Day # 276 - Segmentation, Age-Gender Estimations

This post is based on current learning's exploring segmentation and age/gender detection.

Experiment #1 - Used the existing model and took faces from edouardjanssens.com for both men / women from 1-100 in increments of 5. The actual result and predicted chart summary

Demo Code

	#http://edouardjanssens.com/art/1-to-100-years/men/
	#http://edouardjanssens.com/art/1-to-100-years/women/
	import cv2
	print(cv2.__version__)
	import glob, os
	from pyagender import PyAgender
	agender = PyAgender()
	def Approach1():
	f = open(r'C:\Data_ML\Data_Scrub\Results.csv','w')
	os.chdir(r"C:\Data_ML\Data_Scrub\Boys")
	for file in glob.glob("*.*"):
	faces = agender.detect_genders_ages(cv2.imread(file))
	#print(faces)
	print("_________________________________________________")
	print(file)
	for data in faces:
	for key in data:
	if(key=='gender' or key == 'age'):
	if key=='gender':
	if data[key] <0.5:
	print(key)
	print('Male')
	val1 = 'Male'
	else:
	print(key)
	print('Female')
	val1 = 'Female'
	if key=='age':
	print(key)
	print(data[key])
	val2= data[key]
	print("_________________________________________________")
	data = file + "," + str(val1) + "," + str(val2) + "\n"
	f.write(data)
	f.close()

view raw Test_age_gender.py hosted with ❤ by GitHub

Experiment #2 - Getting Started with Segmentation, Experimented with Repo - https://github.com/divamgupta/image-segmentation-keras

Few tweaks in code

Demo Code

	#https://github.com/divamgupta/image-segmentation-keras
	import keras_segmentation
	model = keras_segmentation.pretrained.pspnet_50_ADE_20K() # load the pretrained model trained on ADE20k dataset
	# load 3 pretrained models
	out = model.predict_segmentation(
	inp=r"E:\Code_Repo\image-segmentation-keras\sample_images\Data1.jpeg",
	out_fname=r"E:\Code_Repo\image-segmentation-keras\sample_images\out1.png"
	)
	model2 = keras_segmentation.pretrained.pspnet_101_cityscapes()
	out = model2.predict_segmentation(
	inp=r"E:\Code_Repo\image-segmentation-keras\sample_images\Data1.jpeg",
	out_fname=r"E:\Code_Repo\image-segmentation-keras\sample_images\out2.png"
	)
	model3 = keras_segmentation.pretrained.pspnet_101_voc12()
	out = model3.predict_segmentation(
	inp=r"E:\Code_Repo\image-segmentation-keras\sample_images\Data1.jpeg",
	out_fname=r"E:\Code_Repo\image-segmentation-keras\sample_images\out3.png"
	)

view raw segdemo.py hosted with ❤ by GitHub

Happy Learning!!!