def __init__(self, val_path, class_names):
self.class_names = class_names
self.xmlProcess = XMLProcess()
self.detection_samples = DetectionSample(val_path, class_names)
self.image_annotation_list = self.detection_samples.get_image_and_label_list(
val_path)
self.use_07_metric = False
def __init__(self, val_path, class_name, image_size=(416, 416)):
super().__init__()
self.image_size = image_size
self.detection_sample = DetectionSample(val_path, class_name, False)
self.detection_sample.read_sample()
self.image_process = ImageProcess()
self.dataset_process = DetectionDataSetProcess()
def __init__(self,
train_path,
class_name,
batch_size=1,
image_size=(768, 320),
multi_scale=False,
is_augment=False,
balanced_sample=False):
super().__init__()
self.className = class_name
self.multi_scale = multi_scale
self.is_augment = is_augment
self.balanced_sample = balanced_sample
self.batch_size = batch_size
self.image_size = image_size
self.detection_sample = DetectionSample(train_path, class_name,
balanced_sample)
self.detection_sample.read_sample()
self.xmlProcess = XMLProcess()
self.image_process = ImageProcess()
self.dataset_process = DetectionDataSetProcess()
self.dataset_augment = DetectionDataAugment()
self.nF = self.detection_sample.get_sample_count()
self.nB = math.ceil(self.nF / batch_size) # number of batches
def __init__(self, train_path):
self.xmlProcess = XMLProcess()
self.image_process = ImageProcess()
self.detection_sample = DetectionSample(train_path,
detect2d_config.className)
self.detection_sample.read_sample()
self.dataset_process = DetectionDataSetProcess()
class CreateDetectionAnchors():
def __init__(self, train_path):
self.xmlProcess = XMLProcess()
self.image_process = ImageProcess()
self.detection_sample = DetectionSample(train_path,
detect2d_config.className)
self.detection_sample.read_sample()
self.dataset_process = DetectionDataSetProcess()
def get_anchors(self, number):
wh_numpy = self.get_width_height()
# Kmeans calculation
k = cluster.vq.kmeans(wh_numpy, number)[0]
k = k[np.argsort(k.prod(1))] # sort small to large
# Measure IoUs
iou = np.stack([self.compute_iou(wh_numpy, x) for x in k], 0)
biou = iou.max(0)[0] # closest anchor IoU
print('Best possible recall: %.3f' %
(biou > 0.2635).float().mean()) # BPR (best possible recall)
# Print
print(
'kmeans anchors (n=%g, img_size=%g, IoU=%.2f/%.2f/%.2f-min/mean/best): '
% (number, detect2d_config.imgSize, biou.min(), iou.mean(),
biou.mean()),
end='')
for i, x in enumerate(k):
print('%i,%i' % (round(x[0]), round(x[1])),
end=', ' if i < len(k) - 1 else '\n')
def get_width_height(self):
count = self.detection_sample.get_sample_count()
result = []
for index in range(count):
img_path, label_path = self.detection_sample.get_sample_path(index)
src_image, rgb_image = self.image_process.readRgbImage(img_path)
_, _, boxes = self.xmlProcess.parseRectData(label_path)
rgb_image, labels = self.dataset_process.resize_dataset(
rgb_image, detect2d_config.imgSize, boxes,
detect2d_config.className)
temp = np.zeros((len(labels), 2), dtype=np.float32)
for index, object in enumerate(labels):
temp[index, :] = np.array([object.width(), object.height()])
result.append(temp)
return np.concatenate(result, axis=0)
def compute_iou(self, list_x, x2):
result = np.zeros((len(list_x), 1), dtype=np.float32)
for index, x1 in enumerate(list_x):
min_w = min(x1[0], x2[0])
min_h = min(x1[0], x2[1])
iou = (min_w * min_h) / (x1[0] * x1[1] + x2[0] * x2[1] -
min_w * min_h)
result[index] = iou
return result
class DetectionValDataLoader(data.Dataset):
def __init__(self, val_path, class_name, image_size=(416, 416)):
super().__init__()
self.image_size = image_size
self.detection_sample = DetectionSample(val_path, class_name, False)
self.detection_sample.read_sample()
self.image_process = ImageProcess()
self.dataset_process = DetectionDataSetProcess()
def __getitem__(self, index):
img_path, label_path = self.detection_sample.get_sample_path(index)
src_image, rgb_image = self.image_process.readRgbImage(img_path)
rgb_image, _ = self.dataset_process.resize_dataset(
rgb_image, self.image_size)
rgb_image, _ = self.dataset_process.normaliza_dataset(rgb_image)
rgb_image = self.dataset_process.numpy_to_torch(rgb_image, flag=0)
return img_path, src_image, rgb_image
def __len__(self):
return self.detection_sample.get_sample_count()
class CalculateMeanAp():
def __init__(self, val_path, class_names):
self.class_names = class_names
self.xmlProcess = XMLProcess()
self.detection_samples = DetectionSample(val_path, class_names)
self.image_annotation_list = self.detection_samples.get_image_and_label_list(
val_path)
self.use_07_metric = False
def eval(self, result_dir):
aps = []
ious = []
for i, name in enumerate(self.class_names):
if name == '__background__':
continue
file_path = os.path.join(result_dir, "%s.txt" % name)
recall, precision, ap = self.calculate_ap(file_path, name, 0.5)
aps += [ap]
# ious += [avg_iou]
self.print_evaluation(aps)
return np.mean(aps), aps
def print_evaluation(self, aps):
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for i, ap in enumerate(aps):
print(self.class_names[i] + ': ' + '{:.3f}'.format(ap))
# print(self.className[i] + '_iou: ' + '{:.3f}'.format(ious[aps.index(ap)]))
print('mAP: ' + '{:.3f}'.format(np.mean(aps)))
# print('Iou acc: ' + '{:.3f}'.format(np.mean(ious)))
print('~~~~~~~~')
def calculate_ap(self, result_path, class_name, iou_thresh=0.5):
if not os.path.exists(result_path):
return 0, 0, 0
recs = self.get_data_boxes()
class_recs, npos = self.get_gt_boxes(recs, class_name)
image_ids, sorted_scores, BB = self.get_detect_result(result_path)
tp, fp, iou = self.get_tp_fp(image_ids, class_recs, BB, iou_thresh)
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
recall = tp / float(npos)
# avg_iou = sum(iou) / len(iou)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
precision = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = self.get_ap(recall, precision)
return recall, precision, ap
def get_data_boxes(self):
recs = {}
for image_path, annotation_path in self.image_annotation_list:
path, filename_post = os.path.split(image_path)
#fileName, post = os.path.splitext(fileNameAndPost)
_, _, boxes = self.xmlProcess.parseRectData(annotation_path)
recs[filename_post] = boxes
return recs
def get_gt_boxes(self, recs, class_name):
# extract gt objects for this class
class_recs = {}
npos = 0
for imageName in recs.keys():
R = [box for box in recs[imageName] if box.name == class_name]
bbox = np.array([x.getVector() for x in R])
difficult = np.array([x.difficult for x in R]).astype(np.bool)
det = [False] * len(R)
npos = npos + sum(~difficult)
class_recs[imageName] = {
'bbox': bbox,
'difficult': difficult,
'det2d': det
}
return class_recs, npos
def get_detect_result(self, result_path):
# read dets
with open(result_path, 'r') as f:
lines = f.readlines()
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
return image_ids, sorted_scores, BB
def calculate_iou(self, BBGT, bb):
ovmax = -np.inf
jmax = None
if BBGT.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
return ovmax, jmax
def get_tp_fp(self, image_ids, class_recs, BB, iou_thresh):
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
iou = []
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
BBGT = R['bbox'].astype(float)
ovmax, jmax = self.calculate_iou(BBGT, bb)
if ovmax > iou_thresh:
if not R['difficult'][jmax]:
if not R['det2d'][jmax]:
tp[d] = 1.
R['det2d'][jmax] = 1
iou.append(ovmax)
else:
fp[d] = 1.
else:
fp[d] = 1.
return tp, fp, iou
def get_ap(self, recall, precision):
"""
ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if self.use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(recall >= t) == 0:
p = 0
else:
p = np.max(precision[recall >= t])
ap = ap + p / 11.
else:
# 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
class DetectionTrainDataloader(DataLoader):
def __init__(self,
train_path,
class_name,
batch_size=1,
image_size=(768, 320),
multi_scale=False,
is_augment=False,
balanced_sample=False):
super().__init__()
self.className = class_name
self.multi_scale = multi_scale
self.is_augment = is_augment
self.balanced_sample = balanced_sample
self.batch_size = batch_size
self.image_size = image_size
self.detection_sample = DetectionSample(train_path, class_name,
balanced_sample)
self.detection_sample.read_sample()
self.xmlProcess = XMLProcess()
self.image_process = ImageProcess()
self.dataset_process = DetectionDataSetProcess()
self.dataset_augment = DetectionDataAugment()
self.nF = self.detection_sample.get_sample_count()
self.nB = math.ceil(self.nF / batch_size) # number of batches
def __iter__(self):
self.count = -1
self.detection_sample.shuffle_sample()
return self
def __next__(self):
self.count += 1
if self.count == self.nB:
raise StopIteration
numpy_images = []
numpy_labels = []
class_index = self.get_random_class()
start_index = self.detection_sample.get_sample_start_index(
self.count, self.batch_size, class_index)
width, height = self.get_image_size()
stop_index = start_index + self.batch_size
for temp_index in range(start_index, stop_index):
img_path, label_path = self.detection_sample.get_sample_path(
temp_index, class_index)
src_image, rgb_image = self.image_process.readRgbImage(img_path)
_, _, boxes = self.xmlProcess.parseRectData(label_path)
rgb_image, labels = self.dataset_process.resize_dataset(
rgb_image, (width, height), boxes, self.className)
rgb_image, labels = self.dataset_augment.augment(rgb_image, labels)
rgb_image, labels = self.dataset_process.normaliza_dataset(
rgb_image, labels, (width, height))
labels = self.dataset_process.change_outside_labels(labels)
numpy_images.append(rgb_image)
torch_labels = self.dataset_process.numpy_to_torch(labels, flag=0)
numpy_labels.append(torch_labels)
numpy_images = np.stack(numpy_images)
torch_images = self.all_numpy_to_tensor(numpy_images)
return torch_images, numpy_labels
def __len__(self):
return self.nB # number of batches
def get_random_class(self):
class_index = None
if self.balanced_sample:
class_index = np.random.randint(0, len(self.className))
print("loading labels {}".format(self.className[class_index]))
return class_index
def get_image_size(self):
if self.multi_scale:
# Multi-Scale YOLO Training
print("wrong code for MultiScale")
width = random.choice(range(10, 20)) * 32 # 320 - 608 pixels
scale = float(self.image_size[0]) / float(self.image_size[1])
height = int(round(float(width / scale) / 32.0) * 32)
else:
# Fixed-Scale YOLO Training
width = self.image_size[0]
height = self.image_size[1]
return width, height