.

6 files changed, 103 insertions, 29 deletions

diff --git a/megapixels/app/models/bbox.py b/megapixels/app/models/bbox.py
index 41b67416..ccdf229e 100644
--- a/megapixels/app/models/bbox.py
+++ b/megapixels/app/models/bbox.py
@@ -1,6 +1,9 @@
+import math
+
 from dlib import rectangle as dlib_rectangle
 import numpy as np
 
+
 class BBoxPoint:
 
   def __init__(self, x, y):
@@ -105,7 +108,12 @@ class BBox:
   # # Utils
 
   # def constrain(self, dim):
-
+  def distance(self, b):
+    a = self
+    dcx = self._cx - b.cx
+    dcy = self._cy - b.cy
+    d = int(math.sqrt(math.pow(dcx, 2) + math.pow(dcy, 2)))
+    return d
 
   # -----------------------------------------------------------------
   # Modify
@@ -117,26 +125,40 @@ class BBox:
     :returns (BBox) in pixel dimensions
     """
     # expand
-    rect_exp = list( (np.array(self._rect) + np.array([-amt, -amt, amt, amt])).astype('int'))
+    r = list( (np.array(self._rect) + np.array([-amt, -amt, amt, amt])).astype('int'))
     # outliers
     oob = list(range(4))
-    oob[0] = min(rect_exp[0], 0)
-    oob[1] = min(rect_exp[1], 0)
-    oob[2] = dim[0] - max(rect_exp[2], 2)
-    oob[3] = dim[1] - max(rect_exp[3], 3)
+    oob[0] = min(r[0], 0)
+    oob[1] = min(r[1], 0)
+    oob[2] = dim[0] - r[2]
+    oob[3] = dim[1] - r[3]
     oob = np.array(oob)
     oob[oob > 0] = 0
-    # amount
+    # absolute amount
     oob = np.absolute(oob)
-    # threshold
-    rect_exp[0] = max(rect_exp[0], 0)
-    rect_exp[1] = max(rect_exp[1], 0)
-    rect_exp[2] = min(rect_exp[2], dim[0])
-    rect_exp[3] = min(rect_exp[3], dim[1])
+    # threshold expanded rectangle
+    r[0] = max(r[0], 0)
+    r[1] = max(r[1], 0)
+    r[2] = min(r[2], dim[0])
+    r[3] = min(r[3], dim[1])
     # redistribute oob amounts
     oob = np.array([-oob[2], -oob[3], oob[0], oob[1]])
-    rect_exp = np.add(np.array(rect_exp), oob)
-    return BBox(*rect_exp)    
+    r = np.add(np.array(r), oob)
+    # find overage
+    oob[0] = min(r[0], 0)
+    oob[1] = min(r[1], 0)
+    oob[2] = dim[0] - r[2]
+    oob[3] = dim[1] - r[3]
+    oob = np.array(oob)
+    oob[oob > 0] = 0
+    oob = np.absolute(oob)
+    if np.array(oob).any():
+      m = np.max(oob)
+      adj = np.array([m, m, -m, -m])
+      # print(adj)
+      r = np.add(np.array(r), adj)
+
+    return BBox(*r)    
 
 
   # -----------------------------------------------------------------
@@ -199,6 +221,13 @@ class BBox:
     return cls(*rect)
 
   @classmethod
+  def from_xyxy(cls, x1, y1, x2, y2):
+    """Converts x1, y1, x2, y2 to BBox
+    same as constructure but zprovided for conveniene
+    """
+    return cls(x1, y1, x2, y2)
+
+  @classmethod
   def from_xywh(cls, x, y, w, h):
     """Converts x1, y1, w, h to BBox
     :param rect: (list) x1, y1, w, h
diff --git a/megapixels/app/processors/face_detector.py b/megapixels/app/processors/face_detector.py
index 02d068dc..747e057b 100644
--- a/megapixels/app/processors/face_detector.py
+++ b/megapixels/app/processors/face_detector.py
@@ -11,13 +11,26 @@ from app.utils import im_utils, logger_utils
 from app.models.bbox import BBox
 from app.settings import app_cfg as cfg
 
+
+class DetectorHaar:
+
+  im_size = (400, 400)
+  cascade_name = types.HaarCascade.FRONTAL
+
+  def __init__(self, cascade=types.HaarCascade.FRONTAL):
+    self.log = logger_utils.Logger.getLogger()
+
+  def detect(self, im, scale_factor=1.05, overlaps=5):
+    return
+    
+
 class DetectorDLIBCNN:
 
   dnn_size = (300, 300)
   pyramids = 0
   conf_thresh = 0.85
 
-  def __init__(self, opt_gpu):
+  def __init__(self, opt_gpu=0):
     self.log = logger_utils.Logger.getLogger()
     cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
     os.environ['CUDA_VISIBLE_DEVICES'] = str(opt_gpu)
diff --git a/megapixels/app/settings/app_cfg.py b/megapixels/app/settings/app_cfg.py
index 739ddce2..3e60a9b4 100644
--- a/megapixels/app/settings/app_cfg.py
+++ b/megapixels/app/settings/app_cfg.py
@@ -14,11 +14,14 @@ from app.utils import click_utils
 # -----------------------------------------------------------------------------
 
 FaceDetectNetVar = click_utils.ParamVar(types.FaceDetectNet)
-
+HaarCascadeVar = click_utils.ParamVar(types.HaarCascade)
 LogLevelVar = click_utils.ParamVar(types.LogLevel)
 
 # # data_store
 DATA_STORE = '/data_store_hdd/'
+DATA_STORE_NAS = '/data_store_nas/'
+DATA_STORE_HDD = '/data_store_hdd/'
+DATA_STORE_SSD = '/data_store_ssd/'
 DIR_DATASETS = join(DATA_STORE,'datasets')
 DIR_APPS = join(DATA_STORE,'apps')
 DIR_APP = join(DIR_APPS,'megapixels')
@@ -38,6 +41,8 @@ DIR_MODELS_DLIB_5PT = join(DIR_MODELS_DLIB, 'shape_predictor_5_face_landmarks.da
 DIR_MODELS_DLIB_68PT = join(DIR_MODELS_DLIB, 'shape_predictor_68_face_landmarks.dat')
 
 
+types.HaarCascade.FRONTAL
+
 # Test images
 DIR_TEST_IMAGES = join(DIR_APP, 'test', 'images')
 
@@ -51,8 +56,10 @@ FP_FONT = join(DIR_ASSETS, 'font')
 # -----------------------------------------------------------------------------
 # click chair settings
 # -----------------------------------------------------------------------------
-DIR_COMMANDS_PROCESSOR_ADMIN = 'admin/commands'
-DIR_COMMANDS_PROCESSOR_DATASETS = 'datasets/commands'
+DIR_COMMANDS_CV = 'commands/cv'
+DIR_COMMANDS_ADMIN = 'commands/admin'
+DIR_COMMANDS_DATASETS = 'commands/datasets'
+DIR_COMMANDS_MISC  = 'commands/misc'
 
 # -----------------------------------------------------------------------------
 # Filesystem settings
diff --git a/megapixels/app/settings/types.py b/megapixels/app/settings/types.py
index 0c3d7942..52470034 100644
--- a/megapixels/app/settings/types.py
+++ b/megapixels/app/settings/types.py
@@ -20,6 +20,10 @@ class CVTarget(Enum):
   """OpenCV 3.4.2+ DNN backend processor type"""
   CPU, OPENCL, OPENCL_FP16, MYRIAD = range(4)
 
+class HaarCascade(Enum):
+  FRONTAL, ALT, ALT2, PROFILE = range(4)
+
+  
 # ---------------------------------------------------------------------
 # Logger, monitoring
 # --------------------------------------------------------------------
diff --git a/megapixels/app/utils/file_utils.py b/megapixels/app/utils/file_utils.py
index 773667b1..99282bd0 100644
--- a/megapixels/app/utils/file_utils.py
+++ b/megapixels/app/utils/file_utils.py
@@ -175,7 +175,7 @@ def write_pickle(data, fp_out, ensure_path=True):
     pickle.dump(data, fp)
 
 
-def write_json(data, fp_out, minify=True, ensure_path=True, sort_keys=True):
+def write_json(data, fp_out, minify=True, ensure_path=True, sort_keys=True, verbose=False):
   """
   """
   if ensure_path:
@@ -185,6 +185,8 @@ def write_json(data, fp_out, minify=True, ensure_path=True, sort_keys=True):
       json.dump(data, fp, separators=(',',':'), sort_keys=sort_keys)
     else:
       json.dump(data, fp, indent=2, sort_keys=sort_keys)
+  if verbose:
+    log.info('Wrote JSON: {}'.format(fp_out))
 
 def write_csv(data, fp_out, header=None):
   """ """
@@ -277,7 +279,7 @@ def sha256(fp_in, block_size=65536):
   """
   sha256 = hashlib.sha256()
   with open(fp_in, 'rb') as fp:
-    for block in iter(lambda: f.read(block_size), b''):
+    for block in iter(lambda: fp.read(block_size), b''):
       sha256.update(block)
   return sha256.hexdigest()
 
diff --git a/megapixels/app/utils/im_utils.py b/megapixels/app/utils/im_utils.py
index a0f23cd2..d5e92aa3 100644
--- a/megapixels/app/utils/im_utils.py
+++ b/megapixels/app/utils/im_utils.py
@@ -22,6 +22,16 @@ import datetime
 
 
 
+def is_grayscale(im, threshold=5):
+  """Returns True if image is grayscale
+  :param im: (numpy.array) image
+  :return (bool) of if image is grayscale"""
+  b = im[:,:,0]
+  g = im[:,:,1]
+  mean = np.mean(np.abs(g - b))
+  return mean < threshold
+
+
 def compute_features(fe,frames,phashes,phash_thresh=1):
   """
   Get vector embedding using FeatureExtractor
@@ -40,7 +50,7 @@ def compute_features(fe,frames,phashes,phash_thresh=1):
   return vals
 
 
-def ensure_pil(im, bgr2rgb=False):
+def np2pil(im, swap=True):
   """Ensure image is Pillow format
     :param im: image in numpy or PIL.Image format
     :returns: image in Pillow RGB format
@@ -49,35 +59,44 @@ def ensure_pil(im, bgr2rgb=False):
       im.verify()
       return im
   except:
-    if bgr2rgb:
+    if swap:
       im = cv.cvtColor(im,cv.COLOR_BGR2RGB)
     return Image.fromarray(im.astype('uint8'), 'RGB')
 
-def ensure_np(im):
+def pil2np(im, swap=True):
   """Ensure image is Numpy.ndarry format
     :param im: image in numpy or PIL.Image format
     :returns: image in Numpy uint8 format
   """
   if type(im) == np.ndarray:
-      return im
-  return np.asarray(im, np.uint8)
+    return im
+  im = np.asarray(im, np.uint8)
+  if swap:
+    im = cv.cvtColor(im, cv.COLOR_RGB2BGR)
+  return im
 
 
-def resize(im,width=0,height=0):
+def resize(im, width=0, height=0):
   """resize image using imutils. Use w/h=[0 || None] to prioritize other edge size
     :param im: a Numpy.ndarray image
     :param wh: a tuple of (width, height)
   """
+  # TODO change to cv.resize and add algorithm choices
   w = width
   h = height
   if w is 0 and h is 0:
     return im
   elif w > 0 and h > 0:
-    return imutils.resize(im,width=w,height=h)
+    ws = im.shape[1] / w
+    hs = im.shape[0] / h
+    if ws > hs:
+      return imutils.resize(im, width=w)
+    else:
+      return imutils.resize(im, height=h)
   elif w > 0 and h is 0:
-    return imutils.resize(im,width=w)
+    return imutils.resize(im, width=w)
   elif w is 0 and h > 0:
-    return imutils.resize(im,height=h)
+    return imutils.resize(im, height=h)
   else:
     return im