YOLOv2是Joseph Redmon提出的针对YOLO算法不足的改进版本，作者使用了一系列的方法对原来的YOLO多目标检测框架进行了改进，在保持原有速度的优势之下，精度上得以提升，此外作者提出了一种目标分类与检测的联合训练方法，通过这种方法YOLO9000可以同时在COCO和ImageNet数据集中进行训练，训练后的模型可以实现多达9000种物体的实时检测。
Paper：https://arxiv.org/abs/1612.08242
Github：https://github.com/pjreddie/darknet
Website：https://pjreddie.com/darknet/yolo
作者为YOLO算法设计了独有的深度学习框架darknet，因此没有提供Python的接口。在实验中，我找到了两种在Python 3中使用YOLOv2网络的方法。
第一种：为darknet添加Python接口
Github：https://github.com/SidHard/py-yolo2
该项目使用了原始的darknet网络，需要使用cmake重新编译源码，因此在Linux上使用更为方便一些。
首先从git上下载该项目
git clone https://github.com/SidHard/py-yolo2.git
执行cmake生成项目
cmake .. && make
最后执行yolo.py测试项目，相应的网络结构.cfg文件保存在cfg文件夹中，权值.weight文件放在根目录下，这些可以从darknet的官方网站上下载使用。
第二种：使用keras
Github：https://github.com/allanzelener/YAD2K
该项目使用了keras与tensorflow-gpu，因此可以在任何使用该框架的环境下运行，我在自己的程序中使用的该种方法。
首先下载源文件并且配置环境，可以使用anaconda环境或者在全局安装。
git clone https://github.com/allanzelener/yad2k.gitcd yad2k # [Option 1] To replicate the conda environment: conda env create -f environment.ymlsource activate yad2k # [Option 2] Install everything globaly. pip install numpy pip install tensorflow-gpu # CPU-only: conda install -c conda-forge tensorflowpip install keras # Possibly older release: conda install keras快速开始
- 从Darknet官方下载model：official YOLO website.
wget http://pjreddie.com/media/files/yolo.weights - 将 Darknet YOLO_v2 model转换为Keras model.
./yad2k.py cfg/yolo.cfg yolo.weights model_data/yolo.h5 - 测试图片位于
images/文件夹.
./test_yolo.py model_data/yolo.h5
最后执行test_yolo就可以执行网络，在images/out/文件夹里可以看到执行效果。
dog.jpg
eagle.jpg
giraffe.jpg
horses.jpg
为了方便模型用于测试视频与图片，我对demo做了修改，相比原来的测试代码，能够直接移植到项目中去，对象化的程序也更易于修改，代码如下
#! /usr/bin/env python """Run a YOLO_v2 style detection model on test images.""" import cv2import osimport timeimport numpy as npfrom keras import backend as Kfrom keras.models import load_model from yad2k.models.keras_yolo import yolo_eval, yolo_head class YOLO(object): def __init__(self): self.model_path = 'model_data/yolo.h5' self.anchors_path = 'model_data/yolo_anchors.txt' self.classes_path = 'model_data/coco_classes.txt' self.score = 0.3 self.iou = 0.5 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(',')] anchors = np.array(anchors).reshape(-1, 2) return anchors def generate(self): model_path = os.path.expanduser(self.model_path) assert model_path.endswith('.h5'), 'Keras model must be a .h5 file.' self.yolo_model = load_model(model_path) # Verify model, anchors, and classes are compatible num_classes = len(self.class_names) num_anchors = len(self.anchors) # TODO: Assumes dim ordering is channel last model_output_channels = self.yolo_model.layers[-1].output_shape[-1] assert model_output_channels == num_anchors * (num_classes + 5), \ 'Mismatch between model and given anchor and class sizes' print('{} model, anchors, and classes loaded.'.format(model_path)) # Check if model is fully convolutional, assuming channel last order. self.model_image_size = self.yolo_model.layers[0].input_shape[1:3] self.is_fixed_size = self.model_image_size != (None, None) # Generate output tensor targets for filtered bounding boxes. # TODO: Wrap these backend operations with Keras layers. yolo_outputs = yolo_head(self.yolo_model.output, self.anchors, len(self.class_names)) self.input_image_shape = K.placeholder(shape=(2, )) boxes, scores, classes = yolo_eval(yolo_outputs, self.input_image_shape, score_threshold=self.score, iou_threshold=self.iou) return boxes, scores, classes def detect_image(self, image): start = time.time() y, x, _ = image.shape if self.is_fixed_size: # TODO: When resizing we can use minibatch input. resized_image = cv2.resize(image, tuple(reversed(self.model_image_size)), interpolation=cv2.INTER_CUBIC) image_data = np.array(resized_image, dtype='float32') else: image_data = np.array(image, dtype='float32') 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.shape[0], image.shape[1]], K.learning_phase(): 0 }) print('Found {} boxes for {}'.format(len(out_boxes), 'img')) 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) 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(y, np.floor(bottom + 0.5).astype('int32')) right = min(x, np.floor(right + 0.5).astype('int32')) print(label, (left, top), (right, bottom)) cv2.rectangle(image, (left, top), (right, bottom), (255, 0, 0), 2) cv2.putText(image, label, (left, int(top - 4)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA) end = time.time() print(end - start) return image def close_session(self): self.sess.close() def detect_vedio(video, yolo): camera = cv2.VideoCapture(video) cv2.namedWindow("detection", cv2.WINDOW_NORMAL) while True: res, frame = camera.read() if not res: break image = yolo.detect_image(frame) cv2.imshow("detection", image) if cv2.waitKey(110) & 0xff == 27: break yolo.close_session() def detect_img(img, yolo): image = cv2.imread(img) r_image = yolo.detect_image(image) cv2.namedWindow("detection") while True: cv2.imshow("detection", r_image) if cv2.waitKey(110) & 0xff == 27: break yolo.close_session() if __name__ == '__main__': yolo = YOLO() img = 'E:\Documents\Downloads\YAD2K-master\YAD2K-master\images\horses.jpg' video = 'E:\Documents\Documents\python\Traffic\data\person.avi' detect_img(img, yolo)detect_vedio(video, yolo)
作者：洛荷
链接：https://www.jianshu.com/p/3e77cefeb49b
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