yolo_carid.py

import colorsys
import os
import time

import cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import ImageDraw, ImageFont, Image

from nets.yolo import YoloBody
from utils.utils import (cvtColor, get_anchors, get_classes, preprocess_input,
                         resize_image, show_config)
from utils.utils_bbox import DecodeBox, DecodeBoxNP

'''
训练自己的数据集必看注释！
'''


class YOLO(object):
    _defaults = {
        # --------------------------------------------------------------------------#
        #   使用自己训练好的模型进行预测一定要修改model_path和classes_path！
        #   model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
        #
        #   训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
        #   验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
        #   如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
        # --------------------------------------------------------------------------#
        "model_path": 'model_data/best_epoch_weights.pth',
        "classes_path": 'model_data/carID_classes.txt',
        # ---------------------------------------------------------------------#
        #   anchors_path代表先验框对应的txt文件，一般不修改。
        #   anchors_mask用于帮助代码找到对应的先验框，一般不修改。
        # ---------------------------------------------------------------------#
        "anchors_path": 'model_data/yolo_anchors.txt',
        "anchors_mask": [[6, 7, 8], [3, 4, 5], [0, 1, 2]],
        # ---------------------------------------------------------------------#
        #   输入图片的大小，必须为32的倍数。
        # ---------------------------------------------------------------------#
        "input_shape": [640, 640],
        # ------------------------------------------------------#
        #   所使用到的yolov7的版本，本仓库一共提供两个：
        #   l : 对应yolov7
        #   x : 对应yolov7_x
        # ------------------------------------------------------#
        "phi": 'l',
        # ---------------------------------------------------------------------#
        #   只有得分大于置信度的预测框会被保留下来
        # ---------------------------------------------------------------------#
        "confidence": 0.5,
        # ---------------------------------------------------------------------#
        #   非极大抑制所用到的nms_iou大小
        # ---------------------------------------------------------------------#
        "nms_iou": 0.3,
        # ---------------------------------------------------------------------#
        #   该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
        #   在多次测试后，发现关闭letterbox_image直接resize的效果更好
        # ---------------------------------------------------------------------#
        "letterbox_image": True,
        # -------------------------------#
        #   是否使用Cuda
        #   没有GPU可以设置成False
        # -------------------------------#
        "cuda": True,
    }

    @classmethod
    def get_defaults(cls, n):
        if n in cls._defaults:
            return cls._defaults[n]
        else:
            return "Unrecognized attribute name '" + n + "'"

    # ---------------------------------------------------#
    #   初始化YOLO
    # ---------------------------------------------------#
    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults)
        for name, value in kwargs.items():
            setattr(self, name, value)
            self._defaults[name] = value

            # ---------------------------------------------------#
        #   获得种类和先验框的数量
        # ---------------------------------------------------#
        self.class_names, self.num_classes = get_classes(self.classes_path)
        self.anchors, self.num_anchors = get_anchors(self.anchors_path)
        self.bbox_util = DecodeBox(self.anchors, self.num_classes, (self.input_shape[0], self.input_shape[1]),
                                   self.anchors_mask)

        # ---------------------------------------------------#
        #   画框设置不同的颜色
        # ---------------------------------------------------#
        hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
        self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
        self.generate()

        show_config(**self._defaults)

    # ---------------------------------------------------#
    #   生成模型
    # ---------------------------------------------------#
    def generate(self, onnx=False):
        # ---------------------------------------------------#
        #   建立yolo模型，载入yolo模型的权重
        # ---------------------------------------------------#
        self.net = YoloBody(self.anchors_mask, self.num_classes, self.phi)
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.net.load_state_dict(torch.load(self.model_path, map_location=device))
        self.net = self.net.fuse().eval()
        print('{} model, and classes loaded.'.format(self.model_path))
        if not onnx:
            if self.cuda:
                self.net = nn.DataParallel(self.net)
                self.net = self.net.cuda()

    # ---------------------------------------------------#
    #   检测图片
    # ---------------------------------------------------#
    def detect_image(self, image, crop=False, count=False):
        # ---------------------------------------------------#
        #   计算输入图片的高和宽
        # ---------------------------------------------------#
        image_shape = np.array(np.shape(image)[0:2])
        # ---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
        # ---------------------------------------------------------#
        image = cvtColor(image)
        # ---------------------------------------------------------#
        #   给图像增加灰条，实现不失真的resize
        #   也可以直接resize进行识别
        # ---------------------------------------------------------#
        image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
        # ---------------------------------------------------------#
        #   添加上batch_size维度
        #   h, w, 3 => 3, h, w => 1, 3, h, w
        # ---------------------------------------------------------#
        image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)

        with torch.no_grad():
            images = torch.from_numpy(image_data)
            if self.cuda:
                images = images.cuda()
            # ---------------------------------------------------------#
            #   将图像输入网络当中进行预测！
            # ---------------------------------------------------------#
            outputs = self.net(images)
            outputs = self.bbox_util.decode_box(outputs)
            # ---------------------------------------------------------#
            #   将预测框进行堆叠，然后进行非极大抑制
            # ---------------------------------------------------------#
            results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
                                                         image_shape, self.letterbox_image, conf_thres=self.confidence,
                                                         nms_thres=self.nms_iou)

            if results[0] is None:
                return None

            top_label = np.array(results[0][:, 6], dtype='int32')
            top_conf = results[0][:, 4] * results[0][:, 5]
            top_boxes = results[0][:, :4]
        # ---------------------------------------------------------#
        #   计数
        # ---------------------------------------------------------#
        if count:
            print("top_label:", top_label)
            classes_nums = np.zeros([self.num_classes])
            for i in range(self.num_classes):
                num = np.sum(top_label == i)
                if num > 0:
                    print(self.class_names[i], " : ", num)
                classes_nums[i] = num
            print("classes_nums:", classes_nums)
        # ---------------------------------------------------------#
        #   计算结果集
        # ---------------------------------------------------------#
        result_set = []
        for i, c in list(enumerate(top_label)):
            predicted_class = self.class_names[int(c)]
            box = top_boxes[i]
            score = top_conf[i]
            top, left, bottom, right = box
            top = max(0, np.floor(top).astype('int32'))
            left = max(0, np.floor(left).astype('int32'))
            bottom = min(image.size[1], np.floor(bottom).astype('int32'))
            right = min(image.size[0], np.floor(right).astype('int32'))
            label = '{} {:.2f}'.format(predicted_class, score)
            label = label.encode('utf-8')
            result_set.append([label, top, left, bottom, right])
            print(label, top, left, bottom, right)

        return result_set

    def get_FPS(self, image, test_interval):
        image_shape = np.array(np.shape(image)[0:2])
        # ---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
        # ---------------------------------------------------------#
        image = cvtColor(image)
        # ---------------------------------------------------------#
        #   给图像增加灰条，实现不失真的resize
        #   也可以直接resize进行识别
        # ---------------------------------------------------------#
        image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
        # ---------------------------------------------------------#
        #   添加上batch_size维度
        # ---------------------------------------------------------#
        image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)

        with torch.no_grad():
            images = torch.from_numpy(image_data)
            if self.cuda:
                images = images.cuda()
            # ---------------------------------------------------------#
            #   将图像输入网络当中进行预测！
            # ---------------------------------------------------------#
            outputs = self.net(images)
            outputs = self.bbox_util.decode_box(outputs)
            # ---------------------------------------------------------#
            #   将预测框进行堆叠，然后进行非极大抑制
            # ---------------------------------------------------------#
            results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
                                                         image_shape, self.letterbox_image, conf_thres=self.confidence,
                                                         nms_thres=self.nms_iou)

        t1 = time.time()
        for _ in range(test_interval):
            with torch.no_grad():
                # ---------------------------------------------------------#
                #   将图像输入网络当中进行预测！
                # ---------------------------------------------------------#
                outputs = self.net(images)
                outputs = self.bbox_util.decode_box(outputs)
                # ---------------------------------------------------------#
                #   将预测框进行堆叠，然后进行非极大抑制
                # ---------------------------------------------------------#
                results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
                                                             image_shape, self.letterbox_image,
                                                             conf_thres=self.confidence, nms_thres=self.nms_iou)

        t2 = time.time()
        tact_time = (t2 - t1) / test_interval
        return tact_time

    def detect_heatmap(self, image, heatmap_save_path):
        import cv2
        import matplotlib.pyplot as plt
        def sigmoid(x):
            y = 1.0 / (1.0 + np.exp(-x))
            return y

        # ---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
        # ---------------------------------------------------------#
        image = cvtColor(image)
        # ---------------------------------------------------------#
        #   给图像增加灰条，实现不失真的resize
        #   也可以直接resize进行识别
        # ---------------------------------------------------------#
        image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
        # ---------------------------------------------------------#
        #   添加上batch_size维度
        # ---------------------------------------------------------#
        image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)

        with torch.no_grad():
            images = torch.from_numpy(image_data)
            if self.cuda:
                images = images.cuda()
            # ---------------------------------------------------------#
            #   将图像输入网络当中进行预测！
            # ---------------------------------------------------------#
            outputs = self.net(images)

        plt.imshow(image, alpha=1)
        plt.axis('off')
        mask = np.zeros((image.size[1], image.size[0]))
        for sub_output in outputs:
            sub_output = sub_output.cpu().numpy()
            b, c, h, w = np.shape(sub_output)
            sub_output = np.transpose(np.reshape(sub_output, [b, 3, -1, h, w]), [0, 3, 4, 1, 2])[0]
            score = np.max(sigmoid(sub_output[..., 4]), -1)
            score = cv2.resize(score, (image.size[0], image.size[1]))
            normed_score = (score * 255).astype('uint8')
            mask = np.maximum(mask, normed_score)

        plt.imshow(mask, alpha=0.5, interpolation='nearest', cmap="jet")

        plt.axis('off')
        plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
        plt.margins(0, 0)
        plt.savefig(heatmap_save_path, dpi=200, bbox_inches='tight', pad_inches=-0.1)
        print("Save to the " + heatmap_save_path)
        plt.show()

    def convert_to_onnx(self, simplify, model_path):
        import onnx
        self.generate(onnx=True)

        im = torch.zeros(1, 3, *self.input_shape).to('cpu')  # image size(1, 3, 512, 512) BCHW
        input_layer_names = ["images"]
        output_layer_names = ["output"]

        # Export the model
        print(f'Starting export with onnx {onnx.__version__}.')
        torch.onnx.export(self.net,
                          im,
                          f=model_path,
                          verbose=False,
                          opset_version=12,
                          training=torch.onnx.TrainingMode.EVAL,
                          do_constant_folding=True,
                          input_names=input_layer_names,
                          output_names=output_layer_names,
                          dynamic_axes=None)

        # Checks
        model_onnx = onnx.load(model_path)  # load onnx model
        onnx.checker.check_model(model_onnx)  # check onnx model

        # Simplify onnx
        if simplify:
            import onnxsim
            print(f'Simplifying with onnx-simplifier {onnxsim.__version__}.')
            model_onnx, check = onnxsim.simplify(
                model_onnx,
                dynamic_input_shape=False,
                input_shapes=None)
            assert check, 'assert check failed'
            onnx.save(model_onnx, model_path)

        print('Onnx model save as {}'.format(model_path))

    def get_map_txt(self, image_id, image, class_names, map_out_path):
        f = open(os.path.join(map_out_path, "detection-results/" + image_id + ".txt"), "w", encoding='utf-8')
        image_shape = np.array(np.shape(image)[0:2])
        # ---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
        # ---------------------------------------------------------#
        image = cvtColor(image)
        # ---------------------------------------------------------#
        #   给图像增加灰条，实现不失真的resize
        #   也可以直接resize进行识别
        # ---------------------------------------------------------#
        image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
        # ---------------------------------------------------------#
        #   添加上batch_size维度
        # ---------------------------------------------------------#
        image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)

        with torch.no_grad():
            images = torch.from_numpy(image_data)
            if self.cuda:
                images = images.cuda()
            # ---------------------------------------------------------#
            #   将图像输入网络当中进行预测！
            # ---------------------------------------------------------#
            outputs = self.net(images)
            outputs = self.bbox_util.decode_box(outputs)
            # ---------------------------------------------------------#
            #   将预测框进行堆叠，然后进行非极大抑制
            # ---------------------------------------------------------#
            results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
                                                         image_shape, self.letterbox_image, conf_thres=self.confidence,
                                                         nms_thres=self.nms_iou)

            if results[0] is None:
                return

            top_label = np.array(results[0][:, 6], dtype='int32')
            top_conf = results[0][:, 4] * results[0][:, 5]
            top_boxes = results[0][:, :4]

        for i, c in list(enumerate(top_label)):
            predicted_class = self.class_names[int(c)]
            box = top_boxes[i]
            score = str(top_conf[i])

            top, left, bottom, right = box
            if predicted_class not in class_names:
                continue

            f.write("%s %s %s %s %s %s\n" % (
                predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)), str(int(bottom))))

        f.close()
        return


class YOLO_ONNX(object):
    _defaults = {
        # --------------------------------------------------------------------------#
        #   使用自己训练好的模型进行预测一定要修改onnx_path和classes_path！
        #   onnx_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
        #
        #   训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
        #   验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
        #   如果出现shape不匹配，同时要注意训练时的onnx_path和classes_path参数的修改
        # --------------------------------------------------------------------------#
        "onnx_path": 'model_data/models.onnx',
        "classes_path": 'model_data/coco_classes.txt',
        # ---------------------------------------------------------------------#
        #   anchors_path代表先验框对应的txt文件，一般不修改。
        #   anchors_mask用于帮助代码找到对应的先验框，一般不修改。
        # ---------------------------------------------------------------------#
        "anchors_path": 'model_data/yolo_anchors.txt',
        "anchors_mask": [[6, 7, 8], [3, 4, 5], [0, 1, 2]],
        # ---------------------------------------------------------------------#
        #   输入图片的大小，必须为32的倍数。
        # ---------------------------------------------------------------------#
        "input_shape": [640, 640],
        # ---------------------------------------------------------------------#
        #   只有得分大于置信度的预测框会被保留下来
        # ---------------------------------------------------------------------#
        "confidence": 0.5,
        # ---------------------------------------------------------------------#
        #   非极大抑制所用到的nms_iou大小
        # ---------------------------------------------------------------------#
        "nms_iou": 0.3,
        # ---------------------------------------------------------------------#
        #   该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
        #   在多次测试后，发现关闭letterbox_image直接resize的效果更好
        # ---------------------------------------------------------------------#
        "letterbox_image": True
    }

    @classmethod
    def get_defaults(cls, n):
        if n in cls._defaults:
            return cls._defaults[n]
        else:
            return "Unrecognized attribute name '" + n + "'"

    # ---------------------------------------------------#
    #   初始化YOLO
    # ---------------------------------------------------#
    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults)
        for name, value in kwargs.items():
            setattr(self, name, value)
            self._defaults[name] = value

        import onnxruntime
        self.onnx_session = onnxruntime.InferenceSession(self.onnx_path)
        # 获得所有的输入node
        self.input_name = self.get_input_name()
        # 获得所有的输出node
        self.output_name = self.get_output_name()

        # ---------------------------------------------------#
        #   获得种类和先验框的数量
        # ---------------------------------------------------#
        self.class_names, self.num_classes = self.get_classes(self.classes_path)
        self.anchors, self.num_anchors = self.get_anchors(self.anchors_path)
        self.bbox_util = DecodeBoxNP(self.anchors, self.num_classes, (self.input_shape[0], self.input_shape[1]),
                                     self.anchors_mask)

        # ---------------------------------------------------#
        #   画框设置不同的颜色
        # ---------------------------------------------------#
        hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
        self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))

        show_config(**self._defaults)

    def get_classes(self, classes_path):
        with open(classes_path, encoding='utf-8') as f:
            class_names = f.readlines()
        class_names = [c.strip() for c in class_names]
        return class_names, len(class_names)

    def get_anchors(self, anchors_path):
        '''loads the anchors from a file'''
        with open(anchors_path, encoding='utf-8') as f:
            anchors = f.readline()
        anchors = [float(x) for x in anchors.split(',')]
        anchors = np.array(anchors).reshape(-1, 2)
        return anchors, len(anchors)

    def get_input_name(self):
        # 获得所有的输入node
        input_name = []
        for node in self.onnx_session.get_inputs():
            input_name.append(node.name)
        return input_name

    def get_output_name(self):
        # 获得所有的输出node
        output_name = []
        for node in self.onnx_session.get_outputs():
            output_name.append(node.name)
        return output_name

    def get_input_feed(self, image_tensor):
        # 利用input_name获得输入的tensor
        input_feed = {}
        for name in self.input_name:
            input_feed[name] = image_tensor
        return input_feed

    # ---------------------------------------------------#
    #   对输入图像进行resize
    # ---------------------------------------------------#
    def resize_image(self, image, size, letterbox_image, mode='PIL'):
        if mode == 'PIL':
            iw, ih = image.size
            w, h = size

            if letterbox_image:
                scale = min(w / iw, h / ih)
                nw = int(iw * scale)
                nh = int(ih * scale)

                image = image.resize((nw, nh), Image.BICUBIC)
                new_image = Image.new('RGB', size, (128, 128, 128))
                new_image.paste(image, ((w - nw) // 2, (h - nh) // 2))
            else:
                new_image = image.resize((w, h), Image.BICUBIC)
        else:
            image = np.array(image)
            if letterbox_image:
                # 获得现在的shape
                shape = np.shape(image)[:2]
                # 获得输出的shape
                if isinstance(size, int):
                    size = (size, size)

                # 计算缩放的比例
                r = min(size[0] / shape[0], size[1] / shape[1])

                # 计算缩放后图片的高宽
                new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
                dw, dh = size[1] - new_unpad[0], size[0] - new_unpad[1]

                # 除以2以padding到两边
                dw /= 2
                dh /= 2

                # 对图像进行resize
                if shape[::-1] != new_unpad:  # resize
                    image = cv2.resize(image, new_unpad, interpolation=cv2.INTER_LINEAR)
                top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
                left, right = int(round(dw - 0.1)), int(round(dw + 0.1))

                new_image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT,
                                               value=(128, 128, 128))  # add border
            else:
                new_image = cv2.resize(image, (w, h))

        return new_image

    def detect_image(self, image):
        image_shape = np.array(np.shape(image)[0:2])
        # ---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
        # ---------------------------------------------------------#
        image = cvtColor(image)

        image_data = self.resize_image(image, self.input_shape, True)
        # ---------------------------------------------------------#
        #   添加上batch_size维度
        #   h, w, 3 => 3, h, w => 1, 3, h, w
        # ---------------------------------------------------------#
        image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)

        input_feed = self.get_input_feed(image_data)
        outputs = self.onnx_session.run(output_names=self.output_name, input_feed=input_feed)

        feature_map_shape = [[int(j / (2 ** (i + 3))) for j in self.input_shape] for i in
                             range(len(self.anchors_mask))][::-1]
        for i in range(len(self.anchors_mask)):
            outputs[i] = np.reshape(outputs[i], (
                1, len(self.anchors_mask[i]) * (5 + self.num_classes), feature_map_shape[i][0],
                feature_map_shape[i][1]))

        outputs = self.bbox_util.decode_box(outputs)
        # ---------------------------------------------------------#
        #   将预测框进行堆叠，然后进行非极大抑制
        # ---------------------------------------------------------#
        results = self.bbox_util.non_max_suppression(np.concatenate(outputs, 1), self.num_classes, self.input_shape,
                                                     image_shape, self.letterbox_image, conf_thres=self.confidence,
                                                     nms_thres=self.nms_iou)

        if results[0] is None:
            return image

        top_label = np.array(results[0][:, 6], dtype='int32')
        top_conf = results[0][:, 4] * results[0][:, 5]
        top_boxes = results[0][:, :4]

        # ---------------------------------------------------------#
        #   设置字体与边框厚度
        # ---------------------------------------------------------#
        font = ImageFont.truetype(font='model_data/simhei.ttf',
                                  size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
        thickness = int(max((image.size[0] + image.size[1]) // np.mean(self.input_shape), 1))

        # ---------------------------------------------------------#
        #   图像绘制
        # ---------------------------------------------------------#
        for i, c in list(enumerate(top_label)):
            predicted_class = self.class_names[int(c)]
            box = top_boxes[i]
            score = top_conf[i]

            top, left, bottom, right = box

            top = max(0, np.floor(top).astype('int32'))
            left = max(0, np.floor(left).astype('int32'))
            bottom = min(image.size[1], np.floor(bottom).astype('int32'))
            right = min(image.size[0], np.floor(right).astype('int32'))

            label = '{} {:.2f}'.format(predicted_class, score)
            draw = ImageDraw.Draw(image)
            label_size = draw.textsize(label, font)
            label = label.encode('utf-8')
            print(label, top, left, bottom, right)

            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[c])
            draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
            draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)
            del draw

        return image