importing image

assest/shape_predictor_68_face_landmarks.dat:Zone.Identifier

@@ -0,0 +1,4 @@
+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://github.com/JeffTrain/selfie/blob/master/shape_predictor_68_face_landmarks.dat
+HostUrl=https://raw.githubusercontent.com/JeffTrain/selfie/master/shape_predictor_68_face_landmarks.dat

filters/rainbow.py

@@ -0,0 +1,60 @@
+import cv2
+import numpy as np
+import dlib
+from math import hypot
+path = r"assest\snapchat-filters-png-4068.png"
+filter_image = cv2.imread(path)
+# Loading Face detector
+detector = dlib.get_frontal_face_detector()
+predictor_path = r"assest\shape_predictor_68_face_landmarks.dat"
+predictor = dlib.shape_predictor(predictor_path)
+
+
+def rainbow_filtering(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:
+
+        for face in faces:
+            landmarks = predictor(gray_frame, face)
+
+            # filter coordinates
+            # top_filter = (landmarks.part(27).x, landmarks.part(24).y)
+            center_filter = (landmarks.part(57).x, landmarks.part(57).y)
+            left_filter = (landmarks.part(2).x, landmarks.part(2).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]) * 0.9)
+            filter_height = int(filter_width * 1.3)
+
+            # New filter position
+            top_left = (int(center_filter[0] - filter_width / 2),
+                        int(center_filter[1] - filter_height / 2 + 20))
+            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_image, (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)
+    except:
+        _, frame_f = cap.read()
+        cv2.imshow("Frame", frame_f)
+    # else:
+    #     _, frame_f = cap.read()
+    #     cv2.imshow("Frame", frame_f)

helpers/faces.jpg:Zone.Identifier

@@ -0,0 +1,4 @@
+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://www.google.jo/
+HostUrl=https://thumbs.dreamstime.com/z/set-people-faces-smiling-face-collection-group-79112820.jpg

helpers/faces2.jpg:Zone.Identifier

@@ -0,0 +1,4 @@
+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://www.google.jo/
+HostUrl=https://image.shutterstock.com/shutterstock/photos/498141919/display_1500/stock-photo-set-of-people-faces-498141919.jpg

helpers/faces2.png:Zone.Identifier

@@ -0,0 +1,3 @@
+[ZoneTransfer]
+LastWriterPackageFamilyName=Microsoft.ScreenSketch_8wekyb3d8bbwe
+ZoneId=3

helpers/import_image.py

@@ -0,0 +1,53 @@
+import cv2
+import dlib
+from math import hypot
+
+path = r"snapchat-filters-png-4068.png"
+filter_image = cv2.imread(path)
+# Loading Face detector
+detector = dlib.get_frontal_face_detector()
+predictor_path = r"shape_predictor_68_face_landmarks.dat"
+predictor = dlib.shape_predictor(predictor_path)
+image = cv2.imread('faces2.jpg')
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+faces = detector(image)
+
+for face in faces:
+    landmarks = predictor(gray, face)
+
+    # filter coordinates
+    # top_filter = (landmarks.part(27).x, landmarks.part(24).y)
+    center_filter = (landmarks.part(57).x, landmarks.part(57).y)
+    left_filter = (landmarks.part(2).x, landmarks.part(2).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]) * 0.9)
+    filter_height = int(filter_width * 1.3)
+
+    # New filter position
+    top_left = (int(center_filter[0] - filter_width / 2),
+                int(center_filter[1] - filter_height / 2 + 25))
+    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_image, (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 = image[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)
+
+    image[top_left[1]: top_left[1] + filter_height,
+    top_left[0]: top_left[0] + filter_width] = final_filter
+
+cv2.imshow("Frame", image)
+cv2.waitKey(0)
+
+
+
+
+

helpers/people_faces.jpg:Zone.Identifier

@@ -0,0 +1,4 @@
+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://www.google.jo/
+HostUrl=https://i.pinimg.com/originals/97/e4/2a/97e42a82fc7911961d3ca55f54d1372c.jpg

helpers/pngfind.com-pilgrim-hat-png-41504.png:Zone.Identifier

@@ -0,0 +1,4 @@
+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://www.pngfind.com/download/hmboo_black-pilgrim-hat-picture-clip-art-pilgrim-hat/
+HostUrl=https://www.pngfind.com/pngs/b/4-41504_pilgrim-hat-png.png

main.py

@@ -1,16 +1,17 @@
 import cv2
 import numpy as np
 import dlib
-from filters.filter1 import filtering
-from filters.dogfilter import filteringdog
+# from filters.filter1 import filtering
+# from filters.dogfilter import filteringdog
+from filters.rainbow import rainbow_filtering
 # Loading Camera and Nose image and Creating mask
 cap = cv2.VideoCapture(0)
 _, frame = cap.read()
 # print(frame)
 rows, cols, _ = frame.shape
 filter1 = np.zeros((rows, cols), np.uint8)
 while True:
-    filteringdog(cap,rows, cols)
+    rainbow_filtering(cap,rows, cols)
     key = cv2.waitKey(1)
     if key == 27:
         break