Merge branch 'master' of github.com:adamhrv/megapixels_dev

3 files changed, 115 insertions, 110 deletions

diff --git a/megapixels/app/processors/face_detector.py b/megapixels/app/processors/face_detector.py
index 593e9feb..3a90c557 100644
--- a/megapixels/app/processors/face_detector.py
+++ b/megapixels/app/processors/face_detector.py
@@ -24,15 +24,15 @@ class DetectorMTCNN:
     from mtcnn.mtcnn import MTCNN
     self.detector = MTCNN()
 
-  def detect(self, im, opt_size=(400,400), opt_conf_thresh=None, opt_pyramids=None, opt_largest=False):
+  def detect(self, im, size=(400,400), conf_thresh=None, pyramids=None, largest=False):
     '''Detects face using MTCNN and returns (list) of BBox
     :param im: (numpy.ndarray) image
     :returns list of BBox
     '''
     bboxes = []
-    #conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
-    #pyramids = self.pyramids if opt_pyramids is None else opt_pyramids
-    dnn_size = self.dnn_size if opt_size is None else opt_size
+    #conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
+    #pyramids = self.pyramids if pyramids is None else pyramids
+    dnn_size = self.dnn_size if size is None else size
 
     im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
     dim = im.shape[:2][::-1]
@@ -43,7 +43,7 @@ class DetectorMTCNN:
       bbox = BBox.from_xywh_dim(*rect, dim)
       bboxes.append(bbox)
 
-    if opt_largest and len(bboxes) > 1:
+    if largest and len(bboxes) > 1:
       # only keep largest
       bboxes.sort(key=operator.attrgetter('area'), reverse=True)
       bboxes = [bboxes[0]]
@@ -70,34 +70,33 @@ class DetectorDLIBCNN:
   pyramids = 0
   conf_thresh = 0.85
 
-  def __init__(self, opt_gpu=0):
+  def __init__(self, gpu=0):
     import dlib
     self.log = logger_utils.Logger.getLogger()
     cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
-    os.environ['CUDA_VISIBLE_DEVICES'] = str(opt_gpu)
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
     self.log.info('load model: {}'.format(cfg.DIR_MODELS_DLIB_CNN))
     self.detector = dlib.cnn_face_detection_model_v1(cfg.DIR_MODELS_DLIB_CNN)
     os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices  # reset
 
-  def detect(self, im, opt_size=None, opt_conf_thresh=None, opt_pyramids=None, opt_largest=False):
+  def detect(self, im, size=None, conf_thresh=None, pyramids=None, largest=False):
     bboxes = []
-    conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
-    pyramids = self.pyramids if opt_pyramids is None else opt_pyramids
-    dnn_size = self.dnn_size if opt_size is None else opt_size
+    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
+    pyramids = self.pyramids if pyramids is None else pyramids
+    dnn_size = self.dnn_size if size is None else size
     # resize image
     im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
     dim = im.shape[:2][::-1]
     im = im_utils.bgr2rgb(im)  # convert to RGB for dlib
     # run detector
-    mmod_rects = self.detector(im, opt_pyramids)
+    mmod_rects = self.detector(im, pyramids)
     # sort results
     for mmod_rect in mmod_rects:
-      self.log.debug('conf: {}, this: {}'.format(conf_thresh, mmod_rect.confidence))
       if mmod_rect.confidence > conf_thresh:
         bbox = BBox.from_dlib_dim(mmod_rect.rect, dim)
         bboxes.append(bbox)
 
-    if opt_largest and len(bboxes) > 1:
+    if largest and len(bboxes) > 1:
       # only keep largest
       bboxes.sort(key=operator.attrgetter('area'), reverse=True)
       bboxes = [bboxes[0]]
@@ -116,25 +115,24 @@ class DetectorDLIBHOG:
     self.log = logger_utils.Logger.getLogger()
     self.detector = dlib.get_frontal_face_detector()
 
-  def detect(self, im, opt_size=None, opt_conf_thresh=None, opt_pyramids=0, opt_largest=False):
-    conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
-    dnn_size = self.size if opt_size is None else opt_size
-    pyramids = self.pyramids if opt_pyramids is None else opt_pyramids
+  def detect(self, im, size=None, conf_thresh=None, pyramids=0, largest=False):
+    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
+    dnn_size = self.size if size is None else size
+    pyramids = self.pyramids if pyramids is None else pyramids
     
-    im = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
+    im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
     dim = im.shape[:2][::-1]
     im = im_utils.bgr2rgb(im)  # ?
     hog_results = self.detector.run(im, pyramids)
     
     bboxes = []
     if len(hog_results[0]) > 0:
-      self.log.debug(hog_results)
       for rect, score, direction in zip(*hog_results):
         if score > conf_thresh:
           bbox = BBox.from_dlib_dim(rect, dim)
           bboxes.append(bbox)
     
-    if opt_largest and len(bboxes) > 1:
+    if largest and len(bboxes) > 1:
       # only keep largest
       bboxes.sort(key=operator.attrgetter('area'), reverse=True)
       bboxes = [bboxes[0]]
@@ -157,10 +155,10 @@ class DetectorCVDNN:
     self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
     self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
 
-  def detect(self, im, opt_size=None, opt_conf_thresh=None, opt_largest=False, opt_pyramids=None):
+  def detect(self, im, size=None, conf_thresh=None, largest=False, pyramids=None):
     """Detects faces and returns (list) of (BBox)"""
-    conf_thresh = self.conf_thresh if opt_conf_thresh is None else opt_conf_thresh
-    dnn_size = self.size if opt_size is None else opt_size
+    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
+    dnn_size = self.size if size is None else size
     im = cv.resize(im, dnn_size)
     blob = cv.dnn.blobFromImage(im, self.dnn_scale, dnn_size, self.dnn_mean)
     self.net.setInput(blob)
@@ -173,7 +171,7 @@ class DetectorCVDNN:
         rect_norm = net_outputs[0, 0, i, 3:7]
         bboxes.append(BBox(*rect_norm))
     
-    if opt_largest and len(bboxes) > 1:
+    if largest and len(bboxes) > 1:
       # only keep largest
       bboxes.sort(key=operator.attrgetter('area'), reverse=True)
       bboxes = [bboxes[0]]
diff --git a/megapixels/app/processors/face_pose.py b/megapixels/app/processors/face_pose.py
index 67ac685d..f2548b32 100644
--- a/megapixels/app/processors/face_pose.py
+++ b/megapixels/app/processors/face_pose.py
@@ -22,89 +22,83 @@ class FacePoseDLIB:
   def __init__(self):
     pass
 
-  def pose(self, landmarks, dim): 
-    '''Calculates pose
-    '''
-    degrees = compute_pose_degrees(landmarks, dim)    
-    return degrees
 
+  def pose(self, landmarks, dim, project_points=False):
+    # computes pose using 6 / 68 points from dlib face landmarks
+    # based on learnopencv.com and 
+    # https://github.com/jerryhouuu/Face-Yaw-Roll-Pitch-from-Pose-Estimation-using-OpenCV/
+    # NB: not as accurate as MTCNN, see @jerryhouuu for ideas
+    
+    pose_points_idx = (30, 8, 36, 45, 48, 54)
+    axis = np.float32([[500,0,0], [0,500,0], [0,0,500]])
+    
+    # 3D model points.
+    model_points = np.array([
+        (0.0, 0.0, 0.0),             # Nose tip
+        (0.0, -330.0, -65.0),        # Chin
+        (-225.0, 170.0, -135.0),     # Left eye left corner
+        (225.0, 170.0, -135.0),      # Right eye right corne
+        (-150.0, -150.0, -125.0),    # Left Mouth corner
+        (150.0, -150.0, -125.0)      # Right mouth corner
+    ])
+    
+    # Assuming no lens distortion
+    dist_coeffs = np.zeros((4,1))
 
-# -----------------------------------------------------------
-# utilities
-# -----------------------------------------------------------
+    # find 6 pose points
+    pose_points = []
+    for j, idx in enumerate(pose_points_idx):
+      pt = landmarks[idx]
+      pose_points.append((pt[0], pt[1]))
+    pose_points = np.array(pose_points, dtype='double')  # convert to double
+    
+    # create camera matrix
+    focal_length = dim[0]
+    center = (dim[0]/2, dim[1]/2)
+    cam_mat = np.array(
+        [[focal_length, 0, center[0]],
+        [0, focal_length, center[1]],
+        [0, 1, 1]], dtype = "double")
+    
+    # solve PnP for rotation and translation
+    (success, rot_vec, tran_vec) = cv.solvePnP(model_points, pose_points, 
+                                               cam_mat, dist_coeffs, 
+                                               flags=cv.SOLVEPNP_ITERATIVE)
 
-def compute_pose_degrees(landmarks, dim):
-  # computes pose using 6 / 68 points from dlib face landmarks
-  # based on learnopencv.com and 
-  # https://github.com/jerryhouuu/Face-Yaw-Roll-Pitch-from-Pose-Estimation-using-OpenCV/
-  # NB: not as accurate as MTCNN, see @jerryhouuu for ideas
-  
-  pose_points_idx = (30, 8, 36, 45, 48, 54)
-  axis = np.float32([[500,0,0], [0,500,0], [0,0,500]])
-  
-  # 3D model points.
-  model_points = np.array([
-      (0.0, 0.0, 0.0),             # Nose tip
-      (0.0, -330.0, -65.0),        # Chin
-      (-225.0, 170.0, -135.0),     # Left eye left corner
-      (225.0, 170.0, -135.0),      # Right eye right corne
-      (-150.0, -150.0, -125.0),    # Left Mouth corner
-      (150.0, -150.0, -125.0)      # Right mouth corner
-  ])
-  
-  # Assuming no lens distortion
-  dist_coeffs = np.zeros((4,1))
+    result = {}
 
-  # find 6 pose points
-  pose_points = []
-  for j, idx in enumerate(pose_points_idx):
-    pt = landmarks[idx]
-    pose_points.append((pt[0], pt[1]))
-  pose_points = np.array(pose_points, dtype='double')  # convert to double
-  
-  # create camera matrix
-  focal_length = dim[0]
-  center = (dim[0]/2, dim[1]/2)
-  cam_mat = np.array(
-      [[focal_length, 0, center[0]],
-      [0, focal_length, center[1]],
-      [0, 1, 1]], dtype = "double")
-  
-  # solve PnP for rotation and translation
-  (success, rot_vec, tran_vec) = cv.solvePnP(model_points, pose_points, 
-                                             cam_mat, dist_coeffs, 
-                                             flags=cv.SOLVEPNP_ITERATIVE)
+    # project points
+    if project_points:
+      pts_im, jac = cv.projectPoints(axis, rot_vec, tran_vec, cam_mat, dist_coeffs)
+      pts_model, jac2 = cv.projectPoints(model_points, rot_vec, tran_vec, cam_mat, dist_coeffs)
+      result['points_model'] = pts_model
+      result['points_image'] = pts_im
+      result['point_nose'] = tuple(landmarks[pose_points_idx[0]])
 
-  # project points
-  #pts_im, jac = cv.projectPoints(axis, rot_vec, tran_vec, cam_mat, dist_coeffs)
-  #pts_model, jac2 = cv.projectPoints(model_points, rot_vec, tran_vec, cam_mat, dist_coeffs)
-  rvec_matrix = cv.Rodrigues(rot_vec)[0]
-  
-  # convert to degrees
-  proj_matrix = np.hstack((rvec_matrix, tran_vec))
-  eulerAngles = cv.decomposeProjectionMatrix(proj_matrix)[6] 
-  pitch, yaw, roll = [math.radians(x) for x in eulerAngles]
-  pitch = math.degrees(math.asin(math.sin(pitch)))
-  roll = -math.degrees(math.asin(math.sin(roll)))
-  yaw = math.degrees(math.asin(math.sin(yaw)))
-  degrees = {'pitch': pitch, 'roll': roll, 'yaw': yaw}
-  
-  # add nose point
-  #pt_nose = tuple(landmarks[pose_points_idx[0]])
-  return degrees
-  #return pts_im, pts_model, degrees, pt_nose
+    rvec_matrix = cv.Rodrigues(rot_vec)[0]
+    
+    # convert to degrees
+    proj_matrix = np.hstack((rvec_matrix, tran_vec))
+    eulerAngles = cv.decomposeProjectionMatrix(proj_matrix)[6] 
+    pitch, yaw, roll = [math.radians(x) for x in eulerAngles]
+    pitch = math.degrees(math.asin(math.sin(pitch)))
+    roll = -math.degrees(math.asin(math.sin(roll)))
+    yaw = math.degrees(math.asin(math.sin(yaw)))
+    degrees = {'pitch': pitch, 'roll': roll, 'yaw': yaw}
+    result['degrees'] = degrees
 
+    return result
 
-def draw_pose(im, pts_im, pts_model, pt_nose):
-  cv.line(im, pt_nose, tuple(pts_im[1].ravel()), (0,255,0), 3) #GREEN
-  cv.line(im, pt_nose, tuple(pts_im[0].ravel()), (255,0,), 3) #BLUE
-  cv.line(im, pt_nose, tuple(pts_im[2].ravel()), (0,0,255), 3) #RED
-  return im
 
+  def draw_pose(self, im, pts_im, pts_model, pt_nose):
+    cv.line(im, pt_nose, tuple(pts_im[1].ravel()), (0,255,0), 3) #GREEN
+    cv.line(im, pt_nose, tuple(pts_im[0].ravel()), (255,0,), 3) #BLUE
+    cv.line(im, pt_nose, tuple(pts_im[2].ravel()), (0,0,255), 3) #RED
 
-def draw_degrees(im, degrees, color=(0,255,0)):
-  for i, item in enumerate(degrees.items()):
-    k, v = item
-    t = '{}: {:.2f}'.format(k, v)
-    origin = (10, 30 + (25 * i))
-    cv.putText(im, t, origin, cv.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness=2, lineType=2)
\ No newline at end of file
+
+  def draw_degrees(self, im, degrees, color=(0,255,0)):
+    for i, item in enumerate(degrees.items()):
+      k, v = item
+      t = '{}: {:.2f}'.format(k, v)
+      origin = (10, 30 + (25 * i))
+      cv.putText(im, t, origin, cv.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness=2, lineType=2)
\ No newline at end of file
diff --git a/megapixels/app/processors/face_recognition.py b/megapixels/app/processors/face_recognition.py
index 9c3a301d..e0b9f752 100644
--- a/megapixels/app/processors/face_recognition.py
+++ b/megapixels/app/processors/face_recognition.py
@@ -17,25 +17,38 @@ class RecognitionDLIB:
   # https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py
   # facerec.compute_face_descriptor(img, shape, 100, 0.25)
 
-  def __init__(self, opt_gpu=0):
+  def __init__(self, gpu=0):
     self.log = logger_utils.Logger.getLogger()
-    if opt_gpu > 0:
+    
+    if gpu > -1:
       cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
-      os.environ['CUDA_VISIBLE_DEVICES'] = str(opt_gpu)
-      self.predictor = dlib.shape_predictor(cfg.DIR_MODELS_DLIB_5PT)
-      self.facerec = dlib.face_recognition_model_v1(cfg.DIR_MODELS_DLIB_FACEREC_RESNET)
+      os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
+    
+    self.predictor = dlib.shape_predictor(cfg.DIR_MODELS_DLIB_5PT)
+    self.facerec = dlib.face_recognition_model_v1(cfg.DIR_MODELS_DLIB_FACEREC_RESNET)
+    
+    if gpu > -1:
       os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices  # reset GPU env
 
+
   def vec(self, im, bbox, width=100, 
-    jitters=cfg.DLIB_FACEREC_JITTERS, padding=cfg.DLIB_FACEREC_PADDING):
+      jitters=cfg.DLIB_FACEREC_JITTERS, padding=cfg.DLIB_FACEREC_PADDING):
     # Converts image and bbox into 128d vector
     # scale the image so the face is always 100x100 pixels
 
+    #self.log.debug('compute scale')
     scale = width / bbox.width
-    im = cv.resize(im, (scale, scale), interploation=cv.INTER_LANCZOS4)
+    #im = cv.resize(im, (scale, scale), cv.INTER_LANCZOS4)
+    #self.log.debug('resize')
+    cv.resize(im, None, fx=scale, fy=scale, interpolation=cv.INTER_LANCZOS4) 
+    #self.log.debug('to dlib')
     bbox_dlib = bbox.to_dlib()
+    #self.log.debug('precitor')
     face_shape = self.predictor(im, bbox_dlib)
-    vec = self.facerec.compute_face_descriptor(im, face_shape, jitters, padding)
+    # vec = self.facerec.compute_face_descriptor(im, face_shape, jitters, padding)
+    #self.log.debug('vec')
+    vec = self.facerec.compute_face_descriptor(im, face_shape, jitters)
+    #vec = self.facerec.compute_face_descriptor(im, face_shape)
     return vec