Day #287 - Dlib Custom Detector

Learning's

• Aspect ratio needs to be maintained
• Trained for only one type of Object (Vim Dishwasher)
• Trained with just a few images 30 images of train and 5 images of test
• Environment - Windows 10, After all the Setup and Steps, It would take ~45 mins to develop for label, training, and testing

Data Set

• Custom Shelf and Vim dishwash Detection

Image Pre-requisites

• I resized data to 512 x 512 format to be consistent
• Place all images in train and test directory respectively before next steps

Label Training Data

• Img Lab Installation - Refer previous post
• Use Shift key to select rectangle
• Use Alt D to delete region
• Each tool has its own commands

	Step 1 - Specify XML and image path
	=====================================
	E:\Code_Repo\dlib\tools\imglab\build\Release\imglab.exe -c E:\Code_Repo\dlib_obj_count\ShelfData\train\train.xml E:\Code_Repo\dlib_obj_count\ShelfData\train
	Step 2
	=======
	cd E:\Code_Repo\dlib_obj_count
	Step 3
	=======
	imglab.exe E:\Code_Repo\dlib_obj_count\ShelfData\train\train.xml

view raw trainingsteps.txt hosted with ❤ by GitHub

Label Testing Data

	Step 1
	========
	E:\Code_Repo\dlib\tools\imglab\build\Release\imglab.exe -c E:\Code_Repo\dlib_obj_count\ShelfData\test\test.xml E:\Code_Repo\dlib_obj_count\ShelfData\test
	Step 2
	======
	cd E:\Code_Repo\dlib_obj_count
	Step 3
	======
	imglab.exe E:\Code_Repo\dlib_obj_count\ShelfData\test\test.xml

view raw testingsteps.txt hosted with ❤ by GitHub

Training Code

	#Base code - https://gist.github.com/atotto/c1ccbfa44ee70a476816f6389834945e
	#Minor changes for my requirements
	import os
	import sys
	import dlib
	options = dlib.simple_object_detector_training_options()
	options.add_left_right_image_flips = False
	options.C = 5
	options.num_threads = 2
	options.be_verbose = True
	training_xml_path = r'E:\Code_Repo\dlib_obj_count\ShelfData\train\train.xml'
	testing_xml_path = r'E:\Code_Repo\dlib_obj_count\ShelfData\test\test.xml'
	dlib.train_simple_object_detector(training_xml_path, r'E:\Code_Repo\dlib_obj_count\ShelfData\detector.svm', options)
	print("")
	print("Training accuracy")
	print(dlib.test_simple_object_detector(training_xml_path, r'E:\Code_Repo\dlib_obj_count\ShelfData\detector.svm'))
	print("Testing accuracy")
	print(dlib.test_simple_object_detector(testing_xml_path, r'E:\Code_Repo\dlib_obj_count\ShelfData\detector.svm'))

view raw train_dlib.py hosted with ❤ by GitHub

Test Code

	import dlib
	import cv2
	detector = dlib.simple_object_detector(r'E:\Code_Repo\dlib_obj_count\ShelfData\detector.svm')
	filepath = r'E:\Code_Repo\dlib_obj_count\ShelfData\test\25.png'
	img = cv2.imread(filepath,1)
	dets = detector(img)
	for d in dets:
	cv2.rectangle(img, (d.left(), d.top()), (d.right(), d.bottom()), (0, 0, 255), 2)
	# Display the resulting frame
	cv2.imshow("frame",img)
	cv2.waitKey(0)
	# When everything done, release the capture
	cv2.destroyAllWindows()

view raw test_dlib.py hosted with ❤ by GitHub

Result

Make your own Haar Cascade on Windows | Quick & Simple

Making your own Haar Cascade Intro - OpenCV with Python for Image and Video Analysis 

Happy Learning!!!