# 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)

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()

"""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()