utils.py

"""
Help functions for SSD
"""

import cv2
import numpy as np


############## preprocess image ##################
# whiten the image
def whiten_image(image, means=(123., 117., 104.)):
    """Subtracts the given means from each image channel"""
    if image.ndim != 3:
        raise ValueError('Input must be of size [height, width, C>0]')
    num_channels = image.shape[-1]
    if len(means) != num_channels:
        raise ValueError('len(means) must match the number of channels')

    mean = np.array(means, dtype=image.dtype)
    image = image - mean
    return image

def resize_image(image, size=(300, 300)):
    return cv2.resize(image, size)

def preprocess_image(image):
    """Preprocess a image to inference"""
    image_cp = np.copy(image).astype(np.float32)
    # whiten the image
    image_whitened = whiten_image(image_cp)
    # resize the image
    image_resized = resize_image(image_whitened)
    # expand the batch_size dim
    image_expanded = np.expand_dims(image_resized, axis=0)
    return image_expanded

############## process bboxes ##################
def bboxes_clip(bbox_ref, bboxes):
    """Clip bounding boxes with respect to reference bbox.
    """
    bboxes = np.copy(bboxes)
    bboxes = np.transpose(bboxes)
    bbox_ref = np.transpose(bbox_ref)
    bboxes[0] = np.maximum(bboxes[0], bbox_ref[0])
    bboxes[1] = np.maximum(bboxes[1], bbox_ref[1])
    bboxes[2] = np.minimum(bboxes[2], bbox_ref[2])
    bboxes[3] = np.minimum(bboxes[3], bbox_ref[3])
    bboxes = np.transpose(bboxes)
    return bboxes

def bboxes_sort(classes, scores, bboxes, top_k=400):
    """Sort bounding boxes by decreasing order and keep only the top_k
    """
    # if priority_inside:
    #     inside = (bboxes[:, 0] > margin) & (bboxes[:, 1] > margin) & \
    #         (bboxes[:, 2] < 1-margin) & (bboxes[:, 3] < 1-margin)
    #     idxes = np.argsort(-scores)
    #     inside = inside[idxes]
    #     idxes = np.concatenate([idxes[inside], idxes[~inside]])
    idxes = np.argsort(-scores)
    classes = classes[idxes][:top_k]
    scores = scores[idxes][:top_k]
    bboxes = bboxes[idxes][:top_k]
    return classes, scores, bboxes

def bboxes_iou(bboxes1, bboxes2):
    """Computing iou between bboxes1 and bboxes2.
    Note: bboxes1 and bboxes2 can be multi-dimensional, but should broacastable.
    """
    bboxes1 = np.transpose(bboxes1)
    bboxes2 = np.transpose(bboxes2)
    # Intersection bbox and volume.
    int_ymin = np.maximum(bboxes1[0], bboxes2[0])
    int_xmin = np.maximum(bboxes1[1], bboxes2[1])
    int_ymax = np.minimum(bboxes1[2], bboxes2[2])
    int_xmax = np.minimum(bboxes1[3], bboxes2[3])

    int_h = np.maximum(int_ymax - int_ymin, 0.)
    int_w = np.maximum(int_xmax - int_xmin, 0.)
    int_vol = int_h * int_w
    # Union volume.
    vol1 = (bboxes1[2] - bboxes1[0]) * (bboxes1[3] - bboxes1[1])
    vol2 = (bboxes2[2] - bboxes2[0]) * (bboxes2[3] - bboxes2[1])
    iou = int_vol / (vol1 + vol2 - int_vol)
    return iou

def bboxes_nms(classes, scores, bboxes, nms_threshold=0.5):
    """Apply non-maximum selection to bounding boxes.
    """
    keep_bboxes = np.ones(scores.shape, dtype=np.bool)
    for i in range(scores.size-1):
        if keep_bboxes[i]:
            # Computer overlap with bboxes which are following.
            overlap = bboxes_iou(bboxes[i], bboxes[(i+1):])
            # Overlap threshold for keeping + checking part of the same class
            keep_overlap = np.logical_or(overlap < nms_threshold, classes[(i+1):] != classes[i])
            keep_bboxes[(i+1):] = np.logical_and(keep_bboxes[(i+1):], keep_overlap)

    idxes = np.where(keep_bboxes)
    return classes[idxes], scores[idxes], bboxes[idxes]

def bboxes_resize(bbox_ref, bboxes):
    """Resize bounding boxes based on a reference bounding box,
    assuming that the latter is [0, 0, 1, 1] after transform.
    """
    bboxes = np.copy(bboxes)
    # Translate.
    bboxes[:, 0] -= bbox_ref[0]
    bboxes[:, 1] -= bbox_ref[1]
    bboxes[:, 2] -= bbox_ref[0]
    bboxes[:, 3] -= bbox_ref[1]
    # Resize.
    resize = [bbox_ref[2] - bbox_ref[0], bbox_ref[3] - bbox_ref[1]]
    bboxes[:, 0] /= resize[0]
    bboxes[:, 1] /= resize[1]
    bboxes[:, 2] /= resize[0]
    bboxes[:, 3] /= resize[1]
    return bboxes

def process_bboxes(rclasses, rscores, rbboxes, rbbox_img = (0.0, 0.0, 1.0, 1.0),
                   top_k=400, nms_threshold=0.5):
    """Process the bboxes including sort and nms"""
    rbboxes = bboxes_clip(rbbox_img, rbboxes)
    rclasses, rscores, rbboxes = bboxes_sort(rclasses, rscores, rbboxes, top_k)
    rclasses, rscores, rbboxes = bboxes_nms(rclasses, rscores, rbboxes, nms_threshold)
    rbboxes = bboxes_resize(rbbox_img, rbboxes)
    return rclasses, rscores, rbboxes