Face Recognition

Face_Recog/.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/

Face_Recog/.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml

Face_Recog/.idea/Face_Recog.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

Face_Recog/.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

Face_Recog/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

Face_Recog/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Face_Recog.iml" filepath="$PROJECT_DIR$/.idea/Face_Recog.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

Face_Recog/LICENSE

+MIT License
+
+Copyright (c) 2018 Muhammad Ali Zia
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Face_Recog/classifier.py

+import numpy as np
+from PIL import  Image
+import os, cv2
+
+# Method to train custom classifier to recognize face
+def train_classifer(data_dir):
+    # Read all the images in custom data-set
+    path = [os.path.join(data_dir, f) for f in os.listdir(data_dir)]
+    faces = []
+    ids = []
+
+    # Store images in a numpy format and ids of the user on the same index in imageNp and id lists
+    for image in path:
+        img = Image.open(image).convert('L')
+        imageNp = np.array(img, 'uint8')
+        id = int(os.path.split(image)[1].split(".")[1])
+
+        faces.append(imageNp)
+        ids.append(id)
+
+    ids = np.array(ids)
+
+    # Train and save classifier
+    clf = cv2.face.LBPHFaceRecognizer_create()
+    clf.train(faces, ids)
+    clf.write("classifier.xml")
+
+
+train_classifer("data")
\ No newline at end of file

Face_Recog/collect_training_data.py

+import cv2
+
+# Method to generate dataset to recognize a person
+def generate_dataset(img, id, img_id):
+    # write image in data dir
+    cv2.imwrite("data/user."+str(id)+"."+str(img_id)+".jpg", img)
+
+# Method to draw boundary around the detected feature
+def draw_boundary(img, classifier, scaleFactor, minNeighbors, color, text):
+    # Converting image to gray-scale
+    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # detecting features in gray-scale image, returns coordinates, width and height of features
+    features = classifier.detectMultiScale(gray_img, scaleFactor, minNeighbors)
+    coords = []
+    # drawing rectangle around the feature and labeling it
+    for (x, y, w, h) in features:
+        cv2.rectangle(img, (x,y), (x+w, y+h), color, 2)
+        cv2.putText(img, text, (x, y-4), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 1, cv2.LINE_AA)
+        coords = [x, y, w, h]
+    return coords
+
+# Method to detect the features
+def detect(img, faceCascade, img_id):
+    color = {"blue":(255,0,0), "red":(0,0,255), "green":(0,255,0), "white":(255,255,255)}
+    coords = draw_boundary(img, faceCascade, 1.1, 10, color['blue'], "Face")
+    # If feature is detected, the draw_boundary method will return the x,y coordinates and width and height of rectangle else the length of coords will be 0
+    if len(coords)==4:
+        # Updating region of interest by cropping image
+        roi_img = img[coords[1]:coords[1]+coords[3], coords[0]:coords[0]+coords[2]]
+        # Assign unique id to each user
+        user_id = 1
+        # img_id to make the name of each image unique
+        generate_dataset(roi_img, user_id, img_id)
+
+    return img
+
+
+# Loading classifiers
+faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+
+# Capturing real time video stream. 0 for built-in web-cams, 0 or -1 for external web-cams
+video_capture = cv2.VideoCapture(-1)
+
+# Initialize img_id with 0
+img_id = 0
+
+while True:
+    if img_id % 50 == 0:
+        print("Collected ", img_id," images")
+    # Reading image from video stream
+    _, img = video_capture.read()
+    # Call method we defined above
+    img = detect(img, faceCascade, img_id)
+    # Writing processed image in a new window
+    cv2.imshow("face detection", img)
+    img_id += 1
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# releasing web-cam
+video_capture.release()
+# Destroying output window
+cv2.destroyAllWindows()
\ No newline at end of file

Face_Recog/face_detection.py

+import cv2
+
+# Method to draw boundary around the detected feature
+def draw_boundary(img, classifier, scaleFactor, minNeighbors, color, text):
+    # Converting image to gray-scale
+    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # detecting features in gray-scale image, returns coordinates, width and height of features
+    features = classifier.detectMultiScale(gray_img, scaleFactor, minNeighbors)
+    coords = []
+    # drawing rectangle around the feature and labeling it
+    for (x, y, w, h) in features:
+        cv2.rectangle(img, (x,y), (x+w, y+h), color, 2)
+        cv2.putText(img, text, (x, y-4), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 1, cv2.LINE_AA)
+        coords = [x, y, w, h]
+    return coords
+
+
+# Method to detect the features
+def detect(img, faceCascade, eyeCascade, noseCascade, mouthCascade):
+    color = {"blue":(255,0,0), "red":(0,0,255), "green":(0,255,0), "white":(255,255,255)}
+    coords = draw_boundary(img, faceCascade, 1.1, 10, color['blue'], "Face")
+    # If feature is detected, the draw_boundary method will return the x,y coordinates and width and height of rectangle else the length of coords will be 0
+    if len(coords)==4:
+        # Updating region of interest by cropping image
+        roi_img = img[coords[1]:coords[1]+coords[3], coords[0]:coords[0]+coords[2]]
+        # Passing roi, classifier, scaling factor, Minimum neighbours, color, label text
+        coords = draw_boundary(roi_img, eyeCascade, 1.1, 12, color['red'], "Eye")
+        coords = draw_boundary(roi_img, noseCascade, 1.1, 4, color['green'], "Nose")
+        coords = draw_boundary(roi_img, mouthCascade, 1.1, 20, color['white'], "Mouth")
+    return img
+
+
+# Loading classifiers
+faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+eyesCascade = cv2.CascadeClassifier('haarcascade_eye.xml')
+noseCascade = cv2.CascadeClassifier('Nariz.xml')
+mouthCascade = cv2.CascadeClassifier('Mouth.xml')
+
+# Capturing real time video stream. 0 for built-in web-cams, 0 or -1 for external web-cams
+video_capture = cv2.VideoCapture(-1)
+
+while True:
+    # Reading image from video stream
+    _, img = video_capture.read()
+    # Call method we defined above
+    img = detect(img, faceCascade, eyesCascade, noseCascade, mouthCascade)
+    # Writing processed image in a new window
+    cv2.imshow("face detection", img)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# releasing web-cam
+video_capture.release()
+# Destroying output window
+cv2.destroyAllWindows()
\ No newline at end of file

Face_Recog/recognize.py

+import cv2
+
+
+def draw_boundary(img, classifier, scaleFactor, minNeighbors, color, text, clf):
+    # Converting image to gray-scale
+    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # detecting features in gray-scale image, returns coordinates, width and height of features
+    features = classifier.detectMultiScale(gray_img, scaleFactor, minNeighbors)
+    coords = []
+    # drawing rectangle around the feature and labeling it
+    for (x, y, w, h) in features:
+        cv2.rectangle(img, (x,y), (x+w, y+h), color, 2)
+        # Predicting the id of the user
+        id, _ = clf.predict(gray_img[y:y+h, x:x+w])
+        # Check for id of user and label the rectangle accordingly
+        if id==1:
+            cv2.putText(img, "Ali", (x, y-4), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 1, cv2.LINE_AA)
+        coords = [x, y, w, h]
+
+    return coords
+
+# Method to recognize the person
+def recognize(img, clf, faceCascade):
+    color = {"blue": (255, 0, 0), "red": (0, 0, 255), "green": (0, 255, 0), "white": (255, 255, 255)}
+    coords = draw_boundary(img, faceCascade, 1.1, 10, color["white"], "Face", clf)
+    return img
+
+
+# Loading classifier
+faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+# Loading custom classifier to recognize
+clf = cv2.face.LBPHFaceRecognizer_create()
+clf.read("classifier.yml")
+
+# Capturing real time video stream. 0 for built-in web-cams, 0 or -1 for external web-cams
+video_capture = cv2.VideoCapture(-1)
+
+while True:
+    # Reading image from video stream
+    _, img = video_capture.read()
+    # Call method we defined above
+    img = recognize(img, clf, faceCascade)
+    # Writing processed image in a new window
+    cv2.imshow("face detection", img)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# releasing web-cam
+video_capture.release()
+# Destroying output window
+cv2.destroyAllWindows()
\ No newline at end of file