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yolo.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
Run a YOLO_v3 style detection model on test images.
"""
import colorsys
import os
import random
from timeit import time
from timeit import default_timer as timer ### to calculate FPS
import numpy as np
from keras import backend as K
from keras.models import load_model
from PIL import Image, ImageFont, ImageDraw
from yolo3.model import yolo_eval
from yolo3.utils import letterbox_image
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input",help="path to input video", default="./1.mp4")
ap.add_argument("-c", "--class",help="name of class", default="person")
args = vars(ap.parse_args())
class YOLO(object):
def __init__(self):
#yolo_tiny
# self.model_path = 'model_data/yolo-tiny.h5' # yolo_tiny权重文件
# self.anchors_path = 'model_data/tiny_yolo_anchors.txt' #yolo_tiny 6个anchor box
#yolo
self.model_path = 'model_data/yolo.h5' #权重文件(原权重文件（种类为80）model_data/yolo.h5)
self.anchors_path = 'model_data/yolo_anchors.txt' #anchor box 9个，从小到大排列 13*13、26*26、52*52feature map 特征图越小，感受域越大，对大目标越敏感
self.classes_path = 'model_data/coco_classes.txt' #原权重文件（种类为80）model_data/coco_classes.txt)类别数
self.score = 0.5 #score置信度阈值，小于阈值被删除
self.iou = 0.5 #iou阈值，大于阈值的重叠框被删除
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.sess = K.get_session()
self.model_image_size = (416, 416) # fixed size or (None, None) 不同于这个尺寸的输入会先调整到标准大小
self.is_fixed_size = self.model_image_size != (None, None)
self.boxes, self.scores, self.classes = self.generate() #由generate（）函数完成目标检测
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path) #转换成用户目录
with open(classes_path) as f:
class_names = f.readlines() #读取所有行并返回列表
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(',')]
anchors = np.array(anchors).reshape(-1, 2)
return anchors
def generate(self):
"""
:return: boxes,scores,classes
"""
model_path = os.path.expanduser(self.model_path) #获取model路径
assert model_path.endswith('.h5'), 'Keras model must be a .h5 file.' #判断model是否以h5结尾
self.yolo_model = load_model(model_path, compile=False) #下载model ###################################################################question-1:load_model()
print('{} model, anchors, and classes loaded.'.format(model_path))
# Generate colors for drawing bounding boxes.生成绘制边框的颜色
hsv_tuples = [(x / len(self.class_names), 1., 1.) #h(色调）：x/len(self.class_names) s(饱和度）：1.0 v(明亮）：1.0
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) #hsv转换为rgb
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors)) #通过hsv_to_rgb()的rgb颜色的取值范围在【0,1】，而RBG取值范围在【0,255】，所以乘上255
random.seed(10101) # Fixed seed for consistent colors across runs.产生随机种子。固定种子为一致的颜色
random.shuffle(self.colors) # Shuffle colors to decorrelate adjacent classes.打乱颜色以消除相邻类的关联
random.seed(None) # Reset seed to default.重置种子为默认
# Generate output tensor targets for filtered bounding boxes.
self.input_image_shape = K.placeholder(shape=(2, )) #K.placeholder:keras中的占位符
boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors, #######################################################################question-2：yolo_eval()
len(self.class_names), self.input_image_shape,
score_threshold=self.score, iou_threshold=self.iou) #yolo_eval():yolo评估函数
return boxes, scores, classes
# def detect_image(self, image):
# if self.is_fixed_size:
# 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))) #进行缩放
# #letterbox_image()：先生成一个用绝对灰（RGB：128,128,128）填充的416*416的新图片，然后按照比例缩放（采样方法：BICBIC）后的输入图片粘贴，粘贴不到的部分保留灰色
# else:
# # model_image_size定义的宽和高必须是32的整倍数；若没有定义model_image_size，将输入图片的尺寸调整到32的整倍数，并调用letterbox_image()函数进行缩放
# new_image_size = (image.width - (image.width % 32),
# image.height - (image.height % 32))
# boxed_image = letterbox_image(image, new_image_size) #进行缩放
# image_data = np.array(boxed_image, dtype='float32')
#
# #print(image_data.shape)
# image_data /= 255. #将缩放后图片的数值除以255，做归一化
# # 将（416,416,3）数组调整为（1,416,416,3）元组，满足YOLOv3输入的张量格式
# 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={ #输入参数
# self.yolo_model.input: image_data, #输入图片
# self.input_image_shape: [image.size[1], image.size[0]], #图片尺寸416x416
# K.learning_phase(): 0 #学习模式：0测试/1训练
# })
# return_boxs = []
# for i, c in reversed(list(enumerate(out_classes))):
# predicted_class = self.class_names[c] #目标类别的名字
# if predicted_class != 'person' :
# continue
# box = out_boxes[i] #目标框
# # score = out_scores[i] #目标框的置信度评分
# x = int(box[1])
# y = int(box[0])
# w = int(box[3]-box[1])
# h = int(box[2]-box[0])
# if x < 0 :
# w = w + x
# x = 0
# if y < 0 :
# h = h + y
# y = 0
# return_boxs.append([x,y,w,h])
#
# return return_boxs
def detect_image(self, image):
if self.is_fixed_size:
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)
image_data /= 255. #归一化
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, ##################################################question：yolo_model.input:image_data,
self.input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0 #K.learning_phase() 学习阶段标志（0=test，1=train），它作为输入传递给任何的Keras函数
})
return_boxs = []
return_class_name = []
person_counter = 0
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
# print(self.class_names[c])
'''
if predicted_class != 'person' and predicted_class != 'car':
print(predicted_class)
continue
'''
if predicted_class != args["class"]:
# print(predicted_class)
continue
person_counter += 1
# if predicted_class != 'car':
# continue
# label = predicted_class
box = out_boxes[i]
# score = out_scores[i]
x = int(box[1])
y = int(box[0])
w = int(box[3] - box[1])
h = int(box[2] - box[0])
if x < 0:
w = w + x
x = 0
if y < 0:
h = h + y
y = 0
return_boxs.append([x, y, w, h])
# print(return_boxs)
return_class_name.append([predicted_class])
# cv2.putText(image, str(self.class_names[c]),(int(box[0]), int(box[1] -50)),0, 5e-3 * 150, (0,255,0),2)
# print("Found person: ",person_counter)
return return_boxs, return_class_name
def close_session(self):
self.sess.close()