density_test.py

"""
keras-yolov3 进行批量测试并保存结果
"""

# -*- coding: utf-8 -*-

import colorsys
import os
from timeit import default_timer as timer
import time

import sqlite3
import numpy as np
from keras import backend as K
from keras.models import load_model
from keras.layers import Input
from PIL import Image, ImageFont, ImageDraw

from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from yolo3.utils import letterbox_image
from keras.utils import multi_gpu_model

path = './density/'  #待检测图片的位置

# 创建创建一个存储检测结果的dir
result_path = './static/density_results'
if not os.path.exists(result_path):
    os.makedirs(result_path)

# result如果之前存放的有文件，全部清除
for i in os.listdir(result_path):
    path_file = os.path.join(result_path,i)  
    if os.path.isfile(path_file):
        os.remove(path_file)

#创建一个记录检测结果的文件
txt_path =result_path + '/result.txt'
file = open(txt_path,'w')  

class YOLO(object):
    _defaults = {
        "model_path": 'model_data/trained_weights_final.h5',
        "anchors_path": 'model_data/yolo_anchors.txt',
        "classes_path": 'model_data/coco_classes.txt',
        "score" : 0.3,
        "iou" : 0.45,
        "model_image_size" : (416, 416),
        "gpu_num" : 1,
    }

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

    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults) # set up default values
        self.__dict__.update(kwargs) # and update with user overrides
        self.class_names = self._get_class()
        self.anchors = self._get_anchors()
        self.sess = K.get_session()
        self.boxes, self.scores, self.classes = self.generate()

    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

    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)
        assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'

        # Load model, or construct model and load weights.
        num_anchors = len(self.anchors)
        num_classes = len(self.class_names)
        is_tiny_version = num_anchors==6 # default setting
        try:
            self.yolo_model = load_model(model_path, compile=False)
        except:
            self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
                if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
            self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
        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))

        # Generate colors for drawing bounding boxes.
        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))
        np.random.seed(10101)  # Fixed seed for consistent colors across runs.
        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
        np.random.seed(None)  # Reset seed to default.

        # Generate output tensor targets for filtered bounding boxes.
        self.input_image_shape = K.placeholder(shape=(2, ))
        if self.gpu_num>=2:
            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
        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() # 开始计时

        global num_people 

        if self.model_image_size != (None, None):
            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)))
        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(image_data.shape) #打印图片的尺寸
        image_data /= 255.
        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

        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')) # 提示用于找到几个bbox

        font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
                    size=np.floor(2e-2 * image.size[1] + 0.2).astype('int32'))
        thickness = (image.size[0] + image.size[1]) // 500

        # 保存框检测出的框的个数
        file.write('find  '+str(len(out_boxes))+' target(s) \n')

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

            label = '{} {:.2f}'.format(predicted_class, score)
            num_people = len(out_boxes)
            draw = ImageDraw.Draw(image)
            label_size = draw.textsize(label, font)

            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'))

            # 写入检测位置            
            file.write(predicted_class+'  score: '+str(score)+' \nlocation: top: '+str(top)+'、 bottom: '+str(bottom)+'、 left: '+str(left)+'、 right: '+str(right)+'\n')
            
            print(label, (left, top), (right, bottom))

            if top - label_size[1] >= 0:
                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):
                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, label, fill=(0, 0, 0), font=font)
            del draw

        end = timer()
        print('time consume:%.3f s '%(end - start))
        return image

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


# 图片检测

if __name__ == '__main__':

    t1 = time.time()
    yolo = YOLO() 
    index = 1

    for filename in os.listdir(path):        
        image_path = path+'/'+filename
        portion = os.path.split(image_path)
        file.write(portion[1]+' detect_result：\n')        
        image = Image.open(image_path)
        r_image = yolo.detect_image(image)
        file.write('\n')
        #r_image.show() 显示检测结果
        print(num_people)
        image_save_path = './static/density_results/' + portion[1]       
        print('detect result save to....:'+image_save_path)
        r_image.save(image_save_path)
        loc = str(portion[1])
        loc = loc[:-4]
        
        #连接数据库,此前在数据库中创建数据库TESTDB
        db = sqlite3.connect('test.db' )
        # 使用cursor()方法获取操作游标 
        cursor = db.cursor()
        #cursor.execute('create table density(location varchar(40) primary key,num_people varchar(40))')
        if num_people <= 3:
            density_people = '稀疏'
        elif num_people <= 8:
            density_people = '适中'
        else:
            density_people = '密集'

        sql = "INSERT INTO density(location, num_people) VALUES ( ' "+loc+" ', ' "+density_people+" ' )"
        # 执行sql语句
        cursor.execute(sql)
        # 提交到数据库执行
        db.commit()
        index += 1

    time_sum = time.time() - t1
    file.write('time sum: '+str(time_sum)+'s') 
    print('time sum:',time_sum)
    file.close() 
    yolo.close_session()