import tensorflow as tf
graph = tf.get_default_graph()##解决web.py 相关报错问题
anchors = [float(x) for x in keras_anchors.split(',')]
anchors = np.array(anchors).reshape(-1, 2)
num_anchors = len(anchors)
num_classes = len(class_names)
textModel = yolo_text(num_classes,anchors)
textModel.load_weights(kerasTextModel)
sess = K.get_session()
image_shape = K.placeholder(shape=(2, ))##图像原尺寸:h,w
input_shape = K.placeholder(shape=(2, ))##图像resize尺寸:h,w
box_score = box_layer([*textModel.output,image_shape,input_shape],anchors, num_classes)
def text_detect(img,prob = 0.05):
im = Image.fromarray(img)
scale = IMGSIZE[0]
w,h = im.size
w_,h_ = resize_im(w,h, scale=scale, max_scale=2048)##短边固定为608,长边max_scale<4000
#boxed_image,f = letterbox_image(im, (w_,h_))
boxed_image = im.resize((w_,h_), Image.BICUBIC)
image_data = np.array(boxed_image, dtype='float32')
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
imgShape = np.array([[h,w]])
inputShape = np.array([[h_,w_]])
def net_output_process(batch_preds,
batch_shape,
batch_shape_padded,
prob=0.05):
"""
将主干网络的批输出转换为boxes,scores,这里方便兼容之前代码，
暂且使用for循环,以后可替换为vectorize处理
@params batch_preds(list of arrays):list长度代表n个采样尺度，其中每个\
array形状为(batch_size,grid_size_w,grid_size_h,3*(4+1+num_classes))
@params batch_shape(list of tuples):图片原始长宽
@params prob(float):置信度小于prob的box将被忽略
@returns batch_boxes(array): [字符区域数量，8]
@returns batch_scores:[字符区域数量,]
"""
batch_boxes = []
batch_scores = []
MAX_HORIZONTAL_GAP = 100
MIN_V_OVERLAPS = 0.6
MIN_SIZE_SIM = 0.6
textdetector = TextDetector(MAX_HORIZONTAL_GAP, MIN_V_OVERLAPS,
MIN_SIZE_SIM)
TEXT_PROPOSALS_MIN_SCORE = 0.1
TEXT_PROPOSALS_NMS_THRESH = 0.3
TEXT_LINE_NMS_THRESH = 0.99
LINE_MIN_SCORE = 0.1
leftAdjustAlph = 0.01
rightAdjustAlph = 0.01
# 首先初步对模型主干输出进行预处理
for y1, y2, y3, image_shape, input_shape in zip(batch_preds[0],
batch_preds[1],
batch_preds[2],
batch_shape,
batch_shape_padded):
outputs = [y1, y2, y3, image_shape, input_shape]
box, scores = box_layer(outputs, anchors, num_classes)
h, w = image_shape
keep = np.where(scores > prob)
# box[:, 0:4][box[:, 0:4]<0] = 0
box = np.array(box)
scores = np.array(scores)
box[box < 0] = 0
box[:, 0][box[:, 0] >= w] = w - 1
box[:, 1][box[:, 1] >= h] = h - 1
box[:, 2][box[:, 2] >= w] = w - 1
box[:, 3][box[:, 3] >= h] = h - 1
boxes = box[keep[0]]
scores = scores[keep[0]]
# 筛选出需要的box，并且进行nms，字符行组合
boxes, scores = textdetector.detect(boxes, scores[:, np.newaxis],
(h, w), TEXT_PROPOSALS_MIN_SCORE,
TEXT_PROPOSALS_NMS_THRESH,
TEXT_LINE_NMS_THRESH,
LINE_MIN_SCORE)
boxes = sort_box(boxes)
batch_boxes.append(boxes)
batch_scores.append(scores)
# print('done')
return batch_boxes, batch_scores