video

face_detection.py

@@ -1,4 +1,6 @@
+import cv2
 import dlib
+import numpy as np
 
 ## Face detection
 def face_detection(img,upsample_times=1):
@@ -9,3 +11,60 @@ def face_detection(img,upsample_times=1):
     faces = detector(img, upsample_times)
 
     return faces
+
+PREDICTOR_PATH = 'models/shape_predictor_68_face_landmarks.dat'
+predictor = dlib.shape_predictor(PREDICTOR_PATH)
+## Face and points detection
+def face_points_detection(img, bbox:dlib.rectangle):
+    # Get the landmarks/parts for the face in box d.
+    shape = predictor(img, bbox)
+
+    # loop over the 68 facial landmarks and convert them
+    # to a 2-tuple of (x, y)-coordinates
+    coords = np.asarray(list([p.x, p.y] for p in shape.parts()), dtype=np.int)
+
+    # return the array of (x, y)-coordinates
+    return coords
+
+def select_face(im, r=10, choose=True):
+    faces = face_detection(im)
+
+    if len(faces) == 0:
+        return None, None, None
+
+    if len(faces) == 1 or not choose:
+        idx = np.argmax([(face.right() - face.left()) * (face.bottom() - face.top()) for face in faces])
+        bbox = faces[idx]
+    else:
+        bbox = []
+
+        def click_on_face(event, x, y, flags, params):
+            if event != cv2.EVENT_LBUTTONDOWN:
+                return
+
+            for face in faces:
+                if face.left() < x < face.right() and face.top() < y < face.bottom():
+                    bbox.append(face)
+                    break
+
+        im_copy = im.copy()
+        for face in faces:
+            # draw the face bounding box
+            cv2.rectangle(im_copy, (face.left(), face.top()), (face.right(), face.bottom()), (0, 0, 255), 1)
+        cv2.imshow('Click the Face:', im_copy)
+        cv2.setMouseCallback('Click the Face:', click_on_face)
+        while len(bbox) == 0:
+            cv2.waitKey(1)
+        cv2.destroyAllWindows()
+        bbox = bbox[0]
+
+    points = np.asarray(face_points_detection(im, bbox))
+
+    im_w, im_h = im.shape[:2]
+    left, top = np.min(points, 0)
+    right, bottom = np.max(points, 0)
+
+    x, y = max(0, left - r), max(0, top - r)
+    w, h = min(right + r, im_h) - x, min(bottom + r, im_w) - y
+
+    return points - np.asarray([[x, y]]), (x, y, w, h), im[y:y + h, x:x + w]

face_swap.py

@@ -197,3 +197,42 @@ def check_points(img,points):
     else:
         return True
     return False
+
+
+def face_swap(src_face, dst_face, src_points, dst_points, dst_shape, dst_img, args, end=48):
+    h, w = dst_face.shape[:2]
+
+    ## 3d warp
+    warped_src_face = warp_image_3d(src_face, src_points[:end], dst_points[:end], (h, w))
+    ## Mask for blending
+    mask = mask_from_points((h, w), dst_points)
+    mask_src = np.mean(warped_src_face, axis=2) > 0
+    mask = np.asarray(mask * mask_src, dtype=np.uint8)
+    ## Correct color
+    if args.correct_color:
+        warped_src_face = apply_mask(warped_src_face, mask)
+        dst_face_masked = apply_mask(dst_face, mask)
+        warped_src_face = correct_colours(dst_face_masked, warped_src_face, dst_points)
+    ## 2d warp
+    if args.warp_2d:
+        unwarped_src_face = warp_image_3d(warped_src_face, dst_points[:end], src_points[:end], src_face.shape[:2])
+        warped_src_face = warp_image_2d(unwarped_src_face, transformation_from_points(dst_points, src_points),
+                                        (h, w, 3))
+
+        mask = mask_from_points((h, w), dst_points)
+        mask_src = np.mean(warped_src_face, axis=2) > 0
+        mask = np.asarray(mask * mask_src, dtype=np.uint8)
+
+    ## Shrink the mask
+    kernel = np.ones((10, 10), np.uint8)
+    mask = cv2.erode(mask, kernel, iterations=1)
+    ##Poisson Blending
+    r = cv2.boundingRect(mask)
+    center = ((r[0] + int(r[2] / 2), r[1] + int(r[3] / 2)))
+    output = cv2.seamlessClone(warped_src_face, dst_face, mask, center, cv2.NORMAL_CLONE)
+
+    x, y, w, h = dst_shape
+    dst_img_cp = dst_img.copy()
+    dst_img_cp[y:y + h, x:x + w] = output
+
+    return dst_img_cp