yolo.py

import colorsys
import os
import time

import numpy as np
from keras import backend as K
from keras.layers import Input
from keras.models import load_model
from PIL import Image, ImageDraw, ImageFont

from nets.yolo4 import yolo_body, yolo_eval
from utils.utils import letterbox_image


#--------------------------------------------#
#   使用自己训练好的模型预测需要修改2个参数
#   model_path和classes_path都需要修改！
#   如果出现shape不匹配，一定要注意
#   训练时的model_path和classes_path参数的修改
#--------------------------------------------#
class YOLO(object):
    _defaults = {
        "model_path"        : 'model_data/yolo4_weight.h5',
        "anchors_path"      : 'model_data/yolo_anchors.txt',
        "classes_path"      : 'model_data/coco_classes.txt',
        "score"             : 0.5,
        "iou"               : 0.3,
        "max_boxes"         : 100,
        # 显存比较小可以使用416x416
        # 显存比较大可以使用608x608
        "model_image_size"  : (416, 416),
        #---------------------------------------------------------------------#
        #   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"   : False,
    }

    @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)
        self.class_names = self._get_class()
        self.anchors = self._get_anchors()
        self.sess = K.get_session()
        self.boxes, self.scores, self.classes = self.generate()

    #---------------------------------------------------#
    #   获得所有的分类
    #---------------------------------------------------#
    def _get_class(self):
        classes_path = os.path.expanduser(self.classes_path)
        with open(classes_path) as f:
            class_names = f.readlines()
        class_names = [c.strip() for c in class_names]
        return class_names

    #---------------------------------------------------#
    #   获得所有的先验框
    #---------------------------------------------------#
    def _get_anchors(self):
        anchors_path = os.path.expanduser(self.anchors_path)
        with open(anchors_path) as f:
            anchors = f.readline()
        anchors = [float(x) for x in anchors.split(',')]
        return np.array(anchors).reshape(-1, 2)

    #---------------------------------------------------#
    #   载入模型
    #---------------------------------------------------#
    def generate(self):
        model_path = os.path.expanduser(self.model_path)
        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
        
        #---------------------------------------------------#
        #   计算先验框的数量和种类的数量
        #---------------------------------------------------#
        num_anchors = len(self.anchors)
        num_classes = len(self.class_names)

        #---------------------------------------------------------#
        #   载入模型，如果原来的模型里已经包括了模型结构则直接载入。
        #   否则先构建模型再载入
        #---------------------------------------------------------#
        try:
            self.yolo_model = load_model(model_path, compile=False)
        except:
            self.yolo_model = yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
            self.yolo_model.load_weights(self.model_path)
        else:
            assert self.yolo_model.layers[-1].output_shape[-1] == \
                num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
                'Mismatch between model and given anchor and class sizes'

        print('{} model, anchors, and classes loaded.'.format(model_path))

        # 画框设置不同的颜色
        hsv_tuples = [(x / len(self.class_names), 1., 1.)
                      for x in range(len(self.class_names))]
        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))

        # 打乱颜色
        np.random.seed(10101)
        np.random.shuffle(self.colors)
        np.random.seed(None)

        self.input_image_shape = K.placeholder(shape=(2, ))

        #---------------------------------------------------------#
        #   在yolo_eval函数中，我们会对预测结果进行后处理
        #   后处理的内容包括，解码、非极大抑制、门限筛选等
        #---------------------------------------------------------#
        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                num_classes, self.input_image_shape, max_boxes = self.max_boxes,
                score_threshold = self.score, iou_threshold = self.iou, letterbox_image = self.letterbox_image)
        return boxes, scores, classes

    #---------------------------------------------------#
    #   检测图片
    #---------------------------------------------------#
    def detect_image(self, image):
        #---------------------------------------------------------#
        #   在这里将图像转换成RGB图像，防止灰度图在预测时报错。
        #---------------------------------------------------------#
        image = image.convert('RGB')

        #---------------------------------------------------------#
        #   给图像增加灰条，实现不失真的resize
        #   也可以直接resize进行识别
        #---------------------------------------------------------#
        if self.letterbox_image:
            boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
        else:
            boxed_image = image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
        image_data = np.array(boxed_image, dtype='float32')
        image_data /= 255.
        #---------------------------------------------------------#
        #   添加上batch_size维度
        #---------------------------------------------------------#
        image_data = np.expand_dims(image_data, 0)

        #---------------------------------------------------------#
        #   将图像输入网络当中进行预测！
        #---------------------------------------------------------#
        out_boxes, out_scores, out_classes = self.sess.run(
            [self.boxes, self.scores, self.classes],
            feed_dict={
                self.yolo_model.input: image_data,
                self.input_image_shape: [image.size[1], image.size[0]],
                K.learning_phase(): 0})

        print('Found {} boxes for {}'.format(len(out_boxes), 'img'))

        #---------------------------------------------------------#
        #   设置字体
        #---------------------------------------------------------#
        font = ImageFont.truetype(font='model_data/simhei.ttf',
                    size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))

        thickness = max((image.size[0] + image.size[1]) // 300, 1)

        for i, c in list(enumerate(out_classes)):
            predicted_class = self.class_names[c]
            box = out_boxes[i]
            score = out_scores[i]

            top, left, bottom, right = box
            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(image.size[1], np.floor(bottom + 0.5).astype('int32'))
            right = min(image.size[0], 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, 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

    def get_FPS(self, image, test_interval):
        if self.letterbox_image:
            boxed_image = letterbox_image(image, (self.model_image_size[1],self.model_image_size[0]))
        else:
            boxed_image = image.convert('RGB')
            boxed_image = boxed_image.resize((self.model_image_size[1],self.model_image_size[0]), Image.BICUBIC)
        image_data = np.array(boxed_image, dtype='float32')
        image_data /= 255.
        image_data = np.expand_dims(image_data, 0)

        out_boxes, out_scores, out_classes = self.sess.run(
            [self.boxes, self.scores, self.classes],
            feed_dict={
                self.yolo_model.input: image_data,
                self.input_image_shape: [image.size[1], image.size[0]],
                K.learning_phase(): 0})

        t1 = time.time()
        for _ in range(test_interval):
            out_boxes, out_scores, out_classes = self.sess.run(
                [self.boxes, self.scores, self.classes],
                feed_dict={
                    self.yolo_model.input: image_data,
                    self.input_image_shape: [image.size[1], image.size[0]],
                    K.learning_phase(): 0})
        t2 = time.time()
        tact_time = (t2 - t1) / test_interval
        return tact_time

    def close_session(self):
        self.sess.close()