yolox_utils.py

import os

import numpy as np
import math
import ailia

import cv2

def preproc(img, input_size, swap=(2, 0, 1)):
    if len(img.shape) == 3:
        padded_img = np.ones((input_size[0], input_size[1], img.shape[2]), dtype=np.uint8) * 114
    else:
        padded_img = np.ones(input_size, dtype=np.uint8) * 114

    r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
    resized_img = cv2.resize(
        img,
        (int(img.shape[1] * r), int(img.shape[0] * r)),
        interpolation=cv2.INTER_LINEAR,
    ).astype(np.uint8)
    padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img

    padded_img = padded_img.transpose(swap)
    padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
    return padded_img, r


def nms(boxes, scores, nms_thr):
    """Single class NMS implemented in Numpy."""
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]

    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    order = scores.argsort()[::-1]

    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])

        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        ovr = inter / (areas[i] + areas[order[1:]] - inter)

        inds = np.where(ovr <= nms_thr)[0]
        order = order[inds + 1]

    return keep


def multiclass_nms(boxes, scores, nms_thr, score_thr, class_agnostic=True):
    """Multiclass NMS implemented in Numpy"""
    if class_agnostic:
        nms_method = multiclass_nms_class_agnostic
    else:
        nms_method = multiclass_nms_class_aware
    return nms_method(boxes, scores, nms_thr, score_thr)


def multiclass_nms_class_aware(boxes, scores, nms_thr, score_thr):
    """Multiclass NMS implemented in Numpy. Class-aware version."""
    final_dets = []
    num_classes = scores.shape[1]
    for cls_ind in range(num_classes):
        cls_scores = scores[:, cls_ind]
        valid_score_mask = cls_scores > score_thr
        if valid_score_mask.sum() == 0:
            continue
        else:
            valid_scores = cls_scores[valid_score_mask]
            valid_boxes = boxes[valid_score_mask]
            keep = nms(valid_boxes, valid_scores, nms_thr)
            if len(keep) > 0:
                cls_inds = np.ones((len(keep), 1)) * cls_ind
                dets = np.concatenate(
                    [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1
                )
                final_dets.append(dets)
    if len(final_dets) == 0:
        return None
    return np.concatenate(final_dets, 0)


def multiclass_nms_class_agnostic(boxes, scores, nms_thr, score_thr):
    """Multiclass NMS implemented in Numpy. Class-agnostic version."""
    cls_inds = scores.argmax(1)
    cls_scores = scores[np.arange(len(cls_inds)), cls_inds]

    valid_score_mask = cls_scores > score_thr
    if valid_score_mask.sum() == 0:
        return None
    valid_scores = cls_scores[valid_score_mask]
    valid_boxes = boxes[valid_score_mask]
    valid_cls_inds = cls_inds[valid_score_mask]
    keep = nms(valid_boxes, valid_scores, nms_thr)
    if keep:
        dets = np.concatenate(
            [valid_boxes[keep], valid_scores[keep, None], valid_cls_inds[keep, None]], 1
        )
    return dets


def postprocess(outputs, img_size, p6=False):

    grids = []
    expanded_strides = []

    if not p6:
        strides = [8, 16, 32]
    else:
        strides = [8, 16, 32, 64]

    hsizes = [img_size[0] // stride for stride in strides]
    wsizes = [img_size[1] // stride for stride in strides]

    for hsize, wsize, stride in zip(hsizes, wsizes, strides):
        xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
        grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
        grids.append(grid)
        shape = grid.shape[:2]
        expanded_strides.append(np.full((*shape, 1), stride))

    grids = np.concatenate(grids, 1)
    expanded_strides = np.concatenate(expanded_strides, 1)
    outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
    outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides

    return outputs


def predictions_to_object(predictions,raw_img,ratio,nms_thr,score_thr):
    boxes = predictions[:, :4]
    scores = predictions[:, 4:5] * predictions[:, 5:]

    boxes_xyxy = np.ones_like(boxes)
    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.
    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.
    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.
    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.
    boxes_xyxy /= ratio
    dets = multiclass_nms(boxes_xyxy, scores, nms_thr, score_thr)

    detect_object = []
    if dets is not None:
        img_size_h, img_size_w = raw_img.shape[:2]
        final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
        for i, box in enumerate(final_boxes):
            x1, y1, x2, y2 = box
            c = int(final_cls_inds[i])
            r = ailia.DetectorObject(
                category=c,
                prob=final_scores[i],
                x=x1 / img_size_w,
                y=y1 / img_size_h,
                w=(x2 - x1) / img_size_w,
                h=(y2 - y1) / img_size_h,
            )
            detect_object.append(r)

    return detect_object