added 3 stickers and one ghost filter

README.md

@@ -51,4 +51,4 @@ Featured task: different filter names must appear to the user
 
 <h2 style= "color:#23968b">Domain Modeling</h2>
 
-[white board](https://miro.com/app/board/o9J_lgjUC2c=/)
+[white board ](https://miro.com/app/board/o9J_lgjUC2c=/)

filters/face_filter_crown.py

@@ -0,0 +1,52 @@
+# import cv2
+# import numpy as np
+# import dlib
+# from math import hypot
+# filter_image = cv2.imread("../assest/flower-crown-png-42606.png")
+#
+# # Loading Face detector
+# detector = dlib.get_frontal_face_detector()
+# predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
+#
+# cap = cv2.VideoCapture(0)
+#
+# while True :
+#     _, frame = cap.read()
+#     gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+#     faces = detector(frame)
+#     for face in faces :
+#         landmarks = predictor(gray_frame, face)
+#         top_glasses = (landmarks.part(24).x , landmarks.part(24).y)
+#         left_glasses = (landmarks.part(0).x , landmarks.part(0).y)
+#         right_glasses  = (landmarks.part(16).x , landmarks.part(16).y)
+#         center_glasses = (landmarks.part(27).x , landmarks.part(27).y)
+#
+#         glasses_width = int (hypot(left_glasses[0] -right_glasses[0],
+#                               left_glasses[1] - right_glasses[1]))
+#
+#         glasses_height = int(glasses_width * 0.6)
+#
+#         # positios
+#         upper_left = (int(center_glasses[0] - glasses_width/2 ),
+#                                int(center_glasses[1] - glasses_height/2))
+#         lower_right = (int(center_glasses[0] + glasses_width / 2),
+#                        int(center_glasses[1] + glasses_height / 2))
+#
+#         # Adding the glasses in the correct position
+#         glasses = cv2.resize(filter_image,(glasses_width , glasses_height))
+#         gray_glasses = cv2.cvtColor(glasses, cv2.COLOR_BGR2GRAY)
+#
+#
+#         _, glasses_mask = cv2.threshold(gray_glasses,120, 225, cv2.THRESH_BINARY_INV)
+#         glasses_area = frame[upper_left[1] : upper_left[1]+glasses_height , upper_left[0]:upper_left[0]+glasses_width]
+#         glasses_ares_no_glasses = cv2.bitwise_and(glasses_area , glasses_area,mask= glasses_mask)
+#         final_glasses = cv2.add(glasses_ares_no_glasses, glasses)
+#         frame[upper_left[1]: upper_left[1] + glasses_height, upper_left[0]:upper_left[0] + glasses_width] = final_glasses
+#
+#     cv2.imshow("Frame",frame)
+#
+#
+#
+#     key = cv2.waitKey(1)
+#     if key ==27 :
+#         break

filters/glasses_black.py

@@ -0,0 +1,62 @@
+import cv2
+import numpy as np
+import dlib
+from math import hypot
+filter_image = cv2.imread("assest/sunglasses2.png")
+
+# Loading Face detector
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("assest\shape_predictor_68_face_landmarks.dat")
+
+def filteringmouse(cap,rows, cols):
+    filter1 = np.zeros((rows, cols), np.uint8)
+    _, frame = cap.read()
+    filter1.fill(0)
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = detector(frame)
+
+    # if faces:
+    try:
+        filter(frame,gray_frame,faces,filter_image,27,27,1,0.35)
+        # filter(frame, gray_frame, faces, filter_image3,27,27,1.5,2,25)
+    except:
+        _, frame_f = cap.read()
+        cv2.imshow("Frame", frame_f)
+    # else:
+    #     _, frame_f = cap.read()
+    #     cv2.imshow("Frame", frame_f)
+def filter(frame,gray_frame,faces,filter_image1,X,Y,width,height,above=0,left=0):
+    for face in faces:
+        landmarks = predictor(gray_frame, face)
+
+        # filter coordinates
+        # top_filter = (landmarks.part(27).x+10, landmarks.part(24).y+10)
+        center_filter = (landmarks.part(X).x-left, landmarks.part(Y).y-above)
+        left_filter = (landmarks.part(0).x, landmarks.part(0).y)
+        right_filter = (landmarks.part(16).x, landmarks.part(16).y)
+
+        filter_width = int(hypot(left_filter[0] - right_filter[0],
+                                 left_filter[1] - right_filter[1]) * width)
+        filter_height = int(filter_width * height)
+
+        # New filter position
+        top_left = (int(center_filter[0] - filter_width / 2),
+                    int(center_filter[1] - filter_height /2 ))
+        bottom_right = (int(center_filter[0] + filter_width / 2),
+                        int(center_filter[1] + filter_height / 2))
+
+        # Adding the new filter
+        # coloring
+        filtery = cv2.resize(filter_image1, (filter_width, filter_height))
+        filtery_gray = cv2.cvtColor(filtery, cv2.COLOR_BGR2GRAY)
+        _, filter1 = cv2.threshold(filtery_gray, 125, 225, cv2.THRESH_BINARY)
+
+        filter_area = frame[top_left[1]: top_left[1] + filter_height,
+                      top_left[0]: top_left[0] + filter_width]
+        filter_area_no_filter = cv2.bitwise_and(filter_area, filter_area, mask=filter1)
+        # final_filter = cv2.add(filter_area_no_filter, filtery)
+
+        frame[top_left[1]: top_left[1] + filter_height,
+        top_left[0]: top_left[0] + filter_width,:] = filter_area_no_filter
+
+        cv2.imshow("Frame", frame)

filters/gost.py

@@ -0,0 +1,30 @@
+import cv2
+import numpy as np
+import dlib
+from math import hypot
+filter_image = cv2.imread("assest/10-2-moustache-free-png-image.png")
+
+# Loading Face detector
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("assest\shape_predictor_68_face_landmarks.dat")
+
+def filteringmouse(cap,rows, cols):
+    filter1 = np.zeros((rows, cols), np.uint8)
+    _, frame = cap.read()
+    filter1.fill(0)
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = detector(frame)
+
+    # if faces:
+    try:
+        filter(frame)
+        # filter(frame, gray_frame, faces, filter_image3,27,27,1.5,2,25)
+    except:
+        _, frame_f = cap.read()
+        cv2.imshow("Frame", frame_f)
+    # else:
+    #     _, frame_f = cap.read()
+    #     cv2.imshow("Frame", frame_f)
+def filter(frame):
+        invert = cv2.bitwise_not(frame)
+        cv2.imshow("Frame", invert)

filters/head_crown.py

@@ -0,0 +1,62 @@
+import cv2
+import numpy as np
+import dlib
+from math import hypot
+filter_image = cv2.imread("assest/flower-crown-png-42606.png")
+
+# Loading Face detector
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("assest\shape_predictor_68_face_landmarks.dat")
+
+def filteringmouse(cap,rows, cols):
+    filter1 = np.zeros((rows, cols), np.uint8)
+    _, frame = cap.read()
+    filter1.fill(0)
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = detector(frame)
+
+    # if faces:
+    try:
+        filter(frame,gray_frame,faces,filter_image,27,27,1.2,0.6,20)
+        # filter(frame, gray_frame, faces, filter_image3,27,27,1.5,2,25)
+    except:
+        _, frame_f = cap.read()
+        cv2.imshow("Frame", frame_f)
+    # else:
+    #     _, frame_f = cap.read()
+    #     cv2.imshow("Frame", frame_f)
+def filter(frame,gray_frame,faces,filter_image1,X,Y,width,height,above=0,left=0):
+    for face in faces:
+        landmarks = predictor(gray_frame, face)
+
+        # filter coordinates
+        # top_filter = (landmarks.part(27).x+10, landmarks.part(24).y+10)
+        center_filter = (landmarks.part(X).x-left, landmarks.part(Y).y-above)
+        left_filter = (landmarks.part(4).x, landmarks.part(4).y)
+        right_filter = (landmarks.part(14).x, landmarks.part(14).y)
+
+        filter_width = int(hypot(left_filter[0] - right_filter[0],
+                                 left_filter[1] - right_filter[1]) * width)
+        filter_height = int(filter_width * height)
+
+        # New filter position
+        top_left = (int(center_filter[0] - filter_width / 2),
+                    int(center_filter[1] - filter_height ))
+        bottom_right = (int(center_filter[0] + filter_width / 2),
+                        int(center_filter[1] + filter_height / 2))
+
+        # Adding the new filter
+        # coloring
+        filtery = cv2.resize(filter_image1, (filter_width, filter_height))
+        filtery_gray = cv2.cvtColor(filtery, cv2.COLOR_BGR2GRAY)
+        _, filter1 = cv2.threshold(filtery_gray, 25, 255, cv2.THRESH_BINARY_INV)
+
+        filter_area = frame[top_left[1]: top_left[1] + filter_height,
+                      top_left[0]: top_left[0] + filter_width]
+        filter_area_no_filter = cv2.bitwise_and(filter_area, filter_area, mask=filter1)
+        final_filter = cv2.add(filter_area_no_filter, filtery)
+
+        frame[top_left[1]: top_left[1] + filter_height,
+        top_left[0]: top_left[0] + filter_width,:] = final_filter
+
+        cv2.imshow("Frame", frame)

filters/mustache_filter.py

@@ -0,0 +1,62 @@
+import cv2
+import numpy as np
+import dlib
+from math import hypot
+filter_image = cv2.imread("assest/mustach.png")
+
+# Loading Face detector
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("assest\shape_predictor_68_face_landmarks.dat")
+
+def filteringmouse(cap,rows, cols):
+    filter1 = np.zeros((rows, cols), np.uint8)
+    _, frame = cap.read()
+    filter1.fill(0)
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = detector(frame)
+
+    # if faces:
+    try:
+        filter(frame,gray_frame,faces,filter_image,57,57,1,1)
+        # filter(frame, gray_frame, faces, filter_image3,27,27,1.5,2,25)
+    except:
+        _, frame_f = cap.read()
+        cv2.imshow("Frame", frame_f)
+    # else:
+    #     _, frame_f = cap.read()
+    #     cv2.imshow("Frame", frame_f)
+def filter(frame,gray_frame,faces,filter_image1,X,Y,width,height,above=0,left=0):
+    for face in faces:
+        landmarks = predictor(gray_frame, face)
+
+        # filter coordinates
+        # top_filter = (landmarks.part(27).x+10, landmarks.part(24).y+10)
+        center_filter = (landmarks.part(X).x-left, landmarks.part(Y).y-above)
+        left_filter = (landmarks.part(4).x, landmarks.part(4).y)
+        right_filter = (landmarks.part(12).x, landmarks.part(12).y)
+
+        filter_width = int(hypot(left_filter[0] - right_filter[0],
+                                 left_filter[1] - right_filter[1]) * width)
+        filter_height = int(filter_width * height)
+
+        # New filter position
+        top_left = (int(center_filter[0] - filter_width / 2),
+                    int(center_filter[1] - filter_height /2 ))
+        bottom_right = (int(center_filter[0] + filter_width / 2),
+                        int(center_filter[1] + filter_height / 2))
+
+        # Adding the new filter
+        # coloring
+        filtery = cv2.resize(filter_image1, (filter_width, filter_height))
+        filtery_gray = cv2.cvtColor(filtery, cv2.COLOR_BGR2GRAY)
+        _, filter1 = cv2.threshold(filtery_gray, 125, 225, cv2.THRESH_BINARY)
+
+        filter_area = frame[top_left[1]: top_left[1] + filter_height,
+                      top_left[0]: top_left[0] + filter_width]
+        filter_area_no_filter = cv2.bitwise_and(filter_area, filter_area, mask=filter1)
+        # final_filter = cv2.add(filter_area_no_filter, filtery)
+
+        frame[top_left[1]: top_left[1] + filter_height,
+        top_left[0]: top_left[0] + filter_width,:] = filter_area_no_filter
+
+        cv2.imshow("Frame", frame)

filters/mustache_filter_2.py

@@ -0,0 +1,62 @@
+import cv2
+import numpy as np
+import dlib
+from math import hypot
+filter_image = cv2.imread("assest/10-2-moustache-free-png-image.png")
+
+# Loading Face detector
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("assest\shape_predictor_68_face_landmarks.dat")
+
+def filteringmouse(cap,rows, cols):
+    filter1 = np.zeros((rows, cols), np.uint8)
+    _, frame = cap.read()
+    filter1.fill(0)
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = detector(frame)
+
+    # if faces:
+    try:
+        filter(frame,gray_frame,faces,filter_image,51,51,1,0.5,1,-3)
+        # filter(frame, gray_frame, faces, filter_image3,27,27,1.5,2,25)
+    except:
+        _, frame_f = cap.read()
+        cv2.imshow("Frame", frame_f)
+    # else:
+    #     _, frame_f = cap.read()
+    #     cv2.imshow("Frame", frame_f)
+def filter(frame,gray_frame,faces,filter_image1,X,Y,width,height,above=0,left=0):
+    for face in faces:
+        landmarks = predictor(gray_frame, face)
+
+        # filter coordinates
+        # top_filter = (landmarks.part(27).x+10, landmarks.part(24).y+10)
+        center_filter = (landmarks.part(X).x-left, landmarks.part(Y).y-above)
+        left_filter = (landmarks.part(4).x, landmarks.part(4).y)
+        right_filter = (landmarks.part(12).x, landmarks.part(12).y)
+
+        filter_width = int(hypot(left_filter[0] - right_filter[0],
+                                 left_filter[1] - right_filter[1]) * width)
+        filter_height = int(filter_width * height)
+
+        # New filter position
+        top_left = (int(center_filter[0] - filter_width / 2),
+                    int(center_filter[1] - filter_height /2 ))
+        bottom_right = (int(center_filter[0] + filter_width / 2),
+                        int(center_filter[1] + filter_height / 2))
+
+        # Adding the new filter
+        # coloring
+        filtery = cv2.resize(filter_image1, (filter_width, filter_height))
+        filtery_gray = cv2.cvtColor(filtery, cv2.COLOR_BGR2GRAY)
+        _, filter1 = cv2.threshold(filtery_gray, 25, 225, cv2.THRESH_BINARY_INV)
+
+        filter_area = frame[top_left[1]: top_left[1] + filter_height,
+                      top_left[0]: top_left[0] + filter_width]
+        filter_area_no_filter = cv2.bitwise_and(filter_area, filter_area, mask=filter1)
+        final_filter = cv2.add(filter_area_no_filter, filtery)
+
+        frame[top_left[1]: top_left[1] + filter_height,
+        top_left[0]: top_left[0] + filter_width,:] = final_filter
+
+        cv2.imshow("Frame", frame)