# --------------------------------------------------------
# Multitask Network Cascade
# Modified from py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn)
# Copyright (c) 2016, Haozhi Qi
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------

"""Blob helper functions."""

import numpy as np
import random
import cv2
from utils.cython_bbox import bbox_overlaps
from mnc_config import cfg


def im_list_to_blob(ims):
    """
    Convert a list of images into a network input.
    Assumes images are already prepared (means subtracted, BGR order, ...).
    """
    max_shape = np.array([im.shape for im in ims]).max(axis=0)
    num_images = len(ims)
    blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
                    dtype=np.float32)
    for i in xrange(num_images):
        im = ims[i]
        blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
    # Move channels (axis 3) to axis 1
    # Axis order will become: (batch elem, channel, height, width)
    channel_swap = (0, 3, 1, 2)
    blob = blob.transpose(channel_swap)
    return blob


def prep_im_for_blob(im, pixel_means, target_size, max_size):
    """Mean subtract and scale an image for use in a blob."""
    im = im.astype(np.float32, copy=False)
    im -= pixel_means
    im_shape = im.shape
    im_size_min = np.min(im_shape[0:2])
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(target_size) / float(im_size_min)
    # Prevent the biggest axis from being more than MAX_SIZE
    if np.round(im_scale * im_size_max) > max_size:
        im_scale = float(max_size) / float(im_size_max)
    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
                    interpolation=cv2.INTER_LINEAR)

    return im, im_scale


def prep_im_for_blob_cfm(im, input_scales):
    """Converts an image into a network input.
    Arguments:
        im (ndarray): a color image in BGR order
    Returns:
        blob (ndarray): a data blob holding an image pyramid
        im_scale_factors (list): list of image scales (relative to im) used
            in the image pyramid
    """
    im_orig = im.astype(np.float32, copy=True)
    im_orig -= cfg.PIXEL_MEANS

    im_shape = im_orig.shape
    im_size_min = np.min(im_shape[0:2])
    im_size_max = np.max(im_shape[0:2])

    processed_ims = []
    im_scale_factors = []

    for target_size in input_scales:
        im_scale = float(target_size) / float(im_size_min)
        # Prevent the biggest axis from being more than MAX_SIZE
        if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
            im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
        im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
                        interpolation=cv2.INTER_LINEAR)
        im_scale_factors.append(im_scale)
        processed_ims.append(im)

    # Create a blob to hold the input images
    blob = im_list_to_blob(processed_ims)

    return blob, np.array(im_scale_factors)


def pred_rois_for_blob(im_rois, im_scales):
    """
    Convert rois to network input
    support multi-scale testing
    """
    im_rois = im_rois.astype(np.float, copy=False)
    if len(im_scales) > 1:
        widths = im_rois[:, 2] - im_rois[:, 0] + 1
        heights = im_rois[:, 3] - im_rois[:, 1] + 1

        areas = widths * heights
        scaled_areas = areas[:, np.newaxis] * (im_scales[np.newaxis, :] ** 2)
        diff_areas = np.abs(scaled_areas - 224 * 224)
        levels = diff_areas.argmin(axis=1)[:, np.newaxis]
    else:
        levels = np.zeros((im_rois.shape[0], 1), dtype=np.int)
    im_rois = im_rois * im_scales[levels]
    rois_blob = np.hstack((levels.astype(np.float), im_rois))
    return rois_blob

