ssd_common.py

import cv2
from constants import image_size
import numpy as np

def draw_rect(I, r, c, thickness=1):
    if abs(sum(r)) < 100: # conditional to prevent min/max error
        cv2.rectangle(I, (int(r[0] * image_size), int(r[1] * image_size)),
                      (int((r[0] + max(r[2], 0)) * image_size), int((r[1] + max(r[3], 0)) * image_size)),
                      c, thickness)

def draw_ann(I, r, text, color=(255, 0, 255), confidence=-1):
    draw_rect(I, r, color, 1)
    cv2.rectangle(I, (int(r[0] * image_size), int(r[1] * image_size - 15)),
                  (int(r[0] * image_size + 100), int(r[1] * image_size)),
                  color, -1)

    text_ = text

    if confidence >= 0:
        text_ += ": %0.2f" % confidence

    cv2.putText(I, text_, (int(r[0] * image_size), int((r[1]) * image_size)),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))

def center2cornerbox(rect):
    return [rect[0] - rect[2]/2.0, rect[1] - rect[3]/2.0, rect[2], rect[3]]

def corner2centerbox(rect):
    return [rect[0] + rect[2]/2.0, rect[1] + rect[3]/2.0, rect[2], rect[3]]

def calc_intersection(r1, r2):
    left = max(r1[0], r2[0])
    right = min(r1[0] + r1[2], r2[0] + r2[2])
    bottom = min(r1[1] + r1[3], r2[1] + r2[3])
    top = max(r1[1], r2[1])

    if left < right and top < bottom:
        return (right - left) * (bottom - top)

    return 0

def clip_box(r):
    return [r[0], r[1], max(r[2], 0.01), max(r[3], 0.01)]

def calc_jaccard(r1, r2):
    r1_ = clip_box(r1)
    r2_ = clip_box(r2)
    intersection = calc_intersection(r1_, r2_)
    union = r1_[2] * r1_[3] + r2_[2] * r2_[3] - intersection

    if union <= 0:
        return 0

    j = intersection / union

    return j

def calc_overlap(r1, host):
    intersection = calc_intersection(r1, host)
    return intersection / (1e-5 + host[2] * host[3])

def non_max_suppression_fast(boxes, overlapThresh):
    # if there are no boxes, return an empty list
    if len(boxes) == 0:
        return []

    # if the bounding boxes integers, convert them to floats --
    # this is important since we'll be doing a bunch of divisions
    if boxes.dtype.kind == "i":
        boxes = boxes.astype("float")

    # initialize the list of picked indexes
    pick = []

    # grab the coordinates of the bounding boxes
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]

    # compute the area of the bounding boxes and sort the bounding
    # boxes by the bottom-right y-coordinate of the bounding box
    area = (x2 - x1 + 1) * (y2 - y1 + 1)
    idxs = np.argsort(y2)

    # keep looping while some indexes still remain in the indexes
    # list
    while len(idxs) > 0:
        # grab the last index in the indexes list and add the
        # index value to the list of picked indexes
        last = len(idxs) - 1
        i = idxs[last]
        pick.append(i)

        # find the largest (x, y) coordinates for the start of
        # the bounding box and the smallest (x, y) coordinates
        # for the end of the bounding box
        xx1 = np.maximum(x1[i], x1[idxs[:last]])
        yy1 = np.maximum(y1[i], y1[idxs[:last]])
        xx2 = np.minimum(x2[i], x2[idxs[:last]])
        yy2 = np.minimum(y2[i], y2[idxs[:last]])

        # compute the width and height of the bounding box
        w = np.maximum(0, xx2 - xx1 + 1)
        h = np.maximum(0, yy2 - yy1 + 1)

        # compute the ratio of overlap
        overlap = (w * h) / area[idxs[:last]]

        # delete all indexes from the index list that have
        idxs = np.delete(idxs, np.concatenate(([last],
                                               np.where(overlap > overlapThresh)[0])))

    # return only the bounding boxes that were picked using the
    # integer data type
    return pick