裁剪成标准2寸照片

README.md

@@ -33,3 +33,12 @@
 文档：[利用PyMatting替换成证件照背景](https://github.com/itainf/aiphoto/wiki/%E5%88%A9%E7%94%A8PyMatting%E7%B2%BE%E7%BB%86%E5%8C%96%E6%8A%A0%E5%9B%BE)
 
 
+2020年7月15日更新
+
+本次更新版本： v20200715
+
+本次更新了，通过dlib框架,裁剪成标准尺寸照片
+
+文档：[裁剪照片](https://github.com/itainf/aiphoto/wiki/%E8%A3%81%E5%89%AA%E7%85%A7%E7%89%87)
+
+

m_dlib/ai_crop.py

@@ -0,0 +1,22 @@
+import m_dlib.face_marks as fmarks
+from PIL import Image
+
+
+def crop_photo(path, target):
+    path = path
+    shape, d = fmarks.predictor_face(path)
+
+    WIDTH_2IN = 413/2
+    HEIGHT_2IN = 626/2
+
+    # 人像中心点
+    X_CENTRE = d.left()+(d.right()-d.left()) / 2
+    Y_CENTER = d.top()+(d.bottom()-d.top()) / 2
+
+    im = Image.open(path)
+    im = im.crop((X_CENTRE-WIDTH_2IN, Y_CENTER-HEIGHT_2IN, X_CENTRE+WIDTH_2IN, Y_CENTER+HEIGHT_2IN))
+    im.save(target)
+
+
+# 通过识别人脸关键点，裁剪图像
+# crop_photo("..//img//meinv_id.png","..//img//2in.jpg")

m_dlib/face_marks.py

@@ -0,0 +1,53 @@
+import dlib
+import cv2
+
+
+def predictor_face(path):
+    """
+    :param path:   原始图片路径
+    :return shape 关键点
+    """
+    # 模型路径 --》修改成自己的路径
+    predictor_path = "E:\\python\\az\\dlib-19.19.0.tar\\shape_predictor_68_face_landmarks.dat"
+
+    # 获取一个探测器
+    detector = dlib.get_frontal_face_detector()
+    # 获取一个预测模型
+    predictor = dlib.shape_predictor(predictor_path)
+    # 加载图片
+    img = dlib.load_rgb_image(path)
+
+    # 探测到的人脸
+    dets = detector(img, 1)
+    print("Number of faces detected: {}".format(len(dets)))
+    # 取一个人脸
+    d = dets[0]
+
+    print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(0, d.left(), d.top(), d.right(), d.bottom()))
+
+    # Get the landmarks/parts for the face in box d.
+    shape = predictor(img, d)
+
+    print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
+    return shape, d
+
+# 显示人脸关键点
+def test_landmarks(path,target_path):
+    path = path
+    img = cv2.imread(path)
+    font=cv2.FONT_HERSHEY_SIMPLEX
+    i = 0
+    shape, d = predictor_face(path)
+    for pt in shape.parts():
+        i = i+1
+        pt_pos = (pt.x, pt.y)
+        cv2.putText(img, str(i), pt_pos, font, 0.3, (0, 255, 0))
+
+    cv2.imshow("image", img)
+    cv2.imwrite(target_path,img)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+# 打印人脸特征点
+# test_landmarks("..//img//meinv_id.png","..//img//meinv_id_landmarks.png")

main.py

@@ -2,6 +2,8 @@
 from u_2_net import my_u2net_test
 from to_background import to_background
 from to_background import to_standard_trimap
+from m_dlib import ai_crop
+
 import numpy as np
 from PIL import Image
 if __name__ == "__main__":
@@ -16,10 +18,15 @@ if __name__ == "__main__":
     trimap = "..\\aiphoto\\img\\meinv_trimap_resize.png"
     to_standard_trimap.to_standard_trimap(alpha_resize_img, trimap)
     #
-    # # 证件照添加纯色背景
+    # 证件照添加蓝底纯色背景
-    id_image = "..\\aiphoto\\img\\meinv_id_grid.png"
+    id_image = "..\\aiphoto\\img\\meinv_id.png"
-    # to_background.to_background(org_img, trimap, id_image, "blue")
+    to_background.to_background(org_img, trimap, id_image, "blue")
-    to_background.to_background_grid(org_img, trimap, id_image)
+    #id_image = "..\\aiphoto\\img\\meinv_id_grid.png"
+    #to_background.to_background_grid(org_img, trimap, id_image)
     # image = Image.open(id_image)
     # data = image.getdata()
     # np.savetxt("data6.txt", data,fmt='%d',delimiter=',')
+
+    # 20200719
+    # 通过识别人脸关键点，裁剪图像
+    ai_crop.crop_photo("..\\aiphoto\\img\\meinv_id.png", "..\\aiphoto\\img\\2in.jpg")