又想作弊了#

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GPU=0
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CUDNN=0
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OPENCV=1
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OPENMP=1

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lib = CDLL("/home/pi/cn_dect/chineseocr/darknet/libdarknet.so", RTLD_GLOBAL)

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>>> boxes  = concatenate(boxes, axis=0)
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Traceback (most recent call last):
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  File "<stdin>", line 1, in <module>
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  File "/home/pi/cn_dect/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/merge.py", line 687, in concatenate
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    return Concatenate(axis=axis, **kwargs)(inputs)
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  File "/home/pi/cn_dect/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 630, in __call__
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    base_layer_utils.create_keras_history(inputs)
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  File "/home/pi/cn_dect/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py", line 199, in create_keras_history
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    _, created_layers = _create_keras_history_helper(tensors, set(), [])
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  File "/home/pi/cn_dect/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py", line 245, in _create_keras_history_helper
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    layer_inputs, processed_ops, created_layers)
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  File "/home/pi/cn_dect/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py", line 245, in _create_keras_history_helper
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    layer_inputs, processed_ops, created_layers)
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  File "/home/pi/cn_dect/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py", line 245, in _create_keras_history_helper
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    layer_inputs, processed_ops, created_layers)
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  [Previous line repeated 2 more times]
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  File "/home/pi/cn_dect/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py", line 243, in _create_keras_history_helper
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    constants[i] = backend.function([], op_input)([])
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  File "/home/pi/cn_dect/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py", line 3349, in __call__
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    run_metadata=self.run_metadata)
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  File "/home/pi/cn_dect/tf/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1450, in __call__
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    run_metadata_ptr)
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tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_367' with dtype float and shape [2]
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         [[{{node Placeholder_367}}]]

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import sys
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sys.path.append(r'~/cn_dect/chineseocr')
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#sys.path.append(r'~/PycharmProjects/chineseocr')
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from config import *
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os.environ["CUDA_VISIBLE_DEVICES"] = ''
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scale,maxScale = IMGSIZE[0],2048
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from text.keras_yolo3 import yolo_text,box_layer,K
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import tensorflow as tf
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import numpy as np
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graph = tf.get_default_graph()##解决web.py 相关报错问题
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anchors = [float(x) for x in keras_anchors.split(',')]
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anchors = np.array(anchors).reshape(-1, 2)
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num_anchors = len(anchors)
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num_classes = len(class_names)
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K.clear_session()
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tf.reset_default_graph()
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textModel = yolo_text(num_classes,anchors)
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textModel.load_weights(kerasTextModel)
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#textModel.load_weights(r'~/PycharmProjects/text.h5')
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sess = K.get_session()
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image_shape = K.placeholder(shape=(2, ))##图像原尺寸:h,w
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input_shape = K.placeholder(shape=(2, ))##图像resize尺寸:h,w
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y1,y2,y3 = [*textModel.output]
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out = [y1,y2,y3]
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num_layers = len(out)
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anchor_mask = [[6,7,8], [3,4,5], [0,1,2]]
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boxes = []
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scores =[]
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input_shape = K.cast(input_shape, tf.float32)
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image_shape = K.cast(image_shape, tf.float32)
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#from keras.utils import plot_model
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#plot_model(textModel, to_file='model.png')
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def yolo_head(feats, anchors, num_classes, input_shape, calc_loss=False):
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    """Convert final layer features to bounding box parameters."""
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    num_anchors = len(anchors)
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    # Reshape to batch, height, width, num_anchors, box_params.
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    anchors_tensor = K.reshape(K.constant(anchors), [1, 1, 1, num_anchors, 2])
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    grid_shape = K.shape(feats)[1:3] # height, width
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    grid_y =tf.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
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        [1, grid_shape[1], 1, 1])
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    grid_x =tf.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
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        [grid_shape[0], 1, 1, 1])
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    grid = K.concatenate([grid_x, grid_y])
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    grid = K.cast(grid, K.dtype(feats))
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    feats = K.reshape(
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        feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])
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    # Adjust preditions to each spatial grid point and anchor size.
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    box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[::-1], K.dtype(feats))
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    box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats))
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    box_confidence = K.sigmoid(feats[..., 4:5])
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    box_class_probs = K.sigmoid(feats[..., 5:])
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    if calc_loss == True:
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        return grid, feats, box_xy, box_wh
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    return box_xy, box_wh, box_confidence, box_class_probs
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for lay in range(num_layers):
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    box_xy, box_wh, box_confidence, box_class_probs = yolo_head(out[lay],anchors[anchor_mask[lay]], num_classes, input_shape)
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    #box_xy = (box_xy - offset) * scale
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    #box_wh = box_wh*scale
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    box_score = box_confidence * box_class_probs
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    box_score = K.reshape(box_score, [-1, num_classes])
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    box_mins  = box_xy - (box_wh / 2.)
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    box_maxes = box_xy + (box_wh / 2.)
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    box =  K.concatenate([
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                    box_mins[..., 0:1],  # xmin
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                    box_mins[..., 1:2],  # ymin
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                    box_maxes[..., 0:1], # xmax
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                    box_maxes[..., 1:2]  # ymax
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                ],axis=-1)
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    box = K.reshape(box, [-1, 4])
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    boxes.append(box)
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    scores.append(box_score)
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concatenate        = tf.keras.layers.concatenate
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boxes  = concatenate(boxes, axis=0)

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proxychains4 wget -O opencv.zip https://github.com/opencv/opencv/archive/4.1.1.zip
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proxychains4 wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.1.1.zip
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unzip opencv.zip
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unzip opencv_contrib.zip
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mv opencv-4.1.1 opencv
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mv opencv_contrib-4.1.1 opencv_contrib