预测过程

预测过程中的detec_imaget函数，函数如下面三图
#保留原始图片信息
old_width = image_shape[1]
old_height = image_shape[0]
old_image = copy.deepcopy(image)
#对图片修剪，并进行归一化
width,height = get_new_img_size(old_width,old_height)
image = image.resize([width,height])
photo = np.array(image,dtype = np.float32)/255photo = np.transpose(photo, (2, 0, 1))
#把图片传入到模型中进行预测
images = []
images.append(photo)
images = np.asarray(images)
images = torch.from_numpy(images).cuda()

在该函数中有调用 DecodeBox()函数该函数如下两图
#防止框超出边缘
_# clip bounding box_cls_bbox[…, 0] = (cls_bbox[…, 0]).clamp(min=0, max=width)
cls_bbox[…, 2] = (cls_bbox[…, 2]).clamp(min=0, max=width)
cls_bbox[…, 1] = (cls_bbox[…, 1]).clamp(min=0, max=height)
cls_bbox[…, 3] = (cls_bbox[…, 3]).clamp(min=0, max=height)
#计算每一个类的概率（包含背景类 如：数据集有4个类，加上背景总共便为五个类）
prob = F.softmax(torch.tensor(roi_scores), dim=1)
raw_cls_bbox = cls_bbox.cpu().numpy()
raw_prob = prob.cpu().numpy()
取出属于这一类的框
cls_bbox_l = raw_cls_bbox[:, l, :]
prob_l = raw_prob[:, l]
判断是否大于score_thresh大于保留
mask = prob_l > score_thresh
cls_bbox_l = cls_bbox_l[mask]

画框的过程

     for i, c in enumerate(label):
        predicted_class = self.class_names[int(c)]
        score = conf[i]   
        left, top, right, bottom = bbox[i]
        top = top - 5
        left = left - 5
        bottom = bottom + 5
        right = right + 5
        top = max(0, np.floor(top + 0.5).astype('int32'))
        left = max(0, np.floor(left + 0.5).astype('int32'))
        bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
        right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))
        # 画框框
        label = '{} {:.2f}'.format(predicted_class, score)
        draw = ImageDraw.Draw(image)
        label_size = draw.textsize(label, font)
        label = label.encode('utf-8')
        print(label)
        if top - label_size[1] >= 0:
            text_origin = np.array([left, top - label_size[1]])
        else:
            text_origin = np.array([left, top + 1])
        for i in range(thickness):
            draw.rectangle(
                [left + i, top + i, right - i, bottom - i],
                outline=self.colors[int(c)])
        draw.rectangle(
            [tuple(text_origin), tuple(text_origin + label_size)],
            fill=self.colors[int(c)])
        draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
        del draw
    print("time:",time.time()-start_time)
    return image