add .py file for webcam detection

The corresponding image for webcam detection added.

facial-landmark.py

@@ -0,0 +1,101 @@
+''' 
+Facial Landmark Detection in Python with OpenCV
+
+Detection from web cam
+'''
+
+# Import Packages
+import cv2
+import os
+import urllib.request as urlreq
+import numpy as np
+
+
+# save face detection algorithm's url in haarcascade_url variable
+haarcascade_url = "https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_alt2.xml"
+
+# save face detection algorithm's name as haarcascade
+haarcascade = "haarcascade_frontalface_alt2.xml"
+haarcascade_clf = "data/" + haarcascade
+
+# check if data folder is in working directory
+if (os.path.isdir('data')):
+    # check if haarcascade is in data directory
+    if (haarcascade in os.listdir('data')):
+        print("File exists")
+    else:
+        # download file from url and save locally as haarcascade_frontalface_alt2.xml
+        urlreq.urlretrieve(haarcascade_url, haarcascade_clf)
+        print("File downloaded")
+else:
+    # create data folder in current directory
+    os.mkdir('data')
+    # download haarcascade to data folder
+    urlreq.urlretrieve(haarcascade_url, haarcascade_clf)
+    print("File downloaded")
+
+# create an instance of the Face Detection Cascade Classifier
+detector = cv2.CascadeClassifier(haarcascade_clf)
+
+# save facial landmark detection model's url in LBFmodel_url variable
+LBFmodel_url = "https://github.com/kurnianggoro/GSOC2017/raw/master/data/lbfmodel.yaml"
+
+# save facial landmark detection model's name as LBFmodel
+LBFmodel = "LFBmodel.yaml"
+LBFmodel_file = "data/" + LBFmodel
+
+# check if data folder is in working directory
+if (os.path.isdir('data')):
+    # check if Landmark detection model is in data directory
+    if (LBFmodel in os.listdir('data')):
+        print("File exists")
+    else:
+        # download file from url and save locally as haarcascade_frontalface_alt2.xml
+        urlreq.urlretrieve(LBFmodel_url, LBFmodel_file)
+        print("File downloaded")
+else:
+    # create data folder in current directory
+    os.mkdir('data')
+    # download Landmark detection model to data folder
+    urlreq.urlretrieve(LBFmodel_url, LBFmodel_file)
+    print("File downloaded")
+
+# create an instance of the Facial landmark Detector with the model
+landmark_detector  = cv2.face.createFacemarkLBF()
+landmark_detector.loadModel(LBFmodel)
+
+# get image from webcam
+webcam_cap = cv2.VideoCapture(0)
+
+while(True):
+    # read webcam
+    _, frame = webcam_cap.read()
+
+    # convert frame to grayscale
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces using the haarcascade classifier on the "grayscale image"
+    faces = detector.detectMultiScale(gray)
+    
+
+    for (x,y,w,d) in faces:
+        # Detect landmarks on "gray"
+        _, landmarks = landmark_detector.fit(gray, np.array(faces))
+
+        for landmark in landmarks:
+            for x,y in landmark[0]:
+                # display landmarks on "frame/image,"
+                # with blue colour in BGR and thickness 2
+                cv2.circle(frame, (x, y), 1, (255, 0, 0), 2)
+
+    # save last instance of detected image
+    cv2.imwrite('face-detect.jpg', frame)    
+    
+    # Show image
+    cv2.imshow("frame", frame)
+
+    # terminate the capture window
+    if cv2.waitKey(20) & 0xFF  == ord('q'):
+        webcam_cap.release()
+        cv2.destroyAllWindows()
+        break