今天为了配合目标检测算法的特殊需要，我们要进行同样特殊的图像预处理
之前的预测是将图像转化为对应的rgb值，而今天这个在那之前需要一个图像拆分的过程
# coding=utf-8 import random class sampler(): def __init__(self, max_sample, max_trial, min_scale, max_scale, min_aspect_ratio, max_aspect_ratio, min_jaccard_overlap, max_jaccard_overlap): # 最多样本量 self.max_sample = max_sample # 最多实验量 self.max_trial = max_trial # 最小规模 self.min_scale = min_scale # 最大规模 self.max_scale = max_scale # 最小的纵横比 self.min_aspect_ratio = min_aspect_ratio # 最大的纵横比 self.max_aspect_ratio = max_aspect_ratio # 最小重叠部分 self.min_jaccard_overlap = min_jaccard_overlap # 最大重叠部分 self.max_jaccard_overlap = max_jaccard_overlap class bbox(): # 定义目标框的大小 def __init__(self, xmin, ymin, xmax, ymax): self.xmin = xmin self.ymin = ymin self.xmax = xmax self.ymax = ymax def bbox_area(src_bbox): # 计算目标框的面积 width = src_bbox.xmax - src_bbox.xmin height = src_bbox.ymax - src_bbox.ymin return width * height def generate_sample(sampler): scale = random.uniform(sampler.min_scale, sampler.max_scale) min_aspect_ratio = max(sampler.min_aspect_ratio, (scale**2.0)) max_aspect_ratio = min(sampler.max_aspect_ratio, 1 / (scale**2.0)) aspect_ratio = random.uniform(min_aspect_ratio, max_aspect_ratio) bbox_width = scale * (aspect_ratio**0.5) bbox_height = scale / (aspect_ratio**0.5) xmin_bound = 1 - bbox_width ymin_bound = 1 - bbox_height xmin = random.uniform(0, xmin_bound) ymin = random.uniform(0, ymin_bound) xmax = xmin + bbox_width ymax = ymin + bbox_height sampled_bbox = bbox(xmin, ymin, xmax, ymax) return sampled_bbox def jaccard_overlap(sample_bbox, object_bbox): if sample_bbox.xmin >= object_bbox.xmax or \ sample_bbox.xmax <= object_bbox.xmin or \ sample_bbox.ymin >= object_bbox.ymax or \ sample_bbox.ymax <= object_bbox.ymin: return 0 intersect_xmin = max(sample_bbox.xmin, object_bbox.xmin) intersect_ymin = max(sample_bbox.ymin, object_bbox.ymin) intersect_xmax = min(sample_bbox.xmax, object_bbox.xmax) intersect_ymax = min(sample_bbox.ymax, object_bbox.ymax) intersect_size = (intersect_xmax - intersect_xmin) * ( intersect_ymax - intersect_ymin) sample_bbox_size = bbox_area(sample_bbox) object_bbox_size = bbox_area(object_bbox) overlap = intersect_size / ( sample_bbox_size + object_bbox_size - intersect_size) return overlap def satisfy_sample_constraint(sampler, sample_bbox, bbox_labels): ''' 满足样品的约束 :param sampler: :param sample_bbox: :param bbox_labels: :return: ''' if sampler.min_jaccard_overlap == 0 and sampler.max_jaccard_overlap == 0: return True for i in range(len(bbox_labels)): object_bbox = bbox(bbox_labels[i][1], bbox_labels[i][2], bbox_labels[i][3], bbox_labels[i][4]) overlap = jaccard_overlap(sample_bbox, object_bbox) if sampler.min_jaccard_overlap != 0 and \ overlap < sampler.min_jaccard_overlap: continue if sampler.max_jaccard_overlap != 0 and \ overlap > sampler.max_jaccard_overlap: continue return True return False def generate_batch_samples(batch_sampler, bbox_labels): ''' 按照一个batch生成一个样本 :param batch_sampler: :param bbox_labels: :return: ''' sampled_bbox = [] index = [] c = 0 for sampler in batch_sampler: found = 0 # 获取最大的样本量 for i in range(sampler.max_trial): if found >= sampler.max_sample: break sample_bbox = generate_sample(sampler) if satisfy_sample_constraint(sampler, sample_bbox, bbox_labels): sampled_bbox.append(sample_bbox) found = found + 1 index.append(c) c = c + 1 return sampled_bbox def clip_bbox(src_bbox): # 修剪目标框的大小 src_bbox.xmin = max(min(src_bbox.xmin, 1.0), 0.0) src_bbox.ymin = max(min(src_bbox.ymin, 1.0), 0.0) src_bbox.xmax = max(min(src_bbox.xmax, 1.0), 0.0) src_bbox.ymax = max(min(src_bbox.ymax, 1.0), 0.0) return src_bbox def meet_emit_constraint(src_bbox, sample_bbox): center_x = (src_bbox.xmax + src_bbox.xmin) / 2 center_y = (src_bbox.ymax + src_bbox.ymin) / 2 if center_x >= sample_bbox.xmin and \ center_x <= sample_bbox.xmax and \ center_y >= sample_bbox.ymin and \ center_y <= sample_bbox.ymax: return True return False def transform_labels(bbox_labels, sample_bbox): proj_bbox = bbox(0, 0, 0, 0) sample_labels = [] for i in range(len(bbox_labels)): sample_label = [] object_bbox = bbox(bbox_labels[i][1], bbox_labels[i][2], bbox_labels[i][3], bbox_labels[i][4]) if not meet_emit_constraint(object_bbox, sample_bbox): continue sample_width = sample_bbox.xmax - sample_bbox.xmin sample_height = sample_bbox.ymax - sample_bbox.ymin proj_bbox.xmin = (object_bbox.xmin - sample_bbox.xmin) / sample_width proj_bbox.ymin = (object_bbox.ymin - sample_bbox.ymin) / sample_height proj_bbox.xmax = (object_bbox.xmax - sample_bbox.xmin) / sample_width proj_bbox.ymax = (object_bbox.ymax - sample_bbox.ymin) / sample_height proj_bbox = clip_bbox(proj_bbox) if bbox_area(proj_bbox) > 0: sample_label.append(bbox_labels[i][0]) sample_label.append(float(proj_bbox.xmin)) sample_label.append(float(proj_bbox.ymin)) sample_label.append(float(proj_bbox.xmax)) sample_label.append(float(proj_bbox.ymax)) sample_label.append(bbox_labels[i][5]) sample_labels.append(sample_label) return sample_labels def crop_image(img, bbox_labels, sample_bbox, image_width, image_height): ''' 裁剪图像 :param img: 图像 :param bbox_labels: 所有的标注信息 :param sample_bbox: 对应一个的标注信息 :param image_width: 图像原始的宽 :param image_height: 图像原始的高 :return:裁剪好的图像和其对应的标注信息 ''' sample_bbox = clip_bbox(sample_bbox) xmin = int(sample_bbox.xmin * image_width) xmax = int(sample_bbox.xmax * image_width) ymin = int(sample_bbox.ymin * image_height) ymax = int(sample_bbox.ymax * image_height) sample_img = img[ymin:ymax, xmin:xmax] sample_labels = transform_labels(bbox_labels, sample_bbox) return sample_img, sample_labels
