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facialsys_backend.py
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facialsys_backend.py
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import os
import numpy as np
# To prevent conflicts with pyqt6
os.environ["QT_API"] = "PyQt5"
# To solve the problem of the icons with relative path
os.chdir(os.path.dirname(os.path.abspath(__file__)))
import os
from typing import *
import cv2
import numpy as np
# in CMD: pip install qdarkstyle -> pip install pyqtdarktheme
import qdarktheme
from facialsys_ui import Ui_MainWindow
from utils.features import *
from PIL import Image
from PyQt5 import QtGui
# imports
from PyQt5.QtWidgets import QApplication, QFileDialog, QMainWindow, QMessageBox
from utils.detection_utils import *
from utils.helper_functions import *
from utils.recognition_utils import *
class BackendClass(QMainWindow, Ui_MainWindow):
def __init__(self):
super().__init__()
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
### ==== PCA ==== ###
self.PCA_test_image_index = 30
self.face_recognition_threshold = 2900
# Configured by the user
self.structure_number = "one" # Dataset folder, containing subfolders named after subjects, each containing a minimum of 5 images, with extra images limited to the quantity of the smallest subject folder.
self.dataset_dir = "../face_recognition_dataset"
self.faces_train, self.faces_test, self.first_image_size = (
store_dataset_method_one(self.dataset_dir)
)
(
self.train_faces_matrix,
self.train_faces_labels,
self.PCA_weights,
self.PCA_eigen_faces
) = train_pca(self.faces_train)
self.test_faces_list, self.test_labels_list = test_faces_and_labels(
self.faces_test
)
self.PCA_test_img = self.test_faces_list[self.PCA_test_image_index]
self.display_image(
self.test_faces_list[self.PCA_test_image_index],
self.ui.PCA_input_figure_canvas,
"Query",
True,
)
# Test size is 20% by default
# PCA cumulativa variance is 90% by default
# PCA Buttons
self.ui.toggle.clicked.connect(self.toggle_PCA_test_image)
self.ui.apply_PCA.clicked.connect(self.apply_PCA)
### ==== Detection ==== ###
self.detection_original_image = None
self.detection_thumbnail_image = None
self.detection_original_float = None
self.detection_grayscale_image = None
self.detection_integral_image = None
self.ui.apply_detection.setEnabled(False)
self.features_per_window = get_number_of_features_per_window()
self.detection_models = upload_cascade_adaboost("../15x15_window_size_model")
self.weak_classifiers = self.detection_models["1st"]
self.weak_classifiers_2 = self.detection_models["2nd"]
self.weak_classifiers_3 = self.detection_models["3rd"]
self.last_stage_threshold = 0
self.ui.apply_detection.clicked.connect(self.apply_face_detection)
self.ui.last_stage_threshold_spinbox.valueChanged.connect(
self.get_face_detection_parameters
)
self.last_stage_info = None
self.detection_output_image = None
### ==== General ==== ###
# Connect menu action to load_image
self.ui.actionImport_Image.triggered.connect(self.load_image)
# Change the icon and title of the app
self.change_the_icon()
def change_the_icon(self):
self.setWindowIcon(QtGui.QIcon("assets/app_icon.png"))
self.setWindowTitle("FacialSys")
def load_image(self):
# Open file dialog if file_path is not provided
file_path, _ = QFileDialog.getOpenFileName(
self,
"Open Image",
"Images",
"Image Files (*.png *.jpg *.jpeg *.bmp *.ppm *.pgm)",
)
if file_path and isinstance(file_path, str):
# Read the matrix, convert to rgb
img = cv2.imread(file_path, 1)
img = convert_BGR_to_RGB(img)
current_tab = self.ui.tabWidget.currentIndex()
if current_tab == 0:
self.PCA_test_img = convert_to_gray(img)
self.display_image(
self.PCA_test_img,
self.ui.PCA_input_figure_canvas,
"Query",
True,
)
self.apply_PCA()
elif current_tab == 1:
self.detection_original_image = Image.open(file_path)
self.detection_thumbnail_image = resize_image_object(
self.detection_original_image, (384, 288)
)
self.detection_original_float = to_float_array(
self.detection_thumbnail_image
)
self.detection_grayscale_image = gleam_converion(
self.detection_original_float
)
self.detection_integral_image = integrate_image(
self.detection_grayscale_image
)
self.display_image(
self.detection_original_float,
self.ui.detection_input_figure_canvas,
"Input Image",
False,
)
self.ui.apply_detection.setEnabled(True)
def display_image(
self, image, canvas, title, grey, hist_or_not=False, axis_disabled="off"
):
""" "
Description:
- Plots the given (image) in the specified (canvas)
"""
canvas.figure.clear()
ax = canvas.figure.add_subplot(111)
if not hist_or_not:
if not grey:
ax.imshow(image)
elif grey:
ax.imshow(image, cmap="gray")
else:
self.ui.histogram_global_thresholds_label.setText(" ")
if grey:
ax.hist(image.flatten(), bins=256, range=(0, 256), alpha=0.75)
for thresh in self.global_thresholds[0]:
ax.axvline(x=thresh, color="r")
thresh = int(thresh)
# Convert the threshold to string with 3 decimal places and add it to the label text
current_text = self.ui.histogram_global_thresholds_label.text()
self.ui.histogram_global_thresholds_label.setText(
current_text + " " + str(thresh)
)
else:
image = convert_to_gray(image)
ax.hist(image.flatten(), bins=256, range=(0, 256), alpha=0.75)
for thresh in self.global_thresholds[0]:
ax.axvline(x=thresh, color="r")
thresh = int(thresh)
# Convert the threshold to string with 3 decimal places and add it to the label text
current_text = self.ui.histogram_global_thresholds_label.text()
self.ui.histogram_global_thresholds_label.setText(
current_text + " " + str(thresh)
)
ax.axis(axis_disabled)
ax.set_title(title)
canvas.figure.subplots_adjust(left=0.1, right=0.90, bottom=0.08, top=0.95)
canvas.draw()
# @staticmethod
def display_selection_dialog(self, image):
"""
Description:
- Shows a message dialog box to determine whether this is the a template or the target image in SIFT
Args:
- image: The image to be displayed.
"""
msgBox = QMessageBox()
msgBox.setIcon(QMessageBox.Question)
msgBox.setText("Select an Image")
msgBox.setWindowTitle("Image Selection")
msgBox.setMinimumWidth(150)
# Set custom button text
msgBox.setStandardButtons(QMessageBox.Yes | QMessageBox.No)
msgBox.button(QMessageBox.Yes).setText("Target Image")
msgBox.button(QMessageBox.No).setText("Template")
# Executing the message box
response = msgBox.exec()
if response == QMessageBox.Rejected:
return
else:
if response == QMessageBox.Yes:
self.sift_target_image = image
self.display_image(
image,
self.ui.input_1_figure_canvas,
"Target Image",
False,
)
elif response == QMessageBox.No:
self.sift_template_image = image
self.display_image(
image,
self.ui.input_2_figure_canvas,
"Template Image",
False,
)
## ============== PCA ============== ##
def apply_PCA(self):
self.ui.PCA_output_figure.clear()
test_image = self.PCA_test_img.copy()
best_match_subject, best_match_subject_distance, best_match_indx = (
recognise_face(
test_image,
self.first_image_size,
self.train_faces_matrix,
self.train_faces_labels,
self.PCA_weights,
self.PCA_eigen_faces,
self.face_recognition_threshold,
)
)
if best_match_subject_distance < self.face_recognition_threshold:
# Visualize
self.display_image(
self.train_faces_matrix[best_match_indx].reshape(self.first_image_size),
self.ui.PCA_output_figure_canvas,
f"Best match:{best_match_subject}",
True,
)
else:
self.display_image(
np.full_like(
self.train_faces_matrix[0].reshape(self.first_image_size),
255,
dtype=np.uint8,
),
self.ui.PCA_output_figure_canvas,
"No matching subject",
True,
)
self.ui.PCA_output_figure_canvas.draw()
def toggle_PCA_test_image(self):
self.ui.PCA_output_figure.clear()
self.PCA_test_image_index += 1
test_labels_list = self.test_labels_list.copy()
self.PCA_test_image_index = self.PCA_test_image_index % len(test_labels_list)
self.PCA_test_img = self.test_faces_list[self.PCA_test_image_index]
self.display_image(
self.PCA_test_img,
self.ui.PCA_input_figure_canvas,
"Query",
True,
)
## ============== Detection ============== ##
def get_face_detection_parameters(self):
self.last_stage_threshold = self.ui.last_stage_threshold_spinbox.value()
def apply_face_detection(self):
self.get_face_detection_parameters()
rows, cols = self.detection_integral_image.shape[:2]
HALF_WINDOW = WINDOW_SIZE // 2
face_positions_1 = list()
face_positions_2 = list()
face_positions_3 = list()
face_positions_3_strength = list()
normalized_integral = integrate_image(
normalize(self.detection_grayscale_image)
) # to reduce lighting variance
for row in range(HALF_WINDOW + 1, rows - HALF_WINDOW):
for col in range(HALF_WINDOW + 1, cols - HALF_WINDOW):
curr_window = normalized_integral[
row - HALF_WINDOW - 1 : row + HALF_WINDOW + 1,
col - HALF_WINDOW - 1 : col + HALF_WINDOW + 1,
]
# First cascade stage
probably_face, _ = strong_classifier(curr_window, self.weak_classifiers)
if probably_face < 0.5:
continue
face_positions_1.append((row, col))
probably_face, strength = strong_classifier(
curr_window, self.weak_classifiers_2
)
if probably_face < 0.5:
continue
face_positions_2.append((row, col))
probably_face, strength = strong_classifier(
curr_window, self.weak_classifiers_3
)
if probably_face < 0.5:
continue
face_positions_3.append((row, col))
face_positions_3_strength.append(strength)
self.last_stage_info = (face_positions_3, face_positions_3_strength)
self.truncate_candidates()
def render_candidates(self, image: Image.Image, candidates: List[Tuple[int, int]]):
HALF_WINDOW = WINDOW_SIZE // 2
canvas = to_float_array(image.copy())
for row, col in candidates:
canvas[
row - HALF_WINDOW - 1 : row + HALF_WINDOW, col - HALF_WINDOW - 1, :
] = [1.0, 0.0, 0.0]
canvas[
row - HALF_WINDOW - 1 : row + HALF_WINDOW, col + HALF_WINDOW - 1, :
] = [1.0, 0.0, 0.0]
canvas[
row - HALF_WINDOW - 1, col - HALF_WINDOW - 1 : col + HALF_WINDOW, :
] = [1.0, 0.0, 0.0]
canvas[
row + HALF_WINDOW - 1, col - HALF_WINDOW - 1 : col + HALF_WINDOW, :
] = [1.0, 0.0, 0.0]
self.detection_output_image = canvas
self.display_image(
self.detection_output_image,
self.ui.detection_output_figure_canvas,
"Output Image",
False,
)
def truncate_candidates(self):
filtered_faces = list()
expected_faces = np.argwhere(
np.array(self.last_stage_info[1]) > self.last_stage_threshold
)
for i in range(len(self.last_stage_info[0])):
if [i] in expected_faces:
filtered_faces.append(self.last_stage_info[0][i])
self.render_candidates(self.detection_thumbnail_image, filtered_faces)
if __name__ == "__main__":
import sys
app = QApplication(sys.argv)
MainWindow = BackendClass()
MainWindow.show()
qdarktheme.setup_theme("dark")
sys.exit(app.exec_())