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yolo3_predict.py
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yolo3_predict.py
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#!/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.
"""
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
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.classes_path = 'configs/coco_classes_ch.txt' # 类别文件
# self.model_path = 'model_data/ep074-loss26.535-val_loss27.370.h5' # 模型文件
# self.classes_path = 'configs/wider_classes.txt' # 类别文件
self.score = 0.60
self.iou = 0.45
# self.iou = 0.01
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.colors = self.__get_colors(self.class_names)
self.boxes, self.scores, self.classes = self.generate()
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path, encoding='utf8') 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)
@staticmethod
def __get_colors(names):
# 不同的框，不同的颜色
hsv_tuples = [(float(x) / len(names), 1., 1.)
for x in range(len(names))] # 不同颜色
colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors)) # RGB
np.random.seed(10101)
np.random.shuffle(colors)
np.random.seed(None)
return colors
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
self.yolo_model = yolo_body(Input(shape=(416, 416, 3)), 3, num_classes)
self.yolo_model.load_weights(model_path) # 加载模型参数
print('{} model, {} anchors, and {} classes loaded.'.format(model_path, num_anchors, num_classes))
# 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]) // 512 # 厚度
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
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
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, label, 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_objects_of_image(self, img_path):
image = Image.open(img_path)
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
image_data = np.array(boxed_image, dtype='float32')
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
# print('detector size {}'.format(image_data.shape))
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('out_boxes: {}'.format(out_boxes))
# print('out_scores: {}'.format(out_scores))
# print('out_classes: {}'.format(out_classes))
img_size = image.size[0] * image.size[1]
objects_line = self._filter_boxes(out_boxes, out_scores, out_classes, img_size)
return objects_line
def _filter_boxes(self, boxes, scores, classes, img_size):
res_items = []
for box, score, clazz in zip(boxes, scores, classes):
top, left, bottom, right = box
box_size = (bottom - top) * (right - left)
rate = float(box_size) / float(img_size)
clz_name = self.class_names[clazz]
if rate > 0.05:
res_items.append('{}-{:0.2f}'.format(clz_name, rate))
res_line = ','.join(res_items)
return res_line
def close_session(self):
self.sess.close()
def detect_img_for_test():
yolo = YOLO()
img_path = './dataset/vDaPl5QHdoqb2wOaVql4FoJWNGglYk.jpg'
image = Image.open(img_path)
r_image = yolo.detect_image(image)
yolo.close_session()
r_image.save('xxx.png')
def test_of_detect_objects_of_image():
yolo = YOLO()
img_path = './dataset/vDaPl5QHdoqb2wOaVql4FoJWNGglYk.jpg'
objects_line = yolo.detect_objects_of_image(img_path)
print(objects_line)
if __name__ == '__main__':
# detect_img_for_test()
test_of_detect_objects_of_image()