clean up

project.py

@@ -20,50 +20,7 @@
 
 
 trackerTypes = ['BOOSTING', 'MIL', 'KCF','TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT']
-# def getCroppedImage(faceid, img):
-#     try:
-
-#         rec = faceid #FaceID is class where face recognition model is written.
-
-#         if img.ndim < 2:
-#             print('Unable to crop the image.')
-#             return (None, -402);
-
-#         if img.ndim == 2:
-#             img = rec.to_rgb(img)
-#             print('to_rgb data dimension: ', img.ndim)
-
-#         x,y,_= img.shape
-
-#         if(y>500):
-#             img = cv2.resize( img, (500, x) )
-
-#         x,y,_ = img.shape
-
-#         if(x>500):
-#             img = cv2.resize( img, (y, 500) )   
-
-#         faces = rec.get_face(img)
-#         if (len(faces)>1):
-#             return (None,-400); #Found more than one face. Image field is none.
-
-#         maxx = 0.9; bestFace = None
-
-#         for f in faces:
-#             if(f[4]>maxx):
-#                 maxx = f[4]
-#                 bestFace = f
-
-
-#         if( bestFace is not None ):
-#             croppedImage = rec.get_crops2(bestFace, img)[0]
-
-#             return (croppedImage,0); 
-
-#         return (None,-401);     
-#     except:
-#         cv2.destroyAllWindows();    
-#         raise;
+
 
 def createTrackerByName(trackerType):
   # Create a tracker based on tracker name
@@ -181,9 +138,11 @@ def yolo():
     if i==0:
       print("persons bounding box is: ",bboxes[j])
       boxes.append(bboxes[j].copy())
-      print(boxes[i])
+      #print(boxes[i])
     j=j+1
 
+  print(boxes) #all faces
+
   ############################temp ###########33
   #for index,value in enumerate(boxes):
   global itr
@@ -201,65 +160,16 @@ def yolo():
     print("I am a detector phewww !")
     print(detector.detect_faces(crop_img))
     face_cropped = detector.detect_faces(crop_img)
-    boxes_face = (face_cropped[0]['box'])
-    y1 = boxes_face[1]
-    x1 = boxes_face[0]
-    h1 = boxes_face[3]
-    w1 = boxes_face[2]
-    crop_img_2 = crop_img[y1:y1+h1, x1:x1+w1]
-    name = 'dataset/' + str("face")+ str(itr) + '.jpg'
-    cv.imwrite(name,crop_img_2)
-
-
     if(len(face_cropped)>0):
+
       boxes_face = (face_cropped[0]['box'])
       y1 = boxes_face[1]
       x1 = boxes_face[0]
       h1 = boxes_face[3]
       w1 = boxes_face[2]
       crop_img_2 = crop_img[y1:y1+h1, x1:x1+w1]
       name = 'dataset/' + str("face")+ str(itr) + '.jpg'
-      #cv.imwrite(name,crop_img_2)
-
-    #crop_img_2 = cv2.resize(crop_img_2,(100,100),interpolation=cv2.INTER_AREA)
-
-    # Matching probabiity 
-    # images = []
-    # for img in glob.glob("dataset/face*.jpg"):
-      #img = cv2.resize(img,(100,100),interpolation=cv2.INTER_AREA)
-      #Sensitive Part
-
-
-     #  rec = faceID()
-     #  print("constructor finished")
-     # # crop_img_2 = getCroppedImage(rec,crop_img_2) accepts image in np arary
-
-     #  crop_img_2 = rec.prewhiten2(crop_img_2)
-
-     #  embeds = rec.getEmbed(crop_img_2) 
-
-
-
-     #  for itr, em in enumerate(embeds): 
-     #    name = rec.search_img_thorough2(em,data)
-
-     #  print(name)
-
-
-     #  n = cv2.imread(img)
-     #  images.append(n)
-
-
-
-    #for img in images:
-     # img = cv2.resize(img,(100,100),interpolation=cv2.INTER_AREA)
-      #if(np.linalg.norm(img-crop_img_2)>=0.9):
-       # val = np.linalg.norm(img-crop_img_2)
-        #print("Amir won",val)
-
-
-
-
+      cv.imwrite(name,crop_img_2)
 
 
   ##########################temp done#########33