Create utils.py

Author: dfaker

utils.py

@@ -0,0 +1,88 @@
+import cv2
+import numpy
+import os
+import json
+
+def get_image_paths( directory ):
+    return [ x.path for x in os.scandir( directory ) if x.name.endswith(".jpg") or x.name.endswith(".png") ]
+
+from tqdm import tqdm
+
+guideShades = numpy.linspace(20,250,68)
+
+def load_images_masked(image_paths, convert=None,blurSize=35):
+  basePath = os.path.split(image_paths[0])[0]
+  alignments = os.path.join(basePath,'alignments.json')
+  alignments = json.loads( open(alignments).read() )
+
+  all_images    = []
+  landmarks     = []
+
+
+  pbar = tqdm(alignments)
+  for original,cropped,mat,points in pbar:
+    pbar.set_description('loading '+basePath)
+    cropped = os.path.split(cropped)[1]
+    cropped = os.path.join(basePath,cropped)
+    if cropped in image_paths and os.path.exists(cropped):
+      cropped = cv2.imread(cropped).astype(float)
+
+      mat = numpy.array(mat).reshape(2,3)
+      points = numpy.array(points).reshape((-1,2))
+
+      mat = mat*160
+      mat[:,2] += 42
+
+      facepoints = numpy.array( points ).reshape((-1,2))
+
+      mask = numpy.zeros_like(cropped,dtype=numpy.uint8)
+
+      hull = cv2.convexHull( facepoints.astype(int) )
+      hull = cv2.transform( hull.reshape(1,-1,2) , mat).reshape(-1,2).astype(int)
+
+      cv2.fillConvexPoly( mask,hull,(255,255,255) )
+
+      kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(15,15))
+
+      mask = cv2.dilate(mask,kernel,iterations = 1,borderType=cv2.BORDER_REFLECT )
+
+      facepoints = cv2.transform( numpy.array( points ).reshape((1,-1,2)) , mat).reshape(-1,2).astype(int)
+
+      mask = mask[:,:,0]
+
+      all_images.append(  numpy.dstack([cropped,mask]).astype(numpy.uint8) )
+      landmarks.append( facepoints )
+
+  return numpy.array(all_images),numpy.array(landmarks)
+
+
+def load_images_std( image_paths, convert=None ):
+    iter_all_images = ( cv2.imread(fn)  for fn in image_paths )
+    if convert:
+        iter_all_images = ( convert(img) for img in iter_all_images )
+    for i,image in enumerate( iter_all_images ):
+        if i == 0:
+            all_images = numpy.empty( ( len(image_paths), ) + image.shape, dtype=image.dtype )
+        all_images[i] = image
+    return all_images
+
+
+load_images = load_images_masked
+
+def get_transpose_axes( n ):
+    if n % 2 == 0:
+        y_axes = list( range( 1, n-1, 2 ) )
+        x_axes = list( range( 0, n-1, 2 ) )
+    else:
+        y_axes = list( range( 0, n-1, 2 ) )
+        x_axes = list( range( 1, n-1, 2 ) )
+    return y_axes, x_axes, [n-1]
+
+def stack_images( images ):
+    images_shape = numpy.array( images.shape )
+    new_axes = get_transpose_axes( len( images_shape ) )
+    new_shape = [ numpy.prod( images_shape[x] ) for x in new_axes ]
+    return numpy.transpose(
+        images,
+        axes = numpy.concatenate( new_axes )
+        ).reshape( new_shape )