'''
https://github.com/zzh8829/yolov3-tf2


MIT License

Copyright (c) 2019 Zihao Zhang

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''


from absl import logging
import numpy as np
import tensorflow as tf
import cv2

YOLOV3_LAYER_LIST = [
    'yolo_darknet',
    'yolo_conv_0',
    'yolo_output_0',
    'yolo_conv_1',
    'yolo_output_1',
    'yolo_conv_2',
    'yolo_output_2',
]

YOLOV3_TINY_LAYER_LIST = [
    'yolo_darknet',
    'yolo_conv_0',
    'yolo_output_0',
    'yolo_conv_1',
    'yolo_output_1',
]


def load_darknet_weights(model, weights_file, tiny=False):
    wf = open(weights_file, 'rb')
    major, minor, revision, seen, _ = np.fromfile(wf, dtype=np.int32, count=5)

    if tiny:
        layers = YOLOV3_TINY_LAYER_LIST
    else:
        layers = YOLOV3_LAYER_LIST

    for layer_name in layers:
        sub_model = model.get_layer(layer_name)
        for i, layer in enumerate(sub_model.layers):
            if not layer.name.startswith('conv2d'):
                continue
            batch_norm = None
            if i + 1 < len(sub_model.layers) and \
                    sub_model.layers[i + 1].name.startswith('batch_norm'):
                batch_norm = sub_model.layers[i + 1]

            logging.info("{}/{} {}".format(
                sub_model.name, layer.name, 'bn' if batch_norm else 'bias'))

            filters = layer.filters
            size = layer.kernel_size[0]
            in_dim = layer.get_input_shape_at(0)[-1]

            if batch_norm is None:
                conv_bias = np.fromfile(wf, dtype=np.float32, count=filters)
            else:
                # darknet [beta, gamma, mean, variance]
                bn_weights = np.fromfile(
                    wf, dtype=np.float32, count=4 * filters)
                # tf [gamma, beta, mean, variance]
                bn_weights = bn_weights.reshape((4, filters))[[1, 0, 2, 3]]

            # darknet shape (out_dim, in_dim, height, width)
            conv_shape = (filters, in_dim, size, size)
            conv_weights = np.fromfile(
                wf, dtype=np.float32, count=np.product(conv_shape))
            # tf shape (height, width, in_dim, out_dim)
            conv_weights = conv_weights.reshape(
                conv_shape).transpose([2, 3, 1, 0])

            if batch_norm is None:
                layer.set_weights([conv_weights, conv_bias])
            else:
                layer.set_weights([conv_weights])
                batch_norm.set_weights(bn_weights)

    assert len(wf.read()) == 0, 'failed to read all data'
    wf.close()


def broadcast_iou(box_1, box_2):
    # box_1: (..., (x1, y1, x2, y2))
    # box_2: (N, (x1, y1, x2, y2))

    # broadcast boxes
    box_1 = tf.expand_dims(box_1, -2)
    box_2 = tf.expand_dims(box_2, 0)
    # new_shape: (..., N, (x1, y1, x2, y2))
    new_shape = tf.broadcast_dynamic_shape(tf.shape(box_1), tf.shape(box_2))
    box_1 = tf.broadcast_to(box_1, new_shape)
    box_2 = tf.broadcast_to(box_2, new_shape)

    int_w = tf.maximum(tf.minimum(box_1[..., 2], box_2[..., 2]) -
                       tf.maximum(box_1[..., 0], box_2[..., 0]), 0)
    int_h = tf.maximum(tf.minimum(box_1[..., 3], box_2[..., 3]) -
                       tf.maximum(box_1[..., 1], box_2[..., 1]), 0)
    int_area = int_w * int_h
    box_1_area = (box_1[..., 2] - box_1[..., 0]) * \
        (box_1[..., 3] - box_1[..., 1])
    box_2_area = (box_2[..., 2] - box_2[..., 0]) * \
        (box_2[..., 3] - box_2[..., 1])
    return int_area / (box_1_area + box_2_area - int_area)


def draw_outputs(img, outputs, class_names):
    boxes, objectness, classes, nums = outputs
    boxes, objectness, classes, nums = boxes[0], objectness[0], classes[0], nums[0]
    wh = np.flip(img.shape[0:2])
    for i in range(nums):
        x1y1 = tuple((np.array(boxes[i][0:2]) * wh).astype(np.int32))
        x2y2 = tuple((np.array(boxes[i][2:4]) * wh).astype(np.int32))
        img = cv2.rectangle(img, x1y1, x2y2, (255, 0, 0), 2)
        img = cv2.putText(img, '{} {:.4f}'.format(
            class_names[int(classes[i])], objectness[i]),
            x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
    return img


def draw_labels(x, y, class_names):
    img = x.numpy()
    boxes, classes = tf.split(y, (4, 1), axis=-1)
    classes = classes[..., 0]
    wh = np.flip(img.shape[0:2])
    for i in range(len(boxes)):
        x1y1 = tuple((np.array(boxes[i][0:2]) * wh).astype(np.int32))
        x2y2 = tuple((np.array(boxes[i][2:4]) * wh).astype(np.int32))
        img = cv2.rectangle(img, x1y1, x2y2, (255, 0, 0), 2)
        img = cv2.putText(img, class_names[classes[i]],
                          x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL,
                          1, (0, 0, 255), 2)
    return img


def freeze_all(model, frozen=True):
    model.trainable = not frozen
    if isinstance(model, tf.keras.Model):
        for l in model.layers:
            freeze_all(l, frozen)