model.py

from config import  *
from crnn import FullCrnn,LiteCrnn,CRNNHandle
from  psenet import  PSENet,PSENetHandel
from angle_class import  AangleClassHandle,shufflenet_v2_x0_5
from utils import  rotate_cut_img,solve,sort_box,draw_bbox,crop_rect
from PIL import Image
import numpy as np
import cv2



if  pse_model_type == "mobilenetv2":
    text_detect_net = PSENet(backbone=pse_model_type, pretrained=False, result_num=6, scale=pse_scale)


text_handle = PSENetHandel(pse_model_path, text_detect_net, pse_scale, gpu_id=GPU_ID)
crnn_net = None

if crnn_type == "full_lstm" or crnn_type == "full_dense":
    crnn_net  = FullCrnn(32, 1, len(alphabet) + 1, nh, n_rnn=2, leakyRelu=False, lstmFlag=LSTMFLAG)
elif crnn_type == "lite_lstm" or crnn_type == "lite_dense":
    crnn_net =  LiteCrnn(32, 1, len(alphabet) + 1, nh, n_rnn=2, leakyRelu=False, lstmFlag=LSTMFLAG)



assert  crnn_type is not None
crnn_handle  =  CRNNHandle(crnn_model_path , crnn_net , gpu_id=GPU_ID)

crnn_vertical_handle = None
if crnn_vertical_model_path is not None:
    crnn_vertical_net = LiteCrnn(32, 1, len(alphabet) + 1, nh, n_rnn=2, leakyRelu=False, lstmFlag=True)
    crnn_vertical_handle = CRNNHandle(crnn_vertical_model_path , crnn_vertical_net , gpu_id=GPU_ID)


assert angle_type in ["shufflenetv2_05"]



if angle_type == "shufflenetv2_05":
    angle_net = shufflenet_v2_x0_5(num_classes=len(lable_map_dict), pretrained=False)


angle_handle = AangleClassHandle(angle_model_path,angle_net,gpu_id=GPU_ID)



def crnnRec(im, rects_re, leftAdjust=False, rightAdjust=False, alph=0.2, f=1.0):
    """
    crnn模型，ocr识别
    @@model,
    @@converter,
    @@im:Array
    @@text_recs:text box
    @@ifIm:是否输出box对应的img

    """
    results = []
    im = Image.fromarray(im)
    for index, rect in enumerate(rects_re):

        degree, w, h, cx, cy = rect


        # partImg, newW, newH = rotate_cut_img(im,  90  + degree  , cx, cy, w, h, leftAdjust, rightAdjust, alph)
        partImg = crop_rect(im,  ((cx, cy ),(h, w),degree))
        newW,newH = partImg.size
        partImg_array  = np.uint8(partImg)

        #
        if newH > 1.5* newW:
            partImg_array = np.rot90(partImg_array,1)

        # partImg = Image.fromarray(partImg_array).convert("RGB")

        # partImg.save("./debug_im/{}.jpg".format(index))

        angel_index = angle_handle.predict(partImg_array)

        angel_class = lable_map_dict[angel_index]
        # print(angel_class)
        rotate_angle = rotae_map_dict[angel_class]


        if rotate_angle != 0 :
            partImg_array = np.rot90(partImg_array,rotate_angle//90)
        




        partImg = Image.fromarray(partImg_array).convert("RGB")
        #
        # partImg.save("./debug_im/{}.jpg".format(index))
        
        partImg_ = partImg.convert('L')

        try:

            if crnn_vertical_handle is not None and angel_class in ["shudao", "shuzhen"]:

                simPred =  crnn_vertical_handle.predict(partImg_)
            else:
                simPred = crnn_handle.predict(partImg_)  ##识别的文本
        except :
            continue

        if simPred.strip() != u'':
            results.append({'cx': cx * f, 'cy': cy * f, 'text': simPred, 'w': newW * f, 'h': newH * f,
                            'degree': degree })
    return results




def text_predict(img):

    # img = cv2.imread(imgpath)

    preds, boxes_list, rects_re, t = text_handle.predict(img, long_size=pse_long_size)


    img2 = draw_bbox(img, boxes_list, color=(0, 255, 0))
    cv2.imwrite("debug_im/draw.jpg", img2)

    result = crnnRec(np.array(img), rects_re)

    return result



if __name__ == "__main__":
    pass