Deep Learning 7-Object Detection with YOLO

Learn A-Z Deep Learning in 15 Days

In this Lecture, will learn about Objection Detection with Retinanet


You Only Look Once

YOLO first takes an input image. The framework then divides the input image into grids (say a n X n grid- Each grid predict):

• Predicts B boundary boxes and each box has one box confidence score Pr(Object) ∗ IOU
• Detects one object only regardless of the number of boxes B,
• Predicts C conditional class probabilities (one per class for the likeliness of the object class).
• The bounding boxes having the class probability above a threshold value is selected and used to locate the object within the image
• Total loss = classification loss+ localization loss (errors between the predicted boundary box and the ground truth) + confidence loss (the objectness of the box)

Data Preprocessing with Kitti dataset

Categories                    frequency

  Car----------------------28742

Pedestrian----------------4487

  Van------------------------2914

Cyclist-------------------1627

Truck--------------------1094

Misc----------------------973

Tram----------------------511

Person_sitting------------222

• Total annotation count = 40570
• Total images = 7481

import pandas as pd

path="kitti/training/label_2/"

df = pd.DataFrame()

# iterate the list of files in the directory 'label_2'
for filename in os.listdir(path): 
    
    filepath= os.path.join(path,filename)
# read file in csv format  
    pdtxtdata=pd.read_csv(filepath, delimiter = ' ', header=None)
    
# create the folder 'updatelabel' in the current folder    
    with open('./updatelabel/'+filename,'a') as f: 
        
# iloc takes the values at 0,4,5,6,7 i,e class_name, xmin, ymin, xmax and ymax.
        for label, xmin, ymin, xmax, ymax in pdtxtdata.iloc[:,[0,4,5,6,7]].values:
# categories 'Pedestrian' and "Person_sitting" are considered
            if label=='Pedestrian' or label=="Person_sitting":
                f.write(str(0)+' '+str(xmin)+' '+str(ymin)+' '+str(xmax)+' '+str(ymax)+'\n')

# Remove the files with 0 size
import os
path="./updatelabel/"
# Iterate the list of files in the directory 'updatelabel'
for filename in os.listdir(path):
    filepath= os.path.join(path,filename)
    if os.path.getsize(filepath)==0:
        os.remove(filepath)

# Copy and change the file name 
import os
import shutil
path="./updatelabel/"
for filename in os.listdir(path):
    shutil.copy("/kitti/training/image_2/"+filename.split('.')[0]+'.png',"./yoloimagelabel/" +filename.split('.')[0]+'.png')

# Convert the kitti to Yolo format i.e (xmin, ymin, xmax ,ymax) to (x, y, w, h) 
import pandas as pd
path="./udatelabel/"
df = pd.DataFrame()
for filename in os.listdir(path):
    filepath= os.path.join(path,filename)
    image= cv2.imread("./image/"+filename.split('.')[0]+'.png')
    image_width= image.shape[1]
    image_height= image.shape[0]
    pdtxtdata=pd.read_csv(filepath, delimiter = ' ', header=None)
    with open('./yoloimagelabel/'+filename,'a') as f:
        for label, xmin, ymin, xmax, ymax in pdtxtdata.iloc[:,[0,1,2,3,4]].values:
            f.write(str(0)+' '+str('%.6f' % float((xmin+xmax)/(2.0*image_width)))+' '+str('%.6f' % float((ymin+ymax)/(2.0*image_height)))+' '+str('%.6f' % float((xmax-xmin)/image_width))+' '+str('%.6f' % float((ymax-ymin)/image_height))+'\n')

Total Images = 1796 & Total Annotation = 4709

# Split the train & test 
# Generate the train.txt and test.txt
import glob, os

current_dir = 'dataset'
# Percentage of images to be used for the test set
percentage_test = 20;
# Create and/or truncate train.txt and test.txt
file_train = open('train.txt', 'w')  
file_test = open('test.txt', 'w')
# Populate train.txt and test.txt
counter = 1  
index_test = round(100 / percentage_test)  
for pathAndFilename in glob.glob(os.path.join(current_dir, "*.*g")):  
if counter == index_test:
counter = 1
file_test.write(current_dir + "/" + os.path.basename(pathAndFilename) + "\n")
else:
file_train.write(current_dir + "/" + os.path.basename(pathAndFilename) + "\n")
counter = counter + 1
file_test.close()
file_train.close()

Run the below code on google colab

!git clone https://github.com/AlexeyAB/darknet.git

cd /content/darknet

Makefile

• Download the Makefile
• Change GPU = 1, CUDNN = 1 & OPENCV = 1
• Upload the Modified Makefile in darknet folder

!make

!./darknet

Train & Test file

• Upload the train & test file in darknet folder

cd /content/darknet/cfg

Obj.data

• Create obj.data file ( as show figure)

1. classes = 1
2. train = train.txt
3. valid = test.txt
4. names = obj.names
5. backup = backup/

• Upload the in cfg folder

Obj.names

• Create obj.data file and
• write the name of all the categories ( as show figure)
  1. Person

• Upload the in cfg folder
• 
  

Yolov3 Network¶

• Download the Yolov3.cfg from cfg folder
• classes and filters params of [yolo] and [convolutional] layers that are just before the [yolo] layers
• filters=(classes + 5) * 3. For a single class we should set filters= (1+5)*3 = 18
  1. classes = 1
  2. filters = 18
• Upload the Yolov3.cfg to cfg folder

Download Weight

cd /content/darknet
!wget https://pjreddie.com/media/files/darknet19_448.conv.23

!./darknet detector train cfg/obj.data cfg/yolov3.cfg darknet19_448.conv.23 -dont_show 0

In the next blog, we will cover  Deeping Learning NLP.
https://sngurukuls247.blogspot.com/2018/09/python-ninja-bootcamp-1-course.html

                                                                                                                                                

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