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1、環(huán)境配置

tensorflow1.12.0

Opencv3.4.2

keras

pycharm

2、配置yolov3

• 下載yolov3代碼：https://github.com/qqwweee/keras-yolo3
• 下載權(quán)重：https://pjreddie.com/media/files/yolov3.weights，并將權(quán)重文件放在keras-yolo3-master文件下

• 執(zhí)行如下命令將darknet下的yolov3配置文件轉(zhuǎn)換成keras適用的h5文件。

  ? ? ? ??? ? ?python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5

更改了一下代碼：重新編寫了一個(gè)測試代碼object_detection_yolo.py

# This code is written at BigVision LLC. It is based on the OpenCV project. It is subject to the license terms in the LICENSE file found in this distribution and at http://opencv.org/license.html# Usage example: python3 object_detection_yolo.py --video=run.mp4 # python3 object_detection_yolo.py --image=bird.jpgimport cv2 as cv import argparse import sys import numpy as np import os.path# Initialize the parameters confThreshold = 0.5 # Confidence threshold nmsThreshold = 0.4 #Non-maximum suppression threshold inpWidth = 416 #Width of network's input image inpHeight = 416 #Height of network's input image parser = argparse.ArgumentParser(description='Object Detection using YOLO in OPENCV') parser.add_argument('--image', help='Path to image file.') parser.add_argument('--video', help='Path to video file.') args = parser.parse_args()# Load names of classes classesFile = "model_data/coco_classes.txt"; classes = None # with open(classesFile, 'rt') as f: # classes = f.read().rstrip('\n').split('\n') classes_path = os.path.expanduser(classesFile) with open(classes_path) as f:class_names = f.readlines()classes = [c.strip() for c in class_names]# Give the configuration and weight files for the model and load the network using them. modelConfiguration = "yolov3.cfg"; modelWeights = "yolov3.weights";net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights) net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV) net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)# Get the names of the output layers def getOutputsNames(net):# Get the names of all the layers in the networklayersNames = net.getLayerNames()# Get the names of the output layers, i.e. the layers with unconnected outputsreturn [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]# Draw the predicted bounding box def drawPred(classId, conf, left, top, right, bottom):# Draw a bounding box.cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)label = '%.2f' % conf# Get the label for the class name and its confidenceif classes:assert(classId < len(classes))label = '%s:%s' % (classes[classId], label)#Display the label at the top of the bounding boxlabelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)top = max(top, labelSize[1])cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (255, 255, 255), cv.FILLED)cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 1)# Remove the bounding boxes with low confidence using non-maxima suppression def postprocess(frame, outs):frameHeight = frame.shape[0]frameWidth = frame.shape[1]classIds = []confidences = []boxes = []# Scan through all the bounding boxes output from the network and keep only the# ones with high confidence scores. Assign the box's class label as the class with the highest score.classIds = []confidences = []boxes = []for out in outs:for detection in out:scores = detection[5:]classId = np.argmax(scores)confidence = scores[classId]if confidence > confThreshold:center_x = int(detection[0] * frameWidth)center_y = int(detection[1] * frameHeight)width = int(detection[2] * frameWidth)height = int(detection[3] * frameHeight)left = int(center_x - width / 2)top = int(center_y - height / 2)classIds.append(classId)confidences.append(float(confidence))boxes.append([left, top, width, height])# Perform non maximum suppression to eliminate redundant overlapping boxes with# lower confidences.indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)for i in indices:i = i[0]box = boxes[i]left = box[0]top = box[1]width = box[2]height = box[3]drawPred(classIds[i], confidences[i], left, top, left + width, top + height)# Process inputs winName = 'Deep learning object detection in OpenCV' #cv.namedWindow(winName, cv.WINDOW_NORMAL) outputFile = "yolo_out_py.avi" if (args.image):# Open the image fileif not os.path.isfile(args.image):print("Input image file ", args.image, " doesn't exist")sys.exit(1)cap = cv.VideoCapture(args.image)outputFile = args.image[:-4]+'_yolo_out_py.jpg' elif (args.video):# Open the video fileif not os.path.isfile(args.video):print("Input video file ", args.video, " doesn't exist")sys.exit(1)cap = cv.VideoCapture(args.video)outputFile = args.video[:-4]+'_yolo_out_py.avi' else:# Webcam inputcap = cv.VideoCapture(0)# Get the video writer initialized to save the output video if (not args.image):vid_writer = cv.VideoWriter(outputFile, cv.VideoWriter_fourcc('M','J','P','G'), 30, (round(cap.get(cv.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv.CAP_PROP_FRAME_HEIGHT))))while cv.waitKey(1) < 0:# get frame from the videohasFrame, frame = cap.read()# Stop the program if reached end of videoif not hasFrame:print("Done processing !!!")print("Output file is stored as ", outputFile)cv.waitKey(3000)break# Create a 4D blob from a frame.blob = cv.dnn.blobFromImage(frame, 1/255, (inpWidth, inpHeight), [0,0,0], 1, crop=False)# Sets the input to the network net.setInput(blob)# Runs the forward pass to get output of the output layersouts = net.forward(getOutputsNames(net))# Remove the bounding boxes with low confidence postprocess(frame, outs)# Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)t, _ = net.getPerfProfile()label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))# Write the frame with the detection boxesif (args.image):cv.imwrite(outputFile, frame.astype(np.uint8));else:vid_writer.write(frame.astype(np.uint8))#cv.imshow(winName, frame) View Code

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3、用自己的數(shù)據(jù)集訓(xùn)練

• 在工程下新建一個(gè)文件夾VOCdevkit，結(jié)構(gòu)與VOC數(shù)據(jù)集格式保持一致，目錄結(jié)構(gòu)如下所示：

將自己的數(shù)據(jù)圖片放入JPEFImages文件中，

• 生成Annotation下的文件，安裝工具labelImg。安裝過程可參照：

  https://blog.csdn.net/u012746060/article/details/81016993，結(jié)果如下圖：

• 生成ImageSet/Main/4個(gè)文件，在VOC2007下新建一個(gè)test.py文件：

import osimport randomtrainval_percent = 0.2train_percent = 0.8xmlfilepath = 'Annotations'txtsavepath = 'ImageSets\Main'total_xml = os.listdir(xmlfilepath)num = len(total_xml)list = range(num)tv = int(num * trainval_percent)tr = int(tv * train_percent)trainval = random.sample(list, tv)train = random.sample(trainval, tr)ftrainval = open('ImageSets/Main/trainval.txt', 'w')ftest = open('ImageSets/Main/test.txt', 'w')ftrain = open('ImageSets/Main/train.txt', 'w')fval = open('ImageSets/Main/val.txt', 'w')for i in list:name = total_xml[i][:-4] + '\n'if i in trainval:ftrainval.write(name)if i in train:ftest.write(name)else:fval.write(name)else:ftrain.write(name)ftrainval.close()ftrain.close()fval.close()ftest.close() View Code

?運(yùn)行代碼之后，生成如下文件，VOC2007數(shù)據(jù)集制作完成。

• 生成yolo3所需的train.txt,val.txt,test.txt

  生成的數(shù)據(jù)集不能供yolov3直接使用。需要運(yùn)行voc_annotation.py(遷移項(xiàng)目時(shí)必須重新運(yùn)行，涉及路徑問題） ，classes以檢測兩個(gè)類為例（redlight和greenlight），在voc_annotation.py需改你的數(shù)據(jù)集為：
  

  運(yùn)行之后，生成如下三個(gè)文件：

?文件內(nèi)容如圖所示：

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• 修改參數(shù)文件yolo3.cfg

  ? ? ? ? ? ? ? ? ?打開yolo3.cfg文件。搜索yolo(共出現(xiàn)三次)，每次按下圖都要修改：

filter：3*（5+len（classes））

? ? ? ? ? ? ? ? ? classes:你要訓(xùn)練的類別數(shù)（我這里是訓(xùn)練兩類）?

? ? ? ? ? ? ? ? ? random：原來是1，顯存小改為0

• 修改model_data下的voc_classes.txt為自己訓(xùn)練的類別

• ?修改train.py代碼（用下面代碼直接替換原來的代碼）

"""Retrain the YOLO model for your own dataset."""import numpy as npimport keras.backend as Kfrom keras.layers import Input, Lambdafrom keras.models import Modelfrom keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStoppingfrom yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_lossfrom yolo3.utils import get_random_datadef _main():annotation_path = '2007_train.txt'log_dir = 'logs/000/'classes_path = 'model_data/voc_classes.txt'anchors_path = 'model_data/yolo_anchors.txt'class_names = get_classes(classes_path)anchors = get_anchors(anchors_path)input_shape = (416,416) # multiple of 32, hw model = create_model(input_shape, anchors, len(class_names) )train(model, annotation_path, input_shape, anchors, len(class_names), log_dir=log_dir)def train(model, annotation_path, input_shape, anchors, num_classes, log_dir='logs/'):model.compile(optimizer='adam', loss={'yolo_loss': lambda y_true, y_pred: y_pred})logging = TensorBoard(log_dir=log_dir)checkpoint = ModelCheckpoint(log_dir + "ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5",monitor='val_loss', save_weights_only=True, save_best_only=True, period=1)batch_size = 10val_split = 0.1with open(annotation_path) as f:lines = f.readlines()np.random.shuffle(lines)num_val = int(len(lines)*val_split)num_train = len(lines) - num_valprint('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))model.fit_generator(data_generator_wrap(lines[:num_train], batch_size, input_shape, anchors, num_classes),steps_per_epoch=max(1, num_train//batch_size),validation_data=data_generator_wrap(lines[num_train:], batch_size, input_shape, anchors, num_classes),validation_steps=max(1, num_val//batch_size),epochs=500,initial_epoch=0)model.save_weights(log_dir + 'trained_weights.h5')def get_classes(classes_path):with open(classes_path) as f:class_names = f.readlines()class_names = [c.strip() for c in class_names]return class_namesdef get_anchors(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 create_model(input_shape, anchors, num_classes, load_pretrained=False, freeze_body=False,weights_path='model_data/yolo_weights.h5'):K.clear_session() # get a new session image_input = Input(shape=(None, None, 3))h, w = input_shapenum_anchors = len(anchors)y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \num_anchors//3, num_classes+5)) for l in range(3)]model_body = yolo_body(image_input, num_anchors//3, num_classes)print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))if load_pretrained:model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)print('Load weights {}.'.format(weights_path))if freeze_body:# Do not freeze 3 output layers. num = len(model_body.layers)-7for i in range(num): model_body.layers[i].trainable = Falseprint('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})([*model_body.output, *y_true])model = Model([model_body.input, *y_true], model_loss)return modeldef data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):n = len(annotation_lines)np.random.shuffle(annotation_lines)i = 0while True:image_data = []box_data = []for b in range(batch_size):i %= nimage, box = get_random_data(annotation_lines[i], input_shape, random=True)image_data.append(image)box_data.append(box)i += 1image_data = np.array(image_data)box_data = np.array(box_data)y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)yield [image_data, *y_true], np.zeros(batch_size)def data_generator_wrap(annotation_lines, batch_size, input_shape, anchors, num_classes):n = len(annotation_lines)if n==0 or batch_size<=0: return Nonereturn data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)if __name__ == '__main__':_main() View Code

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?替換完成后，千萬千萬值得注意的是，因?yàn)槌绦蛑杏衛(wèi)ogs/000/目錄，你需要?jiǎng)?chuàng)建這樣一個(gè)目錄，這個(gè)目錄的作用就是存放自己的數(shù)據(jù)集訓(xùn)練得到的模型。不然程序運(yùn)行到最后會(huì)因?yàn)檎也坏皆撀窂蕉l(fā)生錯(cuò)誤。

轉(zhuǎn)載于:https://www.cnblogs.com/wyx501/p/10644248.html

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