xBank-Code-AI/analysis_result/tools_analysis.py

def iou_result_two(result_dict_list,iou_para):

    result_0 = list(result_dict_list[0].values())[0]
    result_1 = list(result_dict_list[1].values())[0]
    label_0 = list(result_dict_list[0].keys())[0]
    label_1 = list(result_dict_list[1].keys())[0]

    return_lists = []

    for re_0 in result_0:

        for re_1 in result_1:

            iou = calculate_iou(re_0, re_1)

            if iou > iou_para:

                label_dict_0 = {label_0: re_0}
                label_dict_1 = {label_1: re_1}

                return_lists.append(label_dict_0)
                return_lists.append(label_dict_1)

    return return_lists


def calculate_iou(box1, box2):
    """
    计算两个边界框之间的IoU值

    参数:
    box1: 边界框1的坐标（x1, y1, x2, y2）
    box2: 边界框2的坐标（x1, y1, x2, y2）

    返回值:
    iou: 两个边界框之间的IoU值
    """
    x1 = max(box1[0], box2[0])
    y1 = max(box1[1], box2[1])
    x2 = min(box1[2], box2[2])
    y2 = min(box1[3], box2[3])

    # 计算交集区域面积
    intersection_area = max(0, x2 - x1 + 1) * max(0, y2 - y1 + 1)

    # 计算边界框1和边界框2的面积
    box1_area = (box1[2] - box1[0] + 1) * (box1[3] - box1[1] + 1)
    box2_area = (box2[2] - box2[0] + 1) * (box2[3] - box2[1] + 1)

    # 计算并集区域面积
    union_area = box1_area + box2_area - intersection_area

    # 计算IoU值
    iou = intersection_area / union_area

    return iou


def select_bbox_by_labels(example_list, result_dicts_list):
    '''
    将result列表中的example列表内的bbox挑选出来,放到同一字典下
    '''

    result_dict_list_change = []
    for label in example_list:
        bbox_list = []
        for result in result_dicts_list:
            if list(result.keys())[0] == label:
                bbox_list.append(list(result.values())[0])

        result_dict_change = {label: bbox_list}
        result_dict_list_change.append(result_dict_change)

    return result_dict_list_change