def get_predict_result(image_set_path, result_set_path, model_path):
'''
对于给定的图片集，给出预测的数字串集合
:param image_set_path:
:param result_set_path:
:param model_path:
:return: 符合格式的数字串
'''
all_result_list = []
# 定义网络
num_anchors = len(anchors)
model_path = os.path.expanduser(model_path)
yolo_model = yolo_body(Input(shape=(None, None, 3)), num_anchors // 3, num_classes)
yolo_model.load_weights(model_path)
boxes, scores, classes = yolo_eval(yolo_model.output, anchors, num_classes, image_size, score_threshold, iou)
# 遍历文件夹下的每张图片
for file in os.listdir(image_set_path):
file_path = os.path.join(image_set_path, file)
image = Image.open(file_path)
boxed_image, scale, padding = letterbox_image(image, tuple(reversed(image_size)))
image_data = np.array(boxed_image, dtype='float32')
image_data /= 255.
image_data = np.expand_dims(image_data, 0)
# 送进网络做预测
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes], feed_dict={
yolo_model.input: image_data,
K.learning_phase(): 0
})
# 取出预测结果
pre_rec = []
pre_trust = []
pre_class = []
for i, _ in sorted(list(enumerate(out_boxes[:, 1])), key=lambda e: e[1]):
c = out_classes[i]
predicted_class = class_names[c]
box = out_boxes[i]
score = out_scores[i]
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
pre_rec.append((top, left, bottom, right))
pre_trust.append(score)
pre_class.append(predicted_class)
# 做类间极大值抑制
one_result_list, one_result_rec, one_result_trust = nms_inter_class(pre_rec, pre_trust, pre_class)
result_save_path = os.path.join(result_set_path, file)
one_result_str = file_path.split('\\')[-1][0:-4] + ':'
for i in range(len(one_result_list)):
if one_result_list[i] == 'number':
draw_box(image, one_result_rec[i], result_save_path, scale, padding)
continue
one_result_str += one_result_list[i]
all_result_list.append(one_result_str)
return all_result_list
def get_predict_result(image_set_path, result_set_path, model_path):
'''
对于给定的图片集，给出预测的数字串集合
:param image_set_path:
:param result_set_path:
:param model_path:
:return: 符合格式的数字串
'''
all_result_list = []
# 定义网络
sess = K.get_session()
num_anchors = len(anchors)
model_path = os.path.expanduser(model_path)
yolo_model = yolo_body(Input(shape=(None, None, 3)), num_anchors // 3,
num_classes)
yolo_model.load_weights(model_path)
boxes, scores, classes = yolo_eval(yolo_model.output, anchors, num_classes,
image_size, score_threshold, iou)
# 遍历文件夹下的每张图片
for file in os.listdir(image_set_path):
file_path = os.path.join(image_set_path, file)
image = Image.open(file_path)
boxed_image, scale, padding = letterbox_image(
image, tuple(reversed(image_size)))
image_data = np.array(boxed_image, dtype='float32')
image_data /= 255.
image_data = np.expand_dims(image_data, 0)
# 送进网络做预测
out_boxes, out_scores, out_classes = sess.run([boxes, scores, classes],
feed_dict={
yolo_model.input:
image_data,
K.learning_phase(): 0
})
# 取出预测结果
pre_rec, pre_trust, pre_class = [], [], []
for i, _ in sorted(list(enumerate(out_boxes[:, 1])),
key=lambda e: e[1]):
c = out_classes[i]
predicted_class = class_names[c]
box = out_boxes[i]
score = out_scores[i]
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
pre_rec.append((top, left, bottom, right))
pre_trust.append(score)
pre_class.append(predicted_class)
# print(pre_class)
# 做类间极大值抑制
one_result_list, one_result_rec, one_result_trust = nms_inter_class(
pre_rec, pre_trust, pre_class)
# 删除number
if 'number' in one_result_list:
index = one_result_list.index('number')
del one_result_list[index]
del one_result_rec[index]
del one_result_trust[index]
# 返回没有空格的predNum
tempArr = []
for i in range(len(one_result_list)):
tempArr.append(one_result_list[i])
one_result_list_blank = get_blank_space(one_result_list,
one_result_rec)
one_result_str = file_path.split('\\')[-1][0:-4] + ':'
# 标出整个数字串矩形框，非number的第一个标签和最后一个标签
# result_save_path = os.path.join(result_set_path, file)
# first_index = len(one_result_list)
# last_index = -1
# for i in range(len(one_result_list)):
# if one_result_list[i] != 'number':
# first_index = i if i < first_index else first_index
# last_index = i if i > last_index else last_index
# one_result_str += one_result_list[i]
#
# if first_index != len(one_result_list) and last_index != -1:
# first = one_result_rec[first_index]
# last = one_result_rec[last_index]
# top = first[0] if first[0] < last[0] else last[0]
# left = first[1] if first[1] < last[1] else last[1]
# bottom = first[2] if first[2] > last[2] else last[2]
# right = first[3] if first[3] > last[3] else last[3]
#
# rectagle = (top, left, bottom, right)
#
# draw_box(image, rectagle, result_save_path, scale, padding)
for i in range(len(one_result_list_blank)):
one_result_str += one_result_list_blank[i]
all_result_list.append(one_result_str)
return all_result_list
def get_test_label_accuracy(test_label_path, model_path):
non_digital_label = ['number', 'up', 'left', 'right', 'up', 'down', 'bottom']
pre_digital_num = 0
real_digital_num = 0
pre_whole_num = 0
real_whole_num = 0
# 定义网络
num_anchors = len(anchors)
model_path = os.path.expanduser(model_path)
yolo_model = yolo_body(Input(shape=(None, None, 3)), num_anchors // 3, num_classes)
yolo_model.load_weights(model_path)
boxes, scores, classes = yolo_eval(yolo_model.output, anchors, num_classes, image_size, score_threshold, iou)
with open(test_label_path, 'r') as f:
line = f.readline()
while line:
image_path = line.split(' ')[0]
print(image_path)
image = Image.open(image_path)
boxed_image, scale, padding = letterbox_image(image, tuple(reversed(image_size)))
image_data = np.array(boxed_image, dtype='float32')
image_data /= 255.
image_data = np.expand_dims(image_data, 0)
# 送进网络做预测
out_boxes, out_scores, out_classes = sess.run(
[boxes, scores, classes], feed_dict={
yolo_model.input: image_data,
K.learning_phase(): 0
})
# 取出预测结果
pre_rec = []
pre_trust = []
pre_class = []
for i, _ in sorted(list(enumerate(out_boxes[:, 1])), key=lambda e: e[1]):
c = out_classes[i]
predicted_class = class_names[c]
box = out_boxes[i]
score = out_scores[i]
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
pre_rec.append((top, left, bottom, right))
pre_trust.append(score)
pre_class.append(predicted_class)
# 做类间极大值抑制
one_result_list, one_result_rec, one_result_trust = nms_inter_class(pre_rec, pre_trust, pre_class)
# ['1','2','3']
name = image_path.split('/')[-1][:-4]
pre_digital_str = ''
for one in one_result_list:
if one not in non_digital_label:
pre_digital_str += one
# 读取标记数据
xml_path = '../dataset/annotation/' + name + '.xml'
real_digital_str = []
node_list = read_one_xml(xml_path)
for node in node_list:
if node.name not in non_digital_label:
real_digital_str.append(node.name)
flag = True
# 取两份文件里面的最小长度
length = min(len(pre_digital_str), len(real_digital_str))
for i in range(length):
if real_digital_str[i] == pre_digital_str[i]:
pre_digital_num += 1
else:
flag = False
# 说明整个图片上的数字都预测正确
if real_digital_str == pre_digital_str:
pre_whole_num += 1
line = f.readline()
real_whole_num += 1
real_digital_num += len(real_digital_str)
print('按数字算准确率为：{}'.format(float(pre_digital_num) / float(real_digital_num)))
print('按整张图片算准确率为：{}'.format(float(pre_whole_num) / float(real_whole_num)))
result_rec.append(pre_rec[i])
result_trust.append(pre_trust[i])
result_class.append(pre_class[i])
return result_class, result_rec, result_trust
model_path = 'C:\\Users\cyw35\Desktop\Bank_Card_OCR\card_recognize\weights\weights_2019_07_08_15_06_27.h5'
sess = K.get_session()
num_anchors = len(anchors)
model_path = os.path.expanduser(model_path)
yolo_model = yolo_body(Input(shape=(None, None, 3)), num_anchors // 3,
num_classes)
yolo_model.load_weights(model_path)
boxes, scores, classes = yolo_eval(yolo_model.output, anchors, num_classes,
image_size, score_threshold, iou)
def get_one_result(image, model_path):
'''
预测单张图片的卡号
:param image: 图片
:param model_path:权重模型路径
:return: 卡号list
'''
# 定义网络
# 转化图片格式
boxed_image, scale, padding = letterbox_image(image,
tuple(reversed(image_size)))
image_data = np.array(boxed_image, dtype='float32')