def object2string(filepath, obj, label_table=None):
an_datum = pb.AnnotatedDatum()
an_datum.type = pb.AnnotatedDatum.BBOX
h, w = read_file_datum(filepath, an_datum.datum)
group = {}
for box in obj:
#cls = box['name']
if label_table is not None:
tm_name = box['label']
if not (is_number(tm_name) or is_char(tm_name)):
tm_name = '*'
if tm_name not in label_table:
label_table[tm_name] = len(label_table) + 1
cls = label_table[tm_name]
else:
cls = int(box['name']) + 1
if cls not in group:
group[cls] = []
group[cls].append(box)
for k, v in group.items():
g = an_datum.annotation_group.add()
g.group_label = int(k)
for i in range(len(v)):
instance = g.annotation.add()
instance.instance_id = i
bbox = instance.bbox
bbox.xmin = float(v[i]['xmin']) / w
bbox.ymin = float(v[i]['ymin']) / h
bbox.xmax = float(v[i]['xmax']) / w
bbox.ymax = float(v[i]['ymax']) / h
bbox.difficult = (int(v[i]['difficult']) <> 0)
bbox.angle = float(ssd_util.degree2angle(v[i]['degree'], w, h))
return an_datum.SerializeToString()
def process(args):
'''main '''
###START
# evalfile = "/train/execute/Tiny/testlabeled.txt"
# detailfile = "/train/execute/Tiny2/PR_detail.txt"
outsavedir = args.output_dir
detection = CaffeDetection(args.gpu, args.prototxt, args.model, args.size,
args.labelmap)
pred_conf_list = []
image_label_list = []
labelmaplist = get_labelmaplist(detection.labelmap)
if args.lmdb is None:
evalfile = args.image_list
filelist = np.array(pd.read_csv(evalfile, sep='\n',
header=None)).tolist()
for i, item in enumerate(tqdm(filelist)):
item_jpg = os.path.join(args.imgdir_add, item[0].split(' ')[0])
# print("dealing img:{}".format(item_jpg))
result = detection.detect(item_jpg)
image_label_list.append(item[0])
pred_conf_list.append(result)
else:
import lmdb
import cv2
lmdb_env = lmdb.open(args.lmdb, readonly=True)
with lmdb_env.begin() as lmdb_txn:
lmdb_cursor = lmdb_txn.cursor()
datum = caffe_pb2.AnnotatedDatum()
innerdatum = caffe_pb2.Datum()
for key, value in tqdm(lmdb_cursor):
datum.ParseFromString(value)
# imgarray = np.fromstring(datum.datum.data, dtype=np.uint8)
# imgarray = imgarray.reshape(datum.datum.channels, datum.datum.height, datum.datum.width)
data_array = np.asarray(bytearray(datum.datum.data),
dtype="uint8")
img_array = cv2.imdecode(data_array, cv2.IMREAD_COLOR)
# pdb.set_trace()
# innerdatum.ParseFromString(datum.datum)
# data_array = caffe.io.datum_to_array(datum.datum)[0]
img_array = img_array / 255.0
result = detection.detect(img_array)
imageitem = key.lstrip("00000000_")
image_label_list.append(imageitem)
pred_conf_list.append(result)
# lmdb_env.close()
if not os.path.exists(outsavedir):
os.mkdir(outsavedir)
with open(outsavedir + '/ssdpredfile.npz', 'wb') as scoreoutfile:
pickle.dump(labelmaplist, scoreoutfile, True)
pickle.dump(image_label_list, scoreoutfile, True)
pickle.dump(pred_conf_list, scoreoutfile, True)
print("data saved")
def open_lmdb(lmdb_path):
lmdb_env = lmdb.open(lmdb_path)
lmdb_txn = lmdb_env.begin() # 生成处理句柄
lmdb_cursor = lmdb_txn.cursor() # 生成迭代器指针
annotated_datum = caffe_pb2.AnnotatedDatum() # AnnotatedDatum结构
return lmdb_cursor, annotated_datum
def datum_read():
import lmdb
import numpy as np
import cv2
from caffe.proto import caffe_pb2
# /home/liuky/HDD_1/soft/caffe-ssd/examples/VOC0712/VOC0712_trainval_lmdb
# /home/liuky/HDD_1/data/tmp_lmdb/smoke_data_lmdb_300
lmdb_env = lmdb.open('/home/liuky/HDD_1/data/tmp_lmdb/test_data')
lmdb_txn = lmdb_env.begin() # 生成处理句柄
lmdb_cursor = lmdb_txn.cursor() # 生成迭代器指针
annotated_datum = caffe_pb2.AnnotatedDatum() # AnnotatedDatum结构
for idx, (key, value) in enumerate(lmdb_cursor):
# print(idx)
# continue
annotated_datum.ParseFromString(value)
datum = annotated_datum.datum # Datum结构
grps = annotated_datum.annotation_group # AnnotationGroup结构，一个group表示一个lebel类，每个group下又有复数个annotation表示检测框box
type = annotated_datum.type
for grp in grps:
label = grp.group_label
for annotation in grp.annotation:
instance_id = annotation.instance_id
xmin = annotation.bbox.xmin * datum.width # Annotation结构
ymin = annotation.bbox.ymin * datum.height
xmax = annotation.bbox.xmax * datum.width
ymax = annotation.bbox.ymax * datum.height
# Datum结构的label以及三个维度
_ = datum.label # 在目标检测的数据集中，这个label是没有意义的，真正的label是上面的group_label
channels = datum.channels
height = datum.height
width = datum.width
image_x = np.fromstring(datum.data, dtype=np.uint8) # 字符串转换为矩阵
if channels == 3:
image = cv2.imdecode(image_x, -1) # decode
cv2.imwrite('/home/liuky/HDD_1/tmp.jpg', image)
else:
img_datas = []
for channel_idx in range(channels):
start_idx = channel_idx * height * width
single_channel = image_x[start_idx:(start_idx +
height * width)]
single_channel = np.reshape(single_channel, (height, width))
img_datas.append(single_channel[:, :, np.newaxis])
image = np.concatenate(img_datas, -1)
tmp_img = image[:, :, [0]]
cv2.imwrite('/home/liuky/HDD_1/tmp.jpg',
cv2.resize(tmp_img, (1280, 720)))
print('')
def ReadLmdb(lmdb_path):
env = lmdb.open(lmdb_path)
txn = env.begin()
anno_datum = caffe_pb2.AnnotatedDatum()
for key, val in txn.cursor():
anno_datum.ParseFromString(val)
data = anno_datum.datum.data
print(key, anno_datum)
np_data = np.frombuffer(data, dtype=np.uint8)
cv_img = cv2.imdecode(np_data, -1)
cv2.imshow('test', cv_img)
cv2.waitKey(0)
env.close()
return
def anno2datum(img, bboxes):
if len(bboxes) == 0:
return
annotated_datum = caffe_pb2.AnnotatedDatum()
annotated_datum.type = annotated_datum.BBOX
datum = annotated_datum.datum
datum.channels = img.shape[2]
datum.width = img.shape[1]
datum.height = img.shape[0]
datum.encoded = True
datum.label = -1
datum.data = cv2.imencode('.jpg', img)[1].tobytes()
groups = annotated_datum.annotation_group
for bbox in bboxes:
found_group = False
instance_id = 0
label = int(bbox[4]) + 1 # background is 0
for group in groups:
if group.group_label == label:
if len(group.annotation) == 0:
instance_id = 0
else:
instance_id = len(group.annotation)
found_group = True
annotation = group.annotation.add()
break
if not found_group:
group = groups.add()
instance_id = 0
group.group_label = label
annotation = group.annotation.add()
annotation.instance_id = instance_id
annotation.bbox.xmin = bbox[0] * 1.0 / img.shape[1]
annotation.bbox.ymin = bbox[1] * 1.0 / img.shape[0]
annotation.bbox.xmax = bbox[2] * 1.0 / img.shape[1]
annotation.bbox.ymax = bbox[3] * 1.0 / img.shape[0]
return annotated_datum
# input preprocessing: 'data' is the name of the input blob == net.inputs[0]
print(net.blobs['data'].data.shape)
idx = 1
# if os.path.exists('/home/aiserver/result/word_dect/res_img/'):
# shutil.rmtree('/home/aiserver/result/word_dect/res_img/')
#
# os.mkdir('/home/aiserver/result/word_dect/res_img/')
lmdb_env = lmdb.open(
'/media/aiserver/disk2/word_dect/pipline_data_v2/lmdb/pipline_data_v2_test_lmdb'
)
lmdb_txn = lmdb_env.begin() # 生成处理句柄
lmdb_cursor = lmdb_txn.cursor() # 生成迭代器指针
annotated_datum = caffe_pb2.AnnotatedDatum() # AnnotatedDatum结构
#
# bei_x = 100000
# bei_y = 100000
# for bei_x in range()
bei_x = 0
bei_y = 0
idx = 0
for off_set in [2, 3, 4]:
for bei_xx in range(0, 1):
for bei_yy in [6, 7, 8, 9]:
res = open(
"---new_ptlogResNet256-lr0.001-100000-0.5-deconv-inputw900_390_re.txt",
"a")
analyze_scores = []
import lmdb
import caffe
import cv2
from caffe.proto import caffe_pb2
import numpy as np
lmdb_file = "/home/tharun/data/ILSVRC/lmdb/DET/ILSVRC2016_trainval1_lmdb_aug"
lmdb_env = lmdb.open(lmdb_file)
lmdb_txn = lmdb_env.begin()
lmdb_cursor = lmdb_txn.cursor()
datum = caffe_pb2.AnnotatedDatum()
for key, value in lmdb_cursor:
datum.ParseFromString(value)
data = datum.datum
grp = datum.annotation_group
arr = np.frombuffer(data.data, dtype='uint8')
img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
width = img.shape[1]
height = img.shape[0]
for annotation in grp:
for bbox in annotation.annotation:
cv2.rectangle(
img,
(int(bbox.bbox.xmin * width), int(bbox.bbox.ymin * height)),
(int(bbox.bbox.xmax * width), int(bbox.bbox.ymax * height)),
(0, 255, 0))
cv2.imshow('decoded image', img)
cv2.waitKey(3000)
break
def GenerateAnnotatedDatum(img_data, label_map, objs, width, height, img_label = -1):
'''
:param img_data: CHW
:param label_map: name map to label
:param objs: object dictionaries include xmin, ymin, xmax, ymax, name, difficult. See voc xml.
:param width: original image width, not resized
:param height: original image height, not resized
:param img_label: discarded
:return:
'''
if img_data.dtype != 'uint8':
print('Image data type must be unsigned byte!')
anno_datum = caffe_pb2.AnnotatedDatum()
anno_datum.type = 0
enc_data = cv2.imencode('.jpg', img_data)
anno_datum.datum.encoded = True
anno_datum.datum.data = np.array(enc_data[1]).tostring()
anno_datum.datum.channels = img_data.shape[2]
anno_datum.datum.height = img_data.shape[0]
anno_datum.datum.width = img_data.shape[1]
anno_datum.datum.label = img_label
# datum = caffe.io.array_to_datum(img_data, img_label)
# anno_datum.datum.channels = datum.channels
# anno_datum.datum.height = datum.height
# anno_datum.datum.width = datum.width
# anno_datum.datum.label = datum.label
# anno_datum.datum.data = datum.data
# anno_datum.datum.float_data.extend(datum.float_data)
# anno_datum.datum.encoded = True
for obj in objs:
group_label = label_map[obj['name']]
found_group = False
for anno_group in anno_datum.annotation_group:
if group_label == anno_group.group_label:
if len(anno_group.annotation) == 0:
instance_id = 0
else:
instance_id = anno_group.annotation[len(anno_group.annotation) - 1].instance_id + 1
anno = anno_group.annotation.add()
found_group = True
if not found_group:
anno_group = anno_datum.annotation_group.add()
anno_group.group_label = group_label
anno = anno_group.annotation.add()
instance_id = 0
anno.instance_id = instance_id
instance_id += 1
bbox = anno.bbox
if obj['xmin'] > width or obj['ymin'] > height or obj['xmax'] > width \
or obj['ymax'] > height or obj['xmin'] < 0 or obj['ymin'] < 0 or obj['xmax'] < 0 or obj['ymax'] < 0:
print('Bounding box exceeds image boundary.')
if obj['xmin'] > obj['xmax'] or obj['ymin'] > obj['ymax']:
print('Bounding box irregular.')
bbox.xmin = obj['xmin'] / width
bbox.ymin = obj['ymin'] / height
bbox.xmax = obj['xmax'] / width
bbox.ymax = obj['ymax'] / height
bbox.difficult = obj['difficult']
return anno_datum
def lmdb2image(args, show=False, gen_anchors=True, normalized=True):
data_dir = "data/" + args.dataset
lmdb_dir = data_dir + "/lmdb/" + args.split + "_lmdb"
if not os.path.exists(lmdb_dir):
print(lmdb_dir + " not exists")
return
db = lmdb.open(lmdb_dir)
txn = db.begin()
cursor = txn.cursor()
annotated_datum = caffe_pb2.AnnotatedDatum()
if gen_anchors:
data = []
labels = CLASSES[args.dataset]
statics = len(labels) * [0]
index = 0
num_images = txn.stat()['entries']
pbar = tqdm(range(num_images))
for key, value in cursor:
pbar.set_description("{}/{}".format(index, num_images))
pbar.update(1)
index += 1
annotated_datum.ParseFromString(value)
groups = annotated_datum.annotation_group
#print(len(groups))
if show or not normalized:
datum = annotated_datum.datum
img = np.fromstring(datum.data, dtype=np.uint8)
img = cv2.imdecode(img, -1)
height, width, _ = img.shape
for group in groups:
for annotation in group.annotation:
bbox = annotation.bbox
if bbox.xmax - bbox.xmin <= 0 or bbox.ymax - bbox.ymin <= 0:
continue
labelindex = group.group_label - 1
label = labels[labelindex] + "_" + str(annotation.instance_id)
statics[labelindex] += 1
if show or not normalized:
x1 = int(bbox.xmin * width)
y1 = int(bbox.ymin * height)
x2 = int(bbox.xmax * width)
y2 = int(bbox.ymax * height)
cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0))
cv2.putText(img, label, (x1, y1), 3, 1, (0, 0, 255))
if gen_anchors:
if normalized:
data.append(
[bbox.xmax - bbox.xmin, bbox.ymax - bbox.ymin])
else:
data.append([x2 - x1, y2 - y1])
if args.savegt:
filename = key.decode().replace("/", "_")
cv2.imwrite("output/gt/" + filename, img)
if show:
cv2.putText(img, key.decode(), (0, 20), 3, 1, (0, 0, 255))
cv2.imshow("img", img)
cv2.waitKey()
total = 0
for i, st in enumerate(statics):
total += st
print(labels[i] + ": " + str(st))
print("-------Total: " + str(total))
if gen_anchors:
from get_anchors import get_anchors
get_anchors(data)
