i]
if image_id not in gt_boxes:
false_positive[i] += len(gt_boxes)
#false_positive[i] = 1
print("image_id", image_id, "not in gt_boxes!!!! skipping....")
continue
gt_box = gt_boxes[image_id]
ious = box_utils.iou_of(box, gt_box)
max_iou = torch.max(ious).item()
max_arg = torch.argmax(ious).item()
if max_iou > iou_threshold:
if difficult_cases[image_id][max_arg] == 0:
if (image_id, max_arg) not in matched:
true_positive[i] += 1
#true_positive[i] = 1
matched.add((image_id, max_arg))
else:
false_positive[i] += 1
#false_positive[i] = 1
else:
#false_positive[i] = 1
false_positive[i] += 1
print("before cum sum")
print(len(true_positive))
print(len(false_positive))
print(true_positive)
print(false_positive)
print("---------------------")
true_positive = true_positive.cumsum()
false_positive = false_positive.cumsum()
precision = true_positive / (true_positive + false_positive)
recall = true_positive / num_true_cases
print("Printing stats for class...")
print("true_positive", true_positive)
print("false_positive", false_positive)
print("precision is", precision)
print("recall is", recall)
if use_2007_metric:
return measurements.compute_voc2007_average_precision(
precision, recall)
else:
return measurements.compute_average_precision(precision, recall)
