people_yolo3_predict_pic.py

#!/usr/bin/env python
# -- coding: utf-8 --
"""
Copyright (c) 2018. All rights reserved.
Created by C. L. Wang on 2018/7/4
"""

"""
Run a YOLO_v3 style detection model on test images.

29420/29998,检测率=98%
"""

import colorsys
import os
from timeit import default_timer as timer

import numpy as np
from PIL import Image, ImageFont, ImageDraw
from keras import backend as K
from keras.layers import Input
from yolo3.model import yolo_eval, yolo_body
from yolo3.utils import letterbox_image

from shirt_color_recognise.people_classify import shirt_color_classify



#用来存储预测结果的txt文件
predict_result = '/home/xuy/code/mAP/predicted/'
#wider数据集的val-set的图片
img_root_path = '/home/xuy/code/keras-yolo3-shirt_color/people-det-base/JPEGImages/'
#img_path是单个图片的测试
# img_path = '/home/xuy/code/keras-yolo3-detection/wider_dataset/WIDER_train/images/0--Parade/0_Parade_marchingband_1_5.jpg'  # 先拿单张图片测试一下
#将预测结果的图片输出的路径
result_path = 'result/'
def iterbrowse(path):
    for home, dirs, files in os.walk(path):
        for filename in files:
            yield os.path.join(home, filename)
class YOLO(object):
    def __init__(self):
        self.anchors_path = 'configs/yolo_anchors.txt'  # Anchors
        # self.model_path = 'model_data/yolo_weights.h5'  # 模型文件
        self.model_path = 'model_data/yolo_weights.h5'  # 模型文件
        # self.classes_path = 'configs/coco_classes.txt'  # 类别文件
        self.classes_path = 'model_data/coco_classes.txt'  # 类别文件

        self.score = 0.1
        # self.iou = 0.45
        self.iou = 0.20
        self.class_names = self._get_class()  # 获取类别
        self.anchors = self._get_anchors()  # 获取anchor
        self.sess = K.get_session()
        self.model_image_size = (416, 416)  # fixed size or (None, None), hw
        self.boxes, self.scores, self.classes = self.generate()

    def _get_class(self):#读取了类别
        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

    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)

    def generate(self):
        model_path = os.path.expanduser(self.model_path)  # 转换~
        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'

        num_anchors = len(self.anchors)  # anchors的数量
        num_classes = len(self.class_names)  # 类别数

        # 加载模型参数
        self.yolo_model = yolo_body(Input(shape=(None, None, 3)), 3, num_classes)
        self.yolo_model.load_weights(model_path)

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

        # 不同的框，不同的颜色
        hsv_tuples = [(float(x) / len(self.class_names), 1., 1.)
                      for x in range(len(self.class_names))]  # 不同颜色
        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))  # RGB
        np.random.seed(10101)
        np.random.shuffle(self.colors)
        np.random.seed(None)

        # 根据检测参数，过滤框
        self.input_image_shape = K.placeholder(shape=(2,))
        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors, len(self.class_names),
                                           self.input_image_shape, score_threshold=self.score, iou_threshold=self.iou)
        return boxes, scores, classes


    def detect_image(self,image):
        start = timer()  # 起始时间

        if self.model_image_size != (None, None):  # 416x416, 416=32*13，必须为32的倍数，最小尺度是除以32
            assert self.model_image_size[0] % 32 == 0, 'Multiples of 32 required'
            assert self.model_image_size[1] % 32 == 0, 'Multiples of 32 required'
            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))  # 填充图像
        else:
            new_image_size = (image.width - (image.width % 32), image.height - (image.height % 32))
            boxed_image = letterbox_image(image, new_image_size)
        image_data = np.array(boxed_image, dtype='float32')
        print('detector size {}'.format(image_data.shape))
        image_data /= 255.  # 转换0~1
        image_data = np.expand_dims(image_data, 0)  # 添加批次维度，将图片增加1维

        # 参数盒子、得分、类别；输入图像0~1，4维；原始图像的尺寸
        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'))  # 检测出的框

        font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
                                  size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))  # 字体
        thickness = (image.size[0] + image.size[1]) // 300  # 厚度
        for i, c in reversed(list(enumerate(out_classes))):

            predicted_class = self.class_names[c]  # 类别
            if predicted_class=='person':
                box = out_boxes[i]  # 框
                score = out_scores[i]  # 执行度


                draw = ImageDraw.Draw(image)  # 画图
                label = '{} {:.2f}'.format(predicted_class, score)  # 标签
                label_size = draw.textsize(label, font)  # 标签文字

                top, left, bottom, right = box
                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'))
                print(label, (left, top), (right, bottom))  # 边框

                
                # print('行人的图片',type(image.crop((left,top,right,bottom))))

                # people_pic=image[top:bottom,left:right]
                people_pic=image.crop((left,top,right,bottom))
                # people_pic.show()
                output, colorLabel, color_prob=shirt_color_classify(people_pic)
                # colorText = "color: {} ({:.2f}%)".format(colorLabel, color_prob)
                colorText = "{}".format(colorLabel)



                if (right-left)<500:

                    if top - label_size[1] >= 0:  # 标签文字
                        text_origin = np.array([left, top - label_size[1]])
                    else:
                        text_origin = np.array([left, top + 1])

                    # My kingdom for a good redistributable image drawing library.
                    for i in range(thickness):  # 画框
                        draw.rectangle(
                            [left + i, top + i, right - i, bottom - i],
                            outline=self.colors[c])
                    draw.rectangle(  # 文字背景
                        [tuple(text_origin), tuple(text_origin + label_size)],
                        fill=self.colors[c])
                    draw.text(text_origin, colorText, fill=(0, 0, 0), font=font)  # 文案
                    del draw

        end = timer()
        print(end - start)  # 检测执行时间

        return image


    # def detect_image(self, image,img_path):#检测每一张图片的人脸位置
    def detect_image_pic(self, image):#检测每一张图片的人脸位置
        start = timer()  # 起始时间
        pic_filename=os.path.basename(img_path)
        # txt_filename=pic_filename.replace("jpg","txt")
        portion=os.path.splitext(pic_filename)
        if portion[1]=='.jpg':
            txt_result=predict_result+portion[0]+'.txt'
        print('txt_result的路径是：'+txt_result)
        if self.model_image_size != (None, None):  # 416x416, 416=32*13，必须为32的倍数，最小尺度是除以32
            assert self.model_image_size[0] % 32 == 0, 'Multiples of 32 required'
            assert self.model_image_size[1] % 32 == 0, 'Multiples of 32 required'
            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))  # 填充图像
        else:
            new_image_size = (image.width - (image.width % 32), image.height - (image.height % 32))
            boxed_image = letterbox_image(image, new_image_size)
        image_data = np.array(boxed_image, dtype='float32')
        print('detector size {}'.format(image_data.shape))
        image_data /= 255.  # 转换0~1
        image_data = np.expand_dims(image_data, 0)  # 添加批次维度，将图片增加1维

        # 参数盒子、得分、类别；输入图像0~1，4维；原始图像的尺寸
        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'))  # 检测出的框

        font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
                                  size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))  # 字体
        thickness = (image.size[0] + image.size[1]) // 128  # 厚度
        predicted_class_list = []
        box_list = []  # 用来存储坐标位置
        score_list = []  # 用来存储置信值
        with open(txt_result,'a')as new_f:
            for i, c in reversed(list(enumerate(out_classes))):
                predicted_class = self.class_names[c]  # 类别
                box = out_boxes[i]  # 框
                score = out_scores[i]  # 执行度

                label = '{} {:.2f}'.format(predicted_class, score)  # 标签,是预测概率值
                draw = ImageDraw.Draw(image)  # 画图
                label_size = draw.textsize(label, font)  # 标签文字

                top, left, bottom, right = box
                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'))
                print(label, (left, top), (right, bottom))  # 边框,这个就是【置信值，xmin,ymin,xmax,ymax】,可以做一下mAP值的分析了
                predicted_class_list.append(predicted_class)
                box_list.append([left, top, right, bottom])
                score_list.append(score)

                if top - label_size[1] >= 0:  # 标签文字
                    text_origin = np.array([left, top - label_size[1]])
                else:
                    text_origin = np.array([left, top + 1])


            if len(score_list)==0:
                return None
            # 获取最大的置信值
            max_index = score_list.index(max(score_list))
            max_score = score_list[max_index]
            max_predicted_class = predicted_class_list[max_index]
            max_box = box_list[max_index]
            max_left, max_top, max_right, max_bottom = max_box
            print("更新之后的坐标标签是：", (max_left, max_top), (max_right, max_bottom))
            label = '{} {:.2f}'.format(max_predicted_class, max_score)  # 标签
            # 这里需要改
            new_f.write(str(label) + " " + str(max_left) + " " + str(max_top) + " " + str(max_right) + " " + str(max_bottom) + '\n')
            # My kingdom for a good redistributable image drawing library.
            for i in range(thickness):  # 画框
                draw.rectangle(
                    [max_left + i, max_top + i, max_right - i, max_bottom - i],
                    outline=self.colors[c])
            draw.rectangle(  # 文字背景是红色
                [tuple(text_origin), tuple(text_origin + label_size)],
               fill=self.colors[c])
            draw.text(text_origin, label, fill=(0, 0, 0), font=font)  # 文字内容，face+是人脸的概率值
            del draw

        end = timer()
        print(end - start)  # 检测执行时间
        return image

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



def detect_img_for_test(yolo):
    #遍历该路径下的每一张图片
    global img_path
    for img_path in iterbrowse(img_root_path):

        print('img_path的路径是：'+img_path)
        image = Image.open(img_path)
        filename=os.path.basename(img_path)
        print('filename'+filename)
        # r_image = yolo.detect_image(image,img_path)
        r_image = yolo.detect_image(image)

        if r_image==None:
            continue
        # r_image.show()  # 先显示，然后再保存
        r_image.save(result_path+filename)







    # for parent,dirnames,filenames in os.walk(img_root_path):    #三个参数：分别返回1.父目录 2.所有文件夹名字（不含路径） 3.所有文件名字
    #     for dirname in dirnames:
    #         for filename in filenames:
    #             img_path=img_root_path+'/'+dirname+'/'+filename
    #             print(img_path)
            #     image = Image.open(img_path)
            #     r_image = yolo.detect_image(image)
            #     # r_image.show()  # 先显示，然后再保存
            #     r_image.save(result_path+filename)


    # image = Image.open(img_path)
    # r_image = yolo.detect_image(image)
    # # r_image.show()#先显示，然后再保存
    # r_image.save('/home/xuy/code/keras-yolo3-detection/' + 'result2.jpg')


    yolo.close_session()


if __name__ == '__main__':
    detect_img_for_test(YOLO())