mirror of
https://github.com/jlengrand/Ivolution.git
synced 2026-03-10 08:21:18 +00:00
106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
"""
|
|
.. module:: FaceParams
|
|
:platform: Unix, Windows
|
|
:synopsis: Simple class used to store parameters used for Face detection.
|
|
|
|
.. moduleauthor:: Julien Lengrand-Lambert <jlengrand@gmail.com>
|
|
|
|
"""
|
|
import cv
|
|
import os
|
|
|
|
import logging
|
|
|
|
import training_types
|
|
|
|
|
|
class FaceParams(object):
|
|
'''
|
|
Simple class used to store parameters used for Face detection
|
|
'''
|
|
def __init__(self, xml_folder, input_folder, output_folder, training_type="frontal_face", sort="name", mode="conservative",speed=1):
|
|
"""
|
|
Creates dictionary for all types of training files
|
|
some of them shall never be used. Perhaps would it be good to lower the dict size, or hide some of them
|
|
postpend .xml
|
|
|
|
:param xml_folder: the location where xml files are located
|
|
:type xml_folder: string
|
|
:param training_type: the type of profile we are going to use
|
|
:type training_type: string
|
|
|
|
:param input_folder: the location where images are located
|
|
:type input_folder: string
|
|
:param output_folder: the location where the video will be saved
|
|
:type output_folder: string
|
|
:param speed: the time delay between frames in the video
|
|
:type speed: int
|
|
:param mode: the creation mode of the video. Defines whether images are cropped, or black borders are added.
|
|
:type mode: string
|
|
:param sort: the method used to sort images chronologically
|
|
:type sort: string
|
|
"""
|
|
|
|
self.input_folder = input_folder
|
|
self.output_folder = output_folder
|
|
self.speed = speed
|
|
self.mode = mode # conservative or crop
|
|
self.sort = sort # name or exif
|
|
|
|
cascade_name = training_types.simple_set[training_type] + ".xml"
|
|
# Setting up some default parameters for Face Detection
|
|
self.face_cascade = cv.Load(os.path.join(xml_folder, cascade_name))
|
|
|
|
# To be defined more precisely
|
|
self.min_size = (20, 20)
|
|
self.image_scale = 2 # Image scaling chosen for classification (2)
|
|
self.haar_scale = 1.2 # Haar scaling chosen for classification (1.2)
|
|
self.min_neighbors = 2 # the Minimum number of neighbors to be defined (2)
|
|
self.haar_flags = 0 # the chosen number of haar flags (0)
|
|
|
|
self.log()
|
|
|
|
def __str__(self):
|
|
"""
|
|
More convenient print method
|
|
"""
|
|
print "---------"
|
|
print "Selected parameters for your Facemovie:"
|
|
print "Input Folder: %s" % (self.input_folder)
|
|
print "Output Folder: %s" % (self.output_folder)
|
|
print "Speed for movie: %s" % (["slow", "medium", "fast"][self.speed])
|
|
print "Video Mode: %s" % (self.mode)
|
|
print "Files sorting method: %s" % (self.sort)
|
|
print "-----"
|
|
print "Selected parameters for Face Detection:"
|
|
print "Selected cascade for Face detection : %s" % ("haarcascade_frontalface_alt")
|
|
print "Minimum Size (x, y): %d, %d" % (self.min_size[0], self.min_size[1])
|
|
print "Image scaling: %d)" % (self.image_scale)
|
|
print "Haar scaling: %f" % (self.haar_scale)
|
|
print "Number of Haar flags: %d" % (self.haar_flags)
|
|
print "Minimum number of neighbors: %d" % (self.min_neighbors)
|
|
print "---------"
|
|
|
|
def log(self):
|
|
"""
|
|
Log configuration
|
|
"""
|
|
my_logger = logging.getLogger('FileLog')
|
|
params_str = "---------"
|
|
params_str += "Selected parameters for your Facemovie:"
|
|
params_str += "Input Folder: %s" % (self.input_folder)
|
|
params_str += "Output Folder: %s" % (self.output_folder)
|
|
params_str += "Speed for movie: %s" % (["slow", "medium", "fast"][self.speed])
|
|
params_str += "Video Mode: %s" % (self.mode)
|
|
params_str += "Files sorting method: %s" % (self.sort)
|
|
params_str += "-----"
|
|
params_str += "Selected parameters for Face Detection:"
|
|
params_str += "Selected cascade for Face detection : %s" % ("haarcascade_frontalface_alt")
|
|
params_str += "Minimum Size (x, y): %d, %d" % (self.min_size[0], self.min_size[1])
|
|
params_str += "Image scaling: %d)" % (self.image_scale)
|
|
params_str += "Haar scaling: %f" % (self.haar_scale)
|
|
params_str += "Number of Haar flags: %d" % (self.haar_flags)
|
|
params_str += "Minimum number of neighbors: %d" % (self.min_neighbors)
|
|
params_str += "---------"
|
|
my_logger.debug(params_str)
|