delete imgpath url

face.py

@@ -15,6 +15,14 @@ from PIL import Image
 import numpy as np
 import random
 
+'''
+from partsim.eye96 import Eye
+from partsim.nose96 import Nose
+from partsim.eyebrow96 import EyeBrow
+from partsim.chin96 import Chin
+from partsim.contour96 import Contour
+from partsim.lip96 import Lip
+'''
 from eye96 import Eye
 from nose96 import Nose
 from eyebrow96 import EyeBrow
@@ -28,9 +36,8 @@ from align import AlignDlib
 import caffe
 import cv2
 
-BASE_DIR='/home/gmface'
-PREDICTOR_PATH = os.path.join(BASE_DIR, 'models','shape_predictor_68_face_landmarks.dat')
-ROLL_MODEL_PATH = os.path.join(BASE_DIR, 'models','cnn_cccdd_30k.tf')
+PREDICTOR_PATH = os.path.join(os.path.dirname(__file__),'models','shape_predictor_68_face_landmarks.dat')
+ROLL_MODEL_PATH = os.path.join(os.path.dirname(__file__),'models','cnn_cccdd_30k.tf')
 
 detector = dlib.get_frontal_face_detector()
 predictor = dlib.shape_predictor(PREDICTOR_PATH)
@@ -46,42 +53,22 @@ class NoFaces(Exception):
 class Face_folder(object): 
     PARTS = {'eye': Eye, 'nose': Nose, 'eyebrow': EyeBrow, 'chin': Chin, 'contour': Contour, 'lip': Lip}
 
-    def __init__(self, image_url,img_path):
-        self.face_raw = None
-
-        if img_path:
-           self.img_path=img_path
-           self.img=cv2.imread(img_path)
-           #print('self.img',self.img)
-        elif image_url:
-           #print('url',image_url)
-           image_url = image_url.strip()
-           result = requests.get(image_url)
-           if result.ok:
-              img = Image.open(io.BytesIO(result.content))
-              img =  img.convert('RGB')
-              self.img =  np.array(img)
-              h,w,c=self.img.shape
-              #print(h,w)
-              if h<70 or w<70:
-                 raise NoFaces
-              #print('img {}'.format(self.img))
-           else:
-              raise NoFaces
-        else:
-            raise NoFaces
+    def __init__(self,img):
+        self.img=np.array(img)
+        h,w,c=self.img.shape
+        if h<70 or w<70:
+           raise NoFaces
  
     @classmethod
-    def get_vector(cls,image_url,img_path,part,net):
+    def get_vector(cls,img,part,net):
         """
         get part vector.
         :return:
         """
-        face = Face_folder(image_url,img_path)
+        face = Face_folder(img)
         face.build_landmark(net)
         face.build_part()
         return getattr(face, part).get_vector()
-
         
     def get_aligned_face_landmarks(self,net):
         img= self.img
@@ -89,48 +76,17 @@ class Face_folder(object):
         bb_aligned=None
         alignedFace=None
         if img is not None:
-           #print('img{}'.format(img))
-           cv2.imwrite('source.jpg',img)
            h,w,c = img.shape
-           #img = cv2.resize(img,(int(w*0.2),int(h*0.2)))
-           #h,w = img.shape[:2]
-           #cv2.imwrite('res.jpg',img)
-           #detector =  dlib.get_frontal_face_detector()
-           #detpath = 'models/mmod_human_face_detector.dat'
-           #detector = dlib.face_detection_model_v1(detpath)
-           #detector = dlib.cnn_face_detection_model_v1(detpath)
-
-           #bb = detector(img) 
            bb = align.getLargestFaceBoundingBox(img)
-           if bb == None:
-              center = (w/2,h/2)
-              angle90 = 270
-              scale = 1.0
-              M = cv2.getRotationMatrix2D(center, angle90, scale)
-              rotated90 = cv2.warpAffine(img, M, (h, w))
-              #img = cv2.flip(img,1)
-              #img = cv2.flip(img,1)
-              cv2.imwrite('flip.jpg',rotated90)
-              faces = detector(img,1)
-              #print('faces',faces)
-              bb = faces[0]
-              #print('face len',len(faces))  
-              bb = align.getLargestFaceBoundingBox(rotated90)
-           #print('bb',bb)
            alignedFace = align.align(
            112,
            img,
            bb,
            landmarkIndices=AlignDlib.OUTER_EYES_AND_NOSE)
-           #print('aligned face{}'.format(alignedFace))
-           #print('ali size',alignedFace.shape)
            alignedFace = cv2.resize(alignedFace,(640,640))
-           #cv2.imwrite('/srv/apps/gmface/align.jpg',alignedFace)
-           #cv2.imwrite('/srv/apps/gmface/org.jpg',img)
            rects=detector(alignedFace,1)
-           #print('rects {}'.format(rects))
         else:
-           print("input img is empty")
+           raise NoFaces
         if len(rects)>1:
            raise TooManyFaces
         if len(rects)==0:
@@ -138,7 +94,6 @@ class Face_folder(object):
         landmarks = net.loadimageAndlandmark98(alignedFace)
         lk_dict={}
      
-        im = alignedFace.copy() 
         for i, p in enumerate(landmarks):
             d={}
             d['x']=p[0]
@@ -148,40 +103,9 @@ class Face_folder(object):
             name='p_'+str(i)
             points[name]=d
             lk_dict.update(points)
-            pos = (int(p[0]), int(p[1]))
-            cv2.putText(im, str(i), pos,
-                fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
-                fontScale=0.4,
-                color=(0, 0, 255))
-            cv2.circle(im, pos, 3, color=(0, 255, 255))
-        #cv2.imwrite('3.jpg',im)
-        #print('lk_dict {}'.format(lk_dict))
         return alignedFace,lk_dict 
    
-    def get_face_landmarks(self):
-        img= self.img
-        if img is not None:
-           rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
-           rects=detector(img,1)    
-        else:
-           print("input img is empty")
-        if len(rects)>1:
-           raise TooManyFaces
-        if len(rects)==0:
-           raise NoFaces
-        lk_dict={}
-        for i, p in enumerate(predictor(img,rects[0]).parts()):
-            d={}
-            d['x']=p.x
-            d['y']=p.y
-            points={}
-            name='p_'+str(i)
-            points[name]=d
-            lk_dict.update(points)
-        face=rects[0]
-        return face,lk_dict
- 
-         
+             
     def build_landmark(self,net):
         """build landmark using dlib"""
         h,w,c=self.img.shape

main2.py

+#import partsim
 import math
 import cv2
 from face import Face_folder,NoFaces,TooManyFaces
@@ -5,10 +6,8 @@ from numpy import array
 import argparse
 import caffe
 import numpy as np
-from align import AlignDlib
 import os
 
-
 parser = argparse.ArgumentParser(description='face part classify')
 parser.add_argument('-p', '--parts', help="input part")
    
@@ -25,8 +24,8 @@ if __name__ == '__main__':
     img1 = cv2.imread(imgpath1)
     img2 = cv2.imread(imgpath2)
 
-    v1 = array(Face_folder.get_vector(None,imgpath1,part,net))
-    v2 = array(Face_folder.get_vector(None,imgpath2,part,net))
+    v1 = array(Face_folder.get_vector(img1,part,net))
+    v2 = array(Face_folder.get_vector(img2,part,net))
 
     print('v1',v1)
     print('v2',v2)