From 53c3028ca8f9453401d14dc40530c4d52a3e69f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=AD=A6=E6=85=A7=E5=87=AF?= Date: Tue, 26 Mar 2019 11:31:49 +0800 Subject: [PATCH] replace face landmark detector --- face_detection.py | 24 ------ face_points_detection.py | 30 +------ face_swap.py | 86 +++---------------- main.py | 2 +- models/shape_predictor_81_face_landmarks.dat | Bin 0 -> 19743860 bytes results/output6_1.jpg | Bin 53869 -> 54019 bytes results/output6_2_2d.jpg | Bin 46552 -> 46966 bytes results/output6_3.jpg | Bin 39223 -> 39248 bytes results/output6_4.jpg | Bin 128234 -> 127986 bytes results/output6_7.jpg | Bin 197455 -> 196737 bytes results/output6_7_2d.jpg | Bin 202818 -> 200466 bytes results/output7_4.jpg | Bin 126513 -> 126712 bytes 12 files changed, 16 insertions(+), 126 deletions(-) create mode 100644 models/shape_predictor_81_face_landmarks.dat diff --git a/face_detection.py b/face_detection.py index 83b36b5..fe0ae7a 100644 --- a/face_detection.py +++ b/face_detection.py @@ -1,7 +1,4 @@ -import cv2 import dlib -import json -import argparse ## Face detection def face_detection(img,upsample_times=1): @@ -12,24 +9,3 @@ def face_detection(img,upsample_times=1): faces = detector(img, upsample_times) return faces - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Face Detection') - parser.add_argument('--img_path', required=True, help='Path for input image') - parser.add_argument('--out', required=True, help='Path for storing bboxes of faces') - args = parser.parse_args() - - # ## Debug - # args.img_path = 'imgs/multi_faces.jpg' - # args.out = 'results/multi_faces.faces.json' - - # Read images - img = cv2.imread(args.img_path) - faces = face_detection(img) - bboxs = [] - - for face in faces: - bboxs.append((face.left(), face.top(), face.right(), face.bottom())) - - with open(args.out, 'w') as f: - json.dump(bboxs, f) \ No newline at end of file diff --git a/face_points_detection.py b/face_points_detection.py index 1a4bda3..09a1c99 100644 --- a/face_points_detection.py +++ b/face_points_detection.py @@ -1,11 +1,8 @@ #! /usr/bin/env python -import json -import cv2 import dlib -import argparse import numpy as np -PREDICTOR_PATH = 'models/shape_predictor_68_face_landmarks.dat' +PREDICTOR_PATH = 'models/shape_predictor_81_face_landmarks.dat' predictor = dlib.shape_predictor(PREDICTOR_PATH) ## Face and points detection def face_points_detection(img, bbox:dlib.rectangle): @@ -18,28 +15,3 @@ def face_points_detection(img, bbox:dlib.rectangle): # return the array of (x, y)-coordinates return coords - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='FaceSwap Demo') - parser.add_argument('--img_path', required=True, help='Path for input image') - parser.add_argument('--bbox_path', required=True, help='Path for bboxes') - parser.add_argument('--index', type=int, default=0, help='Which bbox to use') - parser.add_argument('--out', required=True, help='Path for storing face points') - args = parser.parse_args() - - # ## Debug - # args.img_path = 'imgs/multi_faces.jpg' - # args.bbox_path = 'results/multi_faces.faces.json' - # args.index = 0 - # args.out = 'results/multi_faces.points.json' - - # Read images - img = cv2.imread(args.img_path) - with open(args.bbox_path) as f: - bbox = dlib.rectangle(*json.load(f)[args.index]) - - # Array of corresponding points - points = face_points_detection(img, bbox) - - with open(args.out, 'w') as f: - json.dump(points, f) \ No newline at end of file diff --git a/face_swap.py b/face_swap.py index a935fd9..0d008f5 100644 --- a/face_swap.py +++ b/face_swap.py @@ -1,11 +1,10 @@ #! /usr/bin/env python -import os import cv2 -import json -import argparse import numpy as np import scipy.spatial as spatial import logging + + ## 3D Transform def bilinear_interpolate(img, coords): """ Interpolates over every image channel @@ -39,9 +38,11 @@ def grid_coordinates(points): xmax = np.max(points[:, 0]) + 1 ymin = np.min(points[:, 1]) ymax = np.max(points[:, 1]) + 1 + return np.asarray([(x, y) for y in range(ymin, ymax) for x in range(xmin, xmax)], np.uint32) + def process_warp(src_img, result_img, tri_affines, dst_points, delaunay): """ Warp each triangle from the src_image only within the @@ -59,9 +60,9 @@ def process_warp(src_img, result_img, tri_affines, dst_points, delaunay): x, y = coords.T result_img[y, x] = bilinear_interpolate(src_img, out_coords) - return None + def triangular_affine_matrices(vertices, src_points, dst_points): """ Calculate the affine transformation matrix for each @@ -78,6 +79,7 @@ def triangular_affine_matrices(vertices, src_points, dst_points): mat = np.dot(src_tri, np.linalg.inv(dst_tri))[:2, :] yield mat + def warp_image_3d(src_img, src_points, dst_points, dst_shape, dtype=np.uint8): rows, cols = dst_shape[:2] result_img = np.zeros((rows, cols, 3), dtype=dtype) @@ -90,6 +92,7 @@ def warp_image_3d(src_img, src_points, dst_points, dst_shape, dtype=np.uint8): return result_img + ## 2D Transform def transformation_from_points(points1, points2): points1 = points1.astype(np.float64) @@ -112,6 +115,7 @@ def transformation_from_points(points1, points2): (c2.T - np.dot(s2 / s1 * R, c1.T))[:, np.newaxis]]), np.array([[0., 0., 1.]])]) + def warp_image_2d(im, M, dshape): output_im = np.zeros(dshape, dtype=im.dtype) cv2.warpAffine(im, @@ -120,8 +124,10 @@ def warp_image_2d(im, M, dshape): dst=output_im, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP) + return output_im + ## Generate Mask def mask_from_points(size, points,erode_flag=1): radius = 10 # kernel size @@ -134,6 +140,7 @@ def mask_from_points(size, points,erode_flag=1): return mask + ## Color Correction def correct_colours(im1, im2, landmarks1): COLOUR_CORRECT_BLUR_FRAC = 0.75 @@ -158,6 +165,7 @@ def correct_colours(im1, im2, landmarks1): return result + ## Copy-and-paste def apply_mask(img, mask): """ Apply mask to supplied image @@ -169,6 +177,7 @@ def apply_mask(img, mask): return masked_img + ## Alpha blending def alpha_feathering(src_img, dest_img, img_mask, blur_radius=15): mask = cv2.blur(img_mask, (blur_radius, blur_radius)) @@ -180,6 +189,7 @@ def alpha_feathering(src_img, dest_img, img_mask, blur_radius=15): return result_img + def check_points(img,points): # Todo: I just consider one situation. if points[8,1]>img.shape[0]: @@ -187,71 +197,3 @@ def check_points(img,points): else: return True return False - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='FaceSwap Demo') - parser.add_argument('--src_img', required=True, help='Path for source image') - parser.add_argument('--dst_img', required=True, help='Path for target image') - parser.add_argument('--mask_img', default=None, help='Path for mask image') - parser.add_argument('--src_points', required=True, help='Path for source face points') - parser.add_argument('--dst_points', required=True, help='Path for target face points') - parser.add_argument('--out', required=True, help='Path for storing output image') - args = parser.parse_args() - - # ## Debug - # args.src_img = 'imgs/I.jpg' - # args.src_points = 'results/I.points.json' - # args.dst_img = 'imgs/multi_faces.jpg' - # args.dst_points = 'results/multi_faces.points.json' - # args.mask_img = None - # args.out = 'results/output.jpg' - - # Read images - src_img = cv2.imread(args.src_img) - dst_img = cv2.imread(args.dst_img) - # Array of corresponding points - with open(args.src_points) as f: - src_points = np.asarray(json.load(f)) - with open(args.dst_points) as f: - dst_points = np.asarray(json.load(f)) - - h, w = dst_img.shape[:2] - ## 2d warp - src_mask = mask_from_points(src_img.shape[:2], src_points) - - src_img = apply_mask(src_img, src_mask) - # Correct Color for 2d warp - warped_dst_img = warp_image_3d(dst_img, dst_points[:48], src_points[:48], src_img.shape[:2]) - src_img = correct_colours(warped_dst_img, src_img, src_points) - # Warp - warped_src_img = warp_image_2d(src_img, transformation_from_points(dst_points, src_points), (h, w, 3)) - ## Mask for blending - if args.mask_img: - mask = cv2.cvtColor(cv2.imread(args.mask_img), cv2.COLOR_BGR2GRAY) - else: - mask = mask_from_points((h, w), dst_points) - mask_src = np.mean(warped_src_img, axis=2) > 0 - mask = np.asarray(mask*mask_src, dtype=np.uint8) - ## Shrink the mask - ## But this steps makes no difference to mask - # kernel = np.ones((1, 1), 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_img, dst_img, mask, center, cv2.NORMAL_CLONE) - - dir_path = os.path.dirname(args.out) - if not os.path.isdir(dir_path): - os.makedirs(dir_path) - - cv2.imwrite(args.out, output) - - # ##For debug - # cv2.imshow("Face Warped", warped_src_img) - # cv2.imshow("Face Swapped(A)", src_img) - # cv2.imshow("Face Swapped(B)", dst_img) - # cv2.imshow("Face Swapped(A->B)", output) - # cv2.waitKey(0) - # - # cv2.destroyAllWindows() \ No newline at end of file diff --git a/main.py b/main.py index 5c12cbf..fce0ec7 100644 --- a/main.py +++ b/main.py @@ -1,7 +1,6 @@ #! /usr/bin/env python import os import cv2 -import dlib import argparse import numpy as np @@ -10,6 +9,7 @@ from face_points_detection import face_points_detection from face_swap import warp_image_2d, warp_image_3d, mask_from_points, apply_mask, correct_colours, transformation_from_points + def select_face(im, r=10): faces = face_detection(im) diff --git a/models/shape_predictor_81_face_landmarks.dat b/models/shape_predictor_81_face_landmarks.dat new file mode 100644 index 0000000000000000000000000000000000000000..99036323b9c97e382836fec68ade1130d2b97324 GIT binary patch literal 19743860 zcmXthbzD}<_w~iGyE{NiF|OU%dhKqpyPJD$4nrtl0Rn;`5+WrbNQi(4ih>A=gaVQZ z2BCI}hkUiD&Ae;AP$<7;J zAUlr=fXrV|32EBe5;8C92jsFVg=ksTsv5W?h5cV}z7l;){P79B3M&tWjF@~6a&$Li 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