update site, white

5 files changed, 99 insertions, 15 deletions

diff --git a/megapixels/app/models/bbox.py b/megapixels/app/models/bbox.py
index 8ecc8971..c840ea1b 100644
--- a/megapixels/app/models/bbox.py
+++ b/megapixels/app/models/bbox.py
@@ -207,11 +207,21 @@ class BBox:
   # -----------------------------------------------------------------
   # Convert to
 
-  def to_square(self, bounds):
+  def to_square(self):
     '''Forces bbox to square dimensions
-    :param bounds: (int, int) w, h of the image
     :returns (BBox) in square ratio
     '''
+    if self._width > self._height:
+      delta = (self._width - self._height) / 2
+      self._y1 -= delta
+      self._y2 += delta
+    elif self._height > self._width:
+      delta = (self._height - self._width) / 2
+      self._x1 -= delta
+      self._x2 += delta
+    return BBox(self._x1, self._y1, self._x2, self._y2)
+
+
 
   def to_dim(self, dim):
     """scale is (w, h) is tuple of dimensions"""
diff --git a/megapixels/app/models/dataset.py b/megapixels/app/models/dataset.py
index a7227a70..c908da1b 100644
--- a/megapixels/app/models/dataset.py
+++ b/megapixels/app/models/dataset.py
@@ -152,6 +152,8 @@ class Dataset:
     image_records = []  # list of image matches w/identity if available
     # find most similar feature vectors indexes
     #match_idxs = self.similar(query_vec, n_results, threshold)
+
+    # TODO: add cosine similarity
     sim_scores = np.linalg.norm(np.array([query_vec]) - np.array(self._face_vectors), axis=1)
     match_idxs = np.argpartition(sim_scores, range(n_results))[:n_results]
 
@@ -180,7 +182,17 @@ class Dataset:
       s3_url = self.data_store_s3.face(ds_record.uuid)
       bbox_norm = BBox.from_xywh_norm_dim(ds_roi.x, ds_roi.y, ds_roi.w, ds_roi.h, dim)
       self.log.debug(f'bbox_norm: {bbox_norm}')
-      score = sim_scores[match_idx]
+      self.log.debug(f'match_idx: {match_idx}, record_idx: {record_idx}, roi_index: {roi_index}, len sim_scores: {len(sim_scores)}')
+      try:
+        score = sim_scores[match_idx]
+      except Exception as e:
+        self.log.error(e)
+      try:
+        score = sim_scores[record_idx]
+      except Exception as e:
+        self.log.error(e)
+
+
 
       if types.Metadata.IDENTITY in self._metadata.keys():
         ds_id = df_identity.loc[df_identity['identity_key'] == ds_record.identity_key].iloc[0]
diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py
index 3700efd1..6ab8c700 100644
--- a/megapixels/app/site/parser.py
+++ b/megapixels/app/site/parser.py
@@ -163,6 +163,35 @@ def intro_section(metadata, s3_path):
   """
 
   section = "<section class='intro_section' style='background-image: url({})'>".format(s3_path + metadata['image'])
+  # section += "<div class='inner'>"
+
+  # parts = []
+  # if 'desc' in metadata:
+  #   desc = metadata['desc']
+  #   # colorize the first instance of the database name in the header
+  #   if 'color' in metadata and metadata['title'] in desc:
+  #     desc = desc.replace(metadata['title'], "<span style='color: {}'>{}</span>".format(metadata['color'], metadata['title']), 1)
+  #   section += "<div class='hero_desc'><span class='bgpad'>{}</span></div>".format(desc, desc)
+
+  # if 'subdesc' in metadata:
+  #   subdesc = markdown(metadata['subdesc']).replace('<p>', '').replace('</p>', '')
+  #   section += "<div class='hero_subdesc'><span class='bgpad'>{}</span></div>".format(subdesc, subdesc)
+
+  # section += "</div>"
+  section += "</section>"
+
+  if 'caption' in metadata:
+    section += "<section><div class='image'><div class='intro-caption caption'>{}</div></div></section>".format(metadata['caption'])
+
+  return section
+
+
+def intro_section_v1(metadata, s3_path):
+  """
+  Build the intro section for datasets
+  """
+
+  section = "<section class='intro_section' style='background-image: url({})'>".format(s3_path + metadata['image'])
   section += "<div class='inner'>"
 
   parts = []
@@ -185,7 +214,6 @@ def intro_section(metadata, s3_path):
 
   return section
 
-
 def fix_images(lines, s3_path):
   """
   do our own transformation of the markdown around images to handle wide images etc
diff --git a/megapixels/app/utils/draw_utils.py b/megapixels/app/utils/draw_utils.py
index 7044a62f..1836768b 100644
--- a/megapixels/app/utils/draw_utils.py
+++ b/megapixels/app/utils/draw_utils.py
@@ -3,8 +3,10 @@ from math import sqrt
 
 import numpy as np
 import cv2 as cv
+import PIL
+from PIL import ImageDraw
 
-from app.utils import logger_utils
+from app.utils import logger_utils, im_utils
 
 log = logger_utils.Logger.getLogger()
 
@@ -118,6 +120,22 @@ def draw_landmarks2D(im, points_norm, radius=3, color=(0,255,0)):
     cv.circle(im_dst, pt, radius, color, -1, cv.LINE_AA)
   return im_dst
 
+def draw_landmarks2D_pil(im, points_norm, radius=3, color=(0,255,0)):
+  '''Draws facial landmarks, either 5pt or 68pt
+  '''
+  im_pil = im_utils.ensure_pil(im_utils.bgr2rgb(im))
+  draw = ImageDraw.Draw(im_pil)
+  dim = im.shape[:2][::-1]
+  for x,y in points_norm:
+    x1, y1 = (int(x*dim[0]), int(y*dim[1]))
+    xyxy = (x1, y1, x1+radius, y1+radius)
+    draw.ellipse(xyxy, fill='white')
+  del draw
+  im_dst = im_utils.ensure_np(im_pil)
+  im_dst = im_utils.rgb2bgr(im_dst)
+  return im_dst
+
+
 def draw_landmarks3D(im, points, radius=3, color=(0,255,0)):
   '''Draws 3D facial landmarks
   '''
@@ -126,12 +144,26 @@ def draw_landmarks3D(im, points, radius=3, color=(0,255,0)):
     cv.circle(im_dst, (x,y), radius, color, -1, cv.LINE_AA)
   return im_dst
 
-def draw_bbox(im, bbox_norm, color=(0,255,0), stroke_weight=2):
+def draw_bbox(im, bboxes_norm, color=(0,255,0), stroke_weight=2):
   '''Draws BBox onto cv image
+  :param color: RGB value
   '''
-  im_dst = im.copy()
-  bbox_dim = bbox_norm.to_dim(im.shape[:2][::-1])
-  cv.rectangle(im_dst, bbox_dim.pt_tl, bbox_dim.pt_br, color, stroke_weight, cv.LINE_AA)
+  #im_dst = im.copy()
+  if not type(bboxes_norm) == list:
+    bboxes_norm = [bboxes_norm]
+
+  im_pil = im_utils.ensure_pil(im_utils.bgr2rgb(im))
+  im_pil_draw = ImageDraw.ImageDraw(im_pil)
+
+  for bbox_norm in bboxes_norm:
+    bbox_dim = bbox_norm.to_dim(im.shape[:2][::-1])
+    #cv.rectangle(im_dst, bbox_dim.pt_tl, bbox_dim.pt_br, color, stroke_weight, cv.LINE_AA)
+    xyxy = (bbox_dim.pt_tl, bbox_dim.pt_br)  
+    im_pil_draw.rectangle(xyxy, outline=color, width=stroke_weight)
+    # draw.rectangle([x1, y1, x2, y2], outline=, width=3)
+  im_dst = im_utils.ensure_np(im_pil)
+  im_dst = im_utils.rgb2bgr(im_dst)
+  del im_pil_draw
   return im_dst
 
 def draw_pose(im, pt_nose, image_pts):
diff --git a/megapixels/app/utils/im_utils.py b/megapixels/app/utils/im_utils.py
index d36c1c32..670d5168 100644
--- a/megapixels/app/utils/im_utils.py
+++ b/megapixels/app/utils/im_utils.py
@@ -11,11 +11,6 @@ from skimage import feature
 import imutils
 import time
 import numpy as np
-import torch
-import torch.nn as nn
-import torchvision.models as models
-import torchvision.transforms as transforms
-from torch.autograd import Variable
 from sklearn.metrics.pairwise import cosine_similarity
 import datetime
 
@@ -293,6 +288,13 @@ def bgr2rgb(im):
   """
   return cv.cvtColor(im,cv.COLOR_BGR2RGB)
 
+def rgb2bgr(im):
+  """Wrapper for cv2.cvtColor transform
+    :param im: Numpy.ndarray (BGR)
+    :returns: Numpy.ndarray (RGB)
+  """
+  return cv.cvtColor(im,cv.COLOR_RGB2BGR)
+
 def compute_laplacian(im):
   # below 100 is usually blurry
   return cv.Laplacian(im, cv.CV_64F).var()
@@ -329,7 +331,7 @@ def normalizedGraylevelVariance(img):
     s = stdev[0]**2 / mean[0]
     return s[0]
 
-def compute_if_blank(im,width=100,sigma=0,thresh_canny=.1,thresh_mean=4,mask=None):
+def is_blank(im,width=100,sigma=0,thresh_canny=.1,thresh_mean=4,mask=None):
   # im is graysacale np
   #im = imutils.resize(im,width=width)
   #mask = imutils.resize(mask,width=width)