init project

+# -*- coding: utf-8 -*-
+###
+import os, sys
+import cv2
+from PIL import Image
+import time
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import backend as K
+from SDTR import alphabets
+
+characters = alphabets.alphabet[:]
+height = 32
+batchsize = 16
+nclass = len(characters) + 1
+
+
+def init():
+    global inputs, outs, sess
+    if tf.test.gpu_device_name():
+        modelPath = './SDTR/crnn_bl2_gpu.pb'
+        config = tf.ConfigProto()
+        config.gpu_options.per_process_gpu_memory_fraction = 0.3
+        config.gpu_options.allow_growth = True
+    else:
+        modelPath = './SDTR/crnn_bl2.pb'
+        config = tf.ConfigProto()
+    print(modelPath)
+    session = tf.Session(config=config)
+    graph = tf.Graph()
+    with graph.as_default():
+        graph_def = tf.GraphDef()
+        with tf.gfile.GFile(modelPath, 'rb') as f:
+            graph_def.ParseFromString(f.read())
+
+        inputs = tf.placeholder(tf.float32, [None, 32, None, 1], name='X')
+        outs = tf.import_graph_def(
+            graph_def,
+            input_map={'the_input:0': inputs},
+            return_elements=['embedding2/Reshape_1:0'])
+    sess = tf.Session(graph=graph, config=config)
+
+
+init()
+
+
+def predict(im, boxes):
+    # global inputs, outs, sess
+    count_boxes = len(boxes)
+    boxes_max = sorted(boxes,
+                   key=lambda box: int(32.0 * (np.linalg.norm(box[0] - box[1])) / (np.linalg.norm(box[3] - box[0]))),
+                   reverse=True)
+
+    if len(boxes) % batchsize != 0:
+        add_box = np.expand_dims(boxes[-1], axis=0)
+        extend_num = batchsize - len(boxes) % batchsize
+        for i in range(extend_num):
+            boxes = np.concatenate((boxes, add_box), axis=0)
+
+    results = {}
+    labels = []
+    rectime = 0.0
+
+    if len(boxes) is not 0:
+        for i in range(int(len(boxes) / batchsize)):
+            slices = []
+            box = boxes_max[i * batchsize]
+            w, h = [int(np.linalg.norm(box[0] - box[1])), int(np.linalg.norm(box[3] - box[0]))]
+            width = int(32.0 * w / h)
+            # print(width)
+            if width < 24:
+                continue
+            for index, box in enumerate(boxes[i * batchsize:(i + 1) * batchsize]):
+                _box = [n for a in box for n in a]
+                if i * batchsize + index < count_boxes:
+                    results[i * batchsize + index] = [np.array(_box)]
+                w, h = [int(np.linalg.norm(box[0] - box[1])), int(np.linalg.norm(box[3] - box[0]))]
+                # print(w)
+                pts1 = np.float32(box)
+                pts2 = np.float32([[0, 0], [w, 0], [w, h], [0, h]])
+                M = cv2.getPerspectiveTransform(pts1, pts2)
+                im_crop = cv2.warpPerspective(im, M, (w, h))
+                im_crop = resize_img(im_crop, width)
+                slices.append(im_crop)
+            slices = np.array(slices)
+            # print(slices.shape)
+            recstart = time.time()
+            preds = sess.run(outs, feed_dict={inputs: slices})
+            # preds=model.predict(slices)
+            recend = time.time()
+            preds = preds[0]
+            # print(preds)
+            rectime += (recend - recstart) * 1000
+            # preds=preds[:,2:,:]
+            rec_labels = decode(preds)
+            labels.extend(rec_labels)
+        for index, label in enumerate(labels[:count_boxes]):
+            results[index].append(label.replace(' ', '').replace('￥', '¥'))
+        return results, rectime
+
+
+def resize_img(im, width):
+    ori_h, ori_w = im.shape
+    ratio1 = width * 1.0 / ori_w
+    ratio2 = height * 1.0 / ori_h
+    if ratio1 < ratio2:
+        ratio = ratio1
+    else:
+        ratio = ratio2
+    new_w, new_h = int(ori_w * ratio), int(ori_h * ratio)
+    im = cv2.resize(im, (new_w, new_h))
+    delta_w = width - new_w
+    delta_h = height - new_h
+    top = delta_h // 2
+    bottom = delta_h - top
+    left = delta_w // 2
+    right = delta_w - left
+    img = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255)
+    img = img / 255.0
+    img = (img - 0.5) / 0.5
+    X = img.reshape((height, width, 1))
+    return X
+
+
+def decode(preds):
+    labels = []
+    charactersS = characters + u' '
+    tops = preds.argmax(axis=2)
+    for t in tops:
+        length = len(t)
+        char_list = []
+        for i in range(length):
+            if t[i] != 0 and (not (i > 0 and t[i - 1] == t[i])):
+                char_list.append(charactersS[t[i] - 1])
+        labels.append(u''.join(char_list))
+    return labels

+# -*- coding: utf-8 -*-
+###
+import cv2
+import numpy as np
+from SDTR import sdtr
+
+
+if __name__ == '__main__':
+    test_img_path = './rec_test.png'
+    test_img = cv2.imread(test_img_path)
+    test_gray = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
+    h, w = test_gray.shape
+    box = [np.array([[0, 0], [w, 0], [w, h], [0, h]])]
+    all_time = 0
+    rangetimes = 1001
+    for i in range(rangetimes):
+        results, rectime = sdtr.predict(test_gray, box)
+        print('{:.5f}ms'.format(rectime))
+        print(results)
+        if i != 0:
+            all_time += rectime
+    print('avgtime:{:.5f}ms'.format(all_time / (rangetimes - 1)))

+SDTR_v1.0:
+    功能：对文字切片进行识别, 返回识别结果
+    使用方法：
+        from SDTR import sdtr
+        sdtr.predict(im, boxes)
+    输入参数：
+        im:opencv下的灰度图
+        boxes:numpy矩阵，大小为N*4*2， N为box个数，每个box包含四个坐标（x,y），且坐标顺序必须为：左上，右上，右下，左下
+    输出：
+        一个字典，包含key:[numpy矩阵box，string识别结果]
+        eg. {0: [array([  0,   0, 626,   0, 626,  87,   0,  87]), '陆万壹仟叁佰圆整']}
+    环境：
+        tensorflow,最好是1.14版本，未在其他版本上测试
+
+
+    v1.0 2019.09.19
+    为CRNN基础
+    GPU版本用了CuDNNLSTM, 相比普通LSTM能减少1/2到2/3的时间
+
+    性能：
+        购车发票上平均全对率为93.25
+        
+        对box缩放到32的高度，一张32*230的图
+        GPU时间：15ms
+        CPU时间：210ms
+
+        具体性能和box数量和box缩放到32高度时的宽度有关