<gh_stars>1000+
#! /usr/bin/env python
# coding=utf-8
#================================================================
#   Copyright (C) 2019 * Ltd. All rights reserved.
#
#   Editor      : VIM
#   File name   : utils.py
#   Author      : YunYang1994
#   Created date: 2019-10-19 23:04:24
#   Description :
#
#================================================================

import cv2
import numpy as np
import tensorflow as tf

grid_h = 45
grid_w = 60

# # original paper anchors
# wandhG = np.array([[100.0, 100.0], [300.0, 300.0], [500.0, 500.0],
                   # [200.0, 100.0], [370.0, 185.0], [440.0, 220.0],
                   # [100.0, 200.0], [185.0, 370.0], [220.0, 440.0]])

# k-means anchors (recommend)
wandhG = np.array([[ 74., 149.],
                   [ 34., 149.],
                   [ 86.,  74.],
                   [109., 132.],
                   [172., 183.],
                   [103., 229.],
                   [149.,  91.],
                   [ 51., 132.],
                   [ 57., 200.]], dtype=np.float32)

def compute_iou(boxes1, boxes2):
    """(xmin, ymin, xmax, ymax)
    boxes1 shape:  [-1, 4], boxes2 shape: [-1, 4]
    """
    left_up = np.maximum(boxes1[..., :2], boxes2[..., :2], )
    right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:])
    inter_wh = np.maximum(right_down - left_up, 0.0)
    inter_area = inter_wh[..., 0] * inter_wh[..., 1]

    boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
    boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])

    union_area = boxes1_area + boxes2_area - inter_area
    ious = inter_area / union_area
    return ious

def plot_boxes_on_image(show_image_with_boxes, boxes, color=[0, 0, 255], thickness=2):
    for box in boxes:
        cv2.rectangle(show_image_with_boxes,
                pt1=(int(box[0]), int(box[1])),
                pt2=(int(box[2]), int(box[3])), color=color, thickness=thickness)
    show_image_with_boxes = cv2.cvtColor(show_image_with_boxes, cv2.COLOR_BGR2RGB)
    return show_image_with_boxes

def load_gt_boxes(path):
    """
    Don't care about what shit it is. whatever, this function
    returns many ground truth boxes with the shape of [-1, 4].

    xmin, ymin, xmax, ymax
    """
    bbs = open(path).readlines()[1:]
    roi = np.zeros([len(bbs), 4])
    for iter_, bb in zip(range(len(bbs)), bbs):
        bb = bb.replace('\n', '').split(' ')
        bbtype = bb[0]
        bba = np.array([float(bb[i]) for i in range(1, 5)])
        # occ = float(bb[5])
        # bbv = np.array([float(bb[i]) for i in range(6, 10)])
        ignore = int(bb[10])

        ignore = ignore or (bbtype != 'person')
        ignore = ignore or (bba[3] < 40)
        bba[2] += bba[0]
        bba[3] += bba[1]

        roi[iter_, :4] = bba
    return roi

def compute_regression(box1, box2):
    """
    box1: ground-truth boxes
    box2: anchor boxes
    """
    target_reg = np.zeros(shape=[4,])
    w1 = box1[2] - box1[0]
    h1 = box1[3] - box1[1]
    w2 = box2[2] - box2[0]
    h2 = box2[3] - box2[1]

    target_reg[0] = (box1[0] - box2[0]) / w2
    target_reg[1] = (box1[1] - box2[1]) / h2
    target_reg[2] = np.log(w1 / w2)
    target_reg[3] = np.log(h1 / h2)

    return target_reg

def decode_output(pred_bboxes, pred_scores, score_thresh=0.5):
    """
    pred_bboxes shape: [1, 45, 60, 9, 4]
    pred_scores shape: [1, 45, 60, 9, 2]
    """
    grid_x, grid_y = tf.range(60, dtype=tf.int32), tf.range(45, dtype=tf.int32)
    grid_x, grid_y = tf.meshgrid(grid_x, grid_y)
    grid_x, grid_y = tf.expand_dims(grid_x, -1), tf.expand_dims(grid_y, -1)
    grid_xy = tf.stack([grid_x, grid_y], axis=-1)
    center_xy = grid_xy * 16 + 8
    center_xy = tf.cast(center_xy, tf.float32)
    anchor_xymin = center_xy - 0.5 * wandhG

    xy_min = pred_bboxes[..., 0:2] * wandhG[:, 0:2] + anchor_xymin
    xy_max = tf.exp(pred_bboxes[..., 2:4]) * wandhG[:, 0:2] + xy_min

    pred_bboxes = tf.concat([xy_min, xy_max], axis=-1)
    pred_scores = pred_scores[..., 1]
    score_mask = pred_scores > score_thresh
    pred_bboxes = tf.reshape(pred_bboxes[score_mask], shape=[-1,4]).numpy()
    pred_scores = tf.reshape(pred_scores[score_mask], shape=[-1,]).numpy()
    return  pred_scores, pred_bboxes


def nms(pred_boxes, pred_score, iou_thresh):
    """
    pred_boxes shape: [-1, 4]
    pred_score shape: [-1,]
    """
    selected_boxes = []
    while len(pred_boxes) > 0:
        max_idx = np.argmax(pred_score)
        selected_box = pred_boxes[max_idx]
        selected_boxes.append(selected_box)
        pred_boxes = np.concatenate([pred_boxes[:max_idx], pred_boxes[max_idx+1:]])
        pred_score = np.concatenate([pred_score[:max_idx], pred_score[max_idx+1:]])
        ious = compute_iou(selected_box, pred_boxes)
        iou_mask = ious <= 0.1
        pred_boxes = pred_boxes[iou_mask]
        pred_score = pred_score[iou_mask]

    selected_boxes = np.array(selected_boxes)
    return selected_boxes


#! /usr/bin/env python
# coding=utf-8
#================================================================
#   Copyright (C) 2019 * Ltd. All rights reserved.
#
#   Editor      : VIM
#   File name   : utils.py
#   Author      : YunYang1994
#   Created date: 2019-10-19 23:04:24
#   Description :
#
#================================================================

import cv2
import numpy as np
import tensorflow as tf

grid_h = 45
grid_w = 60

# # original paper anchors
# wandhG = np.array([[100.0, 100.0], [300.0, 300.0], [500.0, 500.0],
                   # [200.0, 100.0], [370.0, 185.0], [440.0, 220.0],
                   # [100.0, 200.0], [185.0, 370.0], [220.0, 440.0]])

# k-means anchors (recommend)
wandhG = np.array([[ 74., 149.],
                   [ 34., 149.],
                   [ 86.,  74.],
                   [109., 132.],
                   [172., 183.],
                   [103., 229.],
                   [149.,  91.],
                   [ 51., 132.],
                   [ 57., 200.]], dtype=np.float32)

def compute_iou(boxes1, boxes2):
    """(xmin, ymin, xmax, ymax)
    boxes1 shape:  [-1, 4], boxes2 shape: [-1, 4]
    """
    left_up = np.maximum(boxes1[..., :2], boxes2[..., :2], )
    right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:])
    inter_wh = np.maximum(right_down - left_up, 0.0)
    inter_area = inter_wh[..., 0] * inter_wh[..., 1]

    boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
    boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])

    union_area = boxes1_area + boxes2_area - inter_area
    ious = inter_area / union_area
    return ious

def plot_boxes_on_image(show_image_with_boxes, boxes, color=[0, 0, 255], thickness=2):
    for box in boxes:
        cv2.rectangle(show_image_with_boxes,
                pt1=(int(box[0]), int(box[1])),
                pt2=(int(box[2]), int(box[3])), color=color, thickness=thickness)
    show_image_with_boxes = cv2.cvtColor(show_image_with_boxes, cv2.COLOR_BGR2RGB)
    return show_image_with_boxes

def load_gt_boxes(path):
    """
    Don't care about what shit it is. whatever, this function
    returns many ground truth boxes with the shape of [-1, 4].

    xmin, ymin, xmax, ymax
    """
    bbs = open(path).readlines()[1:]
    roi = np.zeros([len(bbs), 4])
    for iter_, bb in zip(range(len(bbs)), bbs):
        bb = bb.replace('\n', '').split(' ')
        bbtype = bb[0]
        bba = np.array([float(bb[i]) for i in range(1, 5)])
        # occ = float(bb[5])
        # bbv = np.array([float(bb[i]) for i in range(6, 10)])
        ignore = int(bb[10])

        ignore = ignore or (bbtype != 'person')
        ignore = ignore or (bba[3] < 40)
        bba[2] += bba[0]
        bba[3] += bba[1]

        roi[iter_, :4] = bba
    return roi

def compute_regression(box1, box2):
    """
    box1: ground-truth boxes
    box2: anchor boxes
    """
    target_reg = np.zeros(shape=[4,])
    w1 = box1[2] - box1[0]
    h1 = box1[3] - box1[1]
    w2 = box2[2] - box2[0]
    h2 = box2[3] - box2[1]

    target_reg[0] = (box1[0] - box2[0]) / w2
    target_reg[1] = (box1[1] - box2[1]) / h2
    target_reg[2] = np.log(w1 / w2)
    target_reg[3] = np.log(h1 / h2)

    return target_reg

def decode_output(pred_bboxes, pred_scores, score_thresh=0.5):
    """
    pred_bboxes shape: [1, 45, 60, 9, 4]
    pred_scores shape: [1, 45, 60, 9, 2]
    """
    grid_x, grid_y = tf.range(60, dtype=tf.int32), tf.range(45, dtype=tf.int32)
    grid_x, grid_y = tf.meshgrid(grid_x, grid_y)
    grid_x, grid_y = tf.expand_dims(grid_x, -1), tf.expand_dims(grid_y, -1)
    grid_xy = tf.stack([grid_x, grid_y], axis=-1)
    center_xy = grid_xy * 16 + 8
    center_xy = tf.cast(center_xy, tf.float32)
    anchor_xymin = center_xy - 0.5 * wandhG

    xy_min = pred_bboxes[..., 0:2] * wandhG[:, 0:2] + anchor_xymin
    xy_max = tf.exp(pred_bboxes[..., 2:4]) * wandhG[:, 0:2] + xy_min

    pred_bboxes = tf.concat([xy_min, xy_max], axis=-1)
    pred_scores = pred_scores[..., 1]
    score_mask = pred_scores > score_thresh
    pred_bboxes = tf.reshape(pred_bboxes[score_mask], shape=[-1,4]).numpy()
    pred_scores = tf.reshape(pred_scores[score_mask], shape=[-1,]).numpy()
    return  pred_scores, pred_bboxes


def nms(pred_boxes, pred_score, iou_thresh):
    """
    pred_boxes shape: [-1, 4]
    pred_score shape: [-1,]
    """
    selected_boxes = []
    while len(pred_boxes) > 0:
        max_idx = np.argmax(pred_score)
        selected_box = pred_boxes[max_idx]
        selected_boxes.append(selected_box)
        pred_boxes = np.concatenate([pred_boxes[:max_idx], pred_boxes[max_idx+1:]])
        pred_score = np.concatenate([pred_score[:max_idx], pred_score[max_idx+1:]])
        ious = compute_iou(selected_box, pred_boxes)
        iou_mask = ious <= 0.1
        pred_boxes = pred_boxes[iou_mask]
        pred_score = pred_score[iou_mask]

    selected_boxes = np.array(selected_boxes)
    return selected_boxes
#! /usr/bin/env python # coding=utf-8 #================================================================ #   Copyright (C) 2019 * Ltd. All rights reserved. # #   Editor      : VIM #   File name   : utils.py #   Author      : YunYang1994 #   Created date: 2019-10-19 23:04:24 #   Description : # #================================================================ # # original paper anchors # wandhG = np.array([[100.0, 100.0], [300.0, 300.0], [500.0, 500.0], # [200.0, 100.0], [370.0, 185.0], [440.0, 220.0], # [100.0, 200.0], [185.0, 370.0], [220.0, 440.0]]) # k-means anchors (recommend) (xmin, ymin, xmax, ymax)     boxes1 shape:  [-1, 4], boxes2 shape: [-1, 4] Don't care about what shit it is. whatever, this function     returns many ground truth boxes with the shape of [-1, 4].      xmin, ymin, xmax, ymax # occ = float(bb[5]) # bbv = np.array([float(bb[i]) for i in range(6, 10)]) box1: ground-truth boxes     box2: anchor boxes pred_bboxes shape: [1, 45, 60, 9, 4]     pred_scores shape: [1, 45, 60, 9, 2] pred_boxes shape: [-1, 4]     pred_score shape: [-1,]