# --------------------------------------------------------  
# Fast R-CNN  
# Copyright (c) 2015 Microsoft  
# Licensed under The MIT License [see LICENSE for details]  
# Written by Ross Girshick  
# --------------------------------------------------------  
  
"""Compute minibatch blobs for training a Fast R-CNN network."""  
  
import numpy as np  
import numpy.random as npr  
import cv2  
from fast_rcnn.config import cfg  
from utils.blob import prep_im_for_blob, im_list_to_blob  
  
def get_minibatch(roidb, num_classes):  
    """Given a roidb, construct a minibatch sampled from it."""  
    num_images = len(roidb)  
    # Sample random scales to use for each image in this batch  
    random_scale_inds = npr.randint(0, high=len(cfg.TRAIN.SCALES),  
                                    size=num_images)#随机索引组成的numpy，大小是roidb的长度  
    assert(cfg.TRAIN.BATCH_SIZE % num_images == 0), \  
        ‘num_images ({}) must divide BATCH_SIZE ({})‘. \  
        format(num_images, cfg.TRAIN.BATCH_SIZE)  
    rois_per_image = cfg.TRAIN.BATCH_SIZE / num_images  #每张图的rois
    fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image) #目标rois 
  
    # Get the input image blob, formatted for caffe  
    im_blob, im_scales = _get_image_blob(roidb, random_scale_inds)  
  
    blobs = {‘data‘: im_blob}  
  
    if cfg.TRAIN.HAS_RPN:  #每个blobs包含图片中相应的box、gt_box信息
        assert len(im_scales) == 1, "Single batch only"  
        assert len(roidb) == 1, "Single batch only"  
        # gt boxes: (x1, y1, x2, y2, cls)  
        gt_inds = np.where(roidb[0][‘gt_classes‘] != 0)[0]  
        gt_boxes = np.empty((len(gt_inds), 5), dtype=np.float32)  
        gt_boxes[:, 0:4] = roidb[0][‘boxes‘][gt_inds, :] * im_scales[0]  
        gt_boxes[:, 4] = roidb[0][‘gt_classes‘][gt_inds]  
        blobs[‘gt_boxes‘] = gt_boxes  
        blobs[‘im_info‘] = np.array(  
            [[im_blob.shape[2], im_blob.shape[3], im_scales[0]]],  
            dtype=np.float32)  
    else: # not using RPN  
        # Now, build the region of interest and label blobs  
        rois_blob = np.zeros((0, 5), dtype=np.float32)  
        labels_blob = np.zeros((0), dtype=np.float32)  
        bbox_targets_blob = np.zeros((0, 4 * num_classes), dtype=np.float32)  
        bbox_inside_blob = np.zeros(bbox_targets_blob.shape, dtype=np.float32)  
        # all_overlaps = []  
        for im_i in xrange(num_images):  
            labels, overlaps, im_rois, bbox_targets, bbox_inside_weights \  
                = _sample_rois(roidb[im_i], fg_rois_per_image, rois_per_image,  
                               num_classes)  
  
            # Add to RoIs blob  
            rois = _project_im_rois(im_rois, im_scales[im_i])  
            batch_ind = im_i * np.ones((rois.shape[0], 1))  
            rois_blob_this_image = np.hstack((batch_ind, rois))  
            rois_blob = np.vstack((rois_blob, rois_blob_this_image))  
  
            # Add to labels, bbox targets, and bbox loss blobs  
            labels_blob = np.hstack((labels_blob, labels))  
            bbox_targets_blob = np.vstack((bbox_targets_blob, bbox_targets))  
            bbox_inside_blob = np.vstack((bbox_inside_blob, bbox_inside_weights))  
            # all_overlaps = np.hstack((all_overlaps, overlaps))  
  
        # For debug visualizations  
        # _vis_minibatch(im_blob, rois_blob, labels_blob, all_overlaps)  
  
        blobs[‘rois‘] = rois_blob  
        blobs[‘labels‘] = labels_blob  
  
        if cfg.TRAIN.BBOX_REG:  
            blobs[‘bbox_targets‘] = bbox_targets_blob  
            blobs[‘bbox_inside_weights‘] = bbox_inside_blob  
            blobs[‘bbox_outside_weights‘] = \  
                np.array(bbox_inside_blob > 0).astype(np.float32)  
  
    return blobs  
#随机生成前景和背景的RoIs  
def _sample_rois(roidb, fg_rois_per_image, rois_per_image, num_classes):  
    """Generate a random sample of RoIs comprising foreground and background 
    examples. 
    """  
    # label = class RoI has max overlap with  
    labels = roidb[‘max_classes‘]  
    overlaps = roidb[‘max_overlaps‘]  
    rois = roidb[‘boxes‘]  
  
    # Select foreground RoIs as those with >= FG_THRESH overlap  
    fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]  
    # Guard against the case when an image has fewer than fg_rois_per_image  
    # foreground RoIs  
    fg_rois_per_this_image = np.minimum(fg_rois_per_image, fg_inds.size)  
    # Sample foreground regions without replacement  
    if fg_inds.size > 0:  
        fg_inds = npr.choice(  
                fg_inds, size=fg_rois_per_this_image, replace=False)  
  
    # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)  
    bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &  
                       (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]  
    # Compute number of background RoIs to take from this image (guarding  
    # against there being fewer than desired)  
    bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image  
    bg_rois_per_this_image = np.minimum(bg_rois_per_this_image,  
                                        bg_inds.size)  
    # Sample foreground regions without replacement  
    if bg_inds.size > 0:  
        bg_inds = npr.choice(  
                bg_inds, size=bg_rois_per_this_image, replace=False)  
  
    # The indices that we‘re selecting (both fg and bg)  
    keep_inds = np.append(fg_inds, bg_inds)  
    # Select sampled values from various arrays:  
    labels = labels[keep_inds]  
    # Clamp labels for the background RoIs to 0  
    labels[fg_rois_per_this_image:] = 0  
    overlaps = overlaps[keep_inds]  
    rois = rois[keep_inds]  
  
    bbox_targets, bbox_inside_weights = _get_bbox_regression_labels(  
            roidb[‘bbox_targets‘][keep_inds, :], num_classes)  
  
    return labels, overlaps, rois, bbox_targets, bbox_inside_weights  
  #由相应尺度的roidb生成相应的blob
def _get_image_blob(roidb, scale_inds):  
    """Builds an input blob from the images in the roidb at the specified 
    scales. 
    """  
    num_images = len(roidb)  
    processed_ims = []  
    im_scales = []  
    for i in xrange(num_images):  
        im = cv2.imread(roidb[i][‘image‘])  
        if roidb[i][‘flipped‘]:  
            im = im[:, ::-1, :]  
        target_size = cfg.TRAIN.SCALES[scale_inds[i]]  
        im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,  
                                        cfg.TRAIN.MAX_SIZE)prep_im_for_blob： util的blob.py中；用于将图片平均后缩放。#im_scales： 每张图片的缩放率  
#  cfg.PIXEL_MEANS： 原始图片会集体减去该值达到mean  
        im_scales.append(im_scale)  
        processed_ims.append(im)  
  
    # Create a blob to hold the input images  
    blob = im_list_to_blob(processed_ims)#将以list形式存放的图片数据处理成(batch elem, channel, height, width)的im_blob形式，height，width用的是此次计算所有图片的最大值  
  
    return blob, im_scales#blob是一个字典，与name_to_top对应，方便把blob数据放进top  
  
def _project_im_rois(im_rois, im_scale_factor):  #图片缩放时，相应的rois也进行缩放
    """Project image RoIs into the rescaled training image."""  
    rois = im_rois * im_scale_factor  
    return rois  
  #由roidb返回相应的box及inside_weights
def _get_bbox_regression_labels(bbox_target_data, num_classes):  
    """Bounding-box regression targets are stored in a compact form in the 
    roidb. 
 
    This function expands those targets into the 4-of-4*K representation used 
    by the network (i.e. only one class has non-zero targets). The loss weights 
    are similarly expanded. 
 
    Returns: 
        bbox_target_data (ndarray): N x 4K blob of regression targets 
        bbox_inside_weights (ndarray): N x 4K blob of loss weights 
    """  
    clss = bbox_target_data[:, 0]  
    bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32)  
    bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)  
    inds = np.where(clss > 0)[0]  
    for ind in inds:  
        cls = clss[ind]  
        start = 4 * cls  
        end = start + 4  
        bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]  
        bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS  
    return bbox_targets, bbox_inside_weights  
  
def _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps):  
    """Visualize a mini-batch for debugging."""  
    import matplotlib.pyplot as plt  
    for i in xrange(rois_blob.shape[0]):  
        rois = rois_blob[i, :]  
        im_ind = rois[0]  
        roi = rois[1:]  
        im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy()  
        im += cfg.PIXEL_MEANS  
        im = im[:, :, (2, 1, 0)]  
        im = im.astype(np.uint8)  
        cls = labels_blob[i]  
        plt.imshow(im)  
        print ‘class: ‘, cls, ‘ overlap: ‘, overlaps[i]  
        plt.gca().add_patch(  
            plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0],  
                          roi[3] - roi[1], fill=False,  
                          edgecolor=‘r‘, linewidth=3)  
            )  
        plt.show()