Lots of edits

Signed-off-by: James P. Ketrenos <james.p.ketrenos@intel.com>

Dockerfile

@@ -26,9 +26,8 @@ RUN DEBIAN_FRONTEND=NONINTERACTIVE apt-get install -y \
 # Install the latest npm and npx
 RUN npm install --global npm@latest
 
-# Install deepface and retina-face
+# Install deepface
 RUN pip install deepface
-RUN pip install retina-face
 RUN pip install piexif
 
 # numpy 1.24 deprecated float; deepface is still using it, so we need to

ketrface/cluster.py

@@ -21,6 +21,8 @@ pictures_path = merge_config_path(config['path'], config['picturesPath'])
 faces_path = merge_config_path(config['path'], config['facesPath'])
 db_path = merge_config_path(config['path'], config["db"]["photos"]["host"])
 html_base = config['basePath']
+if html_base == "/":
+  html_base = "."
 
 # TODO
 # Switch to using DBSCAN

ketrface/detect.py

@@ -7,8 +7,6 @@ import argparse
 
 from PIL import Image, ImageOps
 from deepface import DeepFace
-from deepface.detectors import FaceDetector
-from retinaface import RetinaFace
 import numpy as np
 import cv2
 
@@ -24,8 +22,8 @@ faces_path = merge_config_path(config['path'], config['facesPath'])
 db_path = merge_config_path(config['path'], config["db"]["photos"]["host"])
 html_base = config['basePath']
 
-model_name = 'VGG-Face'    # 'ArcFace'
+model_name = 'VGG-Face'
-detector_backend = 'mtcnn' # 'retinaface'
+detector_backend = 'mtcnn'
 model = DeepFace.build_model(model_name)
 
 # Derived from
@@ -70,44 +68,38 @@ def variance_of_laplacian(image):
 def extract_faces(
   img, threshold=0.95, allow_upscaling = True, focus_threshold = 100):
 
-  if detector_backend == 'retinaface':
+  img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # mtcnn expects RGB
-    faces = RetinaFace.detect_faces(
-      img_path = img, 
-      threshold = threshold, 
-      model = model, 
-      allow_upscaling = allow_upscaling)
-  elif detector_backend == 'mtcnn':
-    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # mtcnn expects RGB
 
-    redirect_on()
+  redirect_on()
-    res = face_detector.detect_faces(img_rgb)
+  res = face_detector.detect_faces(img_rgb)
-    redirect_off()
+  redirect_off()
 
-    faces = {}
+  if type(res) != list:
-    if type(res) == list:
+    return None
-      for i, face in enumerate(res):
-        if threshold > face['confidence']:
-          continue
-        x = face['box'][0]
-        y = face['box'][1]
-        w = face['box'][2]
-        h = face['box'][3]
-        # If face is less than 2.5% of the image width and height,
-        # skip it (too small) -- filters out likely blurry faces in
-        # large group photos where the actual face may exceed
-        # min_face_size passed to MTCNN
-        if 0.025 > w / img.shape[0] and 0.025 > h / img.shape[1]:
-          print(f'Dropping due to small face size: {w / img.shape[0]} x {h / img.shape[1]}')
-          continue
-        faces[f'face_{i+1}'] = { # standardize properties
-          'facial_area': [ x, y, x + w, y + h ],
-          'landmarks': {
-            'left_eye': list(face['keypoints']['left_eye']),
-            'right_eye': list(face['keypoints']['right_eye']),
-          },
-          'score': face['confidence'],
 
-        }
+  faces = {}
+  for i, face in enumerate(res):
+    if threshold > face['confidence']:
+      continue
+    x = face['box'][0]
+    y = face['box'][1]
+    w = face['box'][2]
+    h = face['box'][3]
+    # If face is less than 2.5% of the image width and height,
+    # skip it (too small) -- filters out likely blurry faces in
+    # large group photos where the actual face may exceed
+    # min_face_size passed to MTCNN
+    if 0.025 > w / img.shape[0] and 0.025 > h / img.shape[1]:
+      print(f'Dropping due to small face size: {w / img.shape[0]} x {h / img.shape[1]}')
+      continue
+    faces[f'face_{i+1}'] = { # standardize properties
+      'facial_area': [ x, y, x + w, y + h ],
+      'landmarks': {
+        'left_eye': list(face['keypoints']['left_eye']),
+        'right_eye': list(face['keypoints']['right_eye']),
+      },
+      'score': face['confidence'],
+    }
 
   # Re-implementation of 'extract_faces' with the addition of keeping a 
   # copy of the face image for caching on disk
@@ -190,8 +182,13 @@ def extract_faces(
 
 
 parser = argparse.ArgumentParser(description = 'Detect faces in images.')
-parser.add_argument('photos', metavar='PHOTO', type=int, nargs='*',
+parser.add_argument(
-                    help='PHOTO ID to scan (default: all unscanned photos)')
+  'photos', 
+  metavar = 'PHOTO', 
+  type=int, 
+  nargs='*',
+  help = 'PHOTO ID to scan (default: all unscanned photos)'
+)
 args = parser.parse_args()
 print(args)
 
@@ -209,7 +206,7 @@ with conn:
   for i, row in enumerate(rows):
     photoId, photoFaces, albumPath, photoFilename = row
     img_path = f'{base}{albumPath}{photoFilename}'
-    print(f'Processing {i+1}/{count}: {img_path}')
+    print(f'Processing {i+1}/{count}: photoId = {photoId}: {img_path}')
     try:
       img = Image.open(img_path)
       img = ImageOps.exif_transpose(img) # auto-rotate if needed
@@ -247,6 +244,8 @@ with conn:
         'descriptorId': faceDescriptorId,
       })
 
+      print(f'Face added to database with faceId = {faceId}')
+
       path = f'{faces_path}/{"{:02d}".format(faceId % 100)}'
       try:
         os.makedirs(path)

ketrface/headers.py

@@ -7,10 +7,18 @@ import uu
 from io import BytesIO
 
 from ketrface.util import *
+from ketrface.config import *
+
+config = read_config()
+
+html_path = merge_config_path(config['path'], 'frontend')
+pictures_path = merge_config_path(config['path'], config['picturesPath'])
+faces_path = merge_config_path(config['path'], config['facesPath'])
+db_path = merge_config_path(config['path'], config["db"]["photos"]["host"])
+html_base = config['basePath']
 
-face_base = "../"
 faceId = int(sys.argv[1])
-path = f'{face_base}faces/{"{:02d}".format(faceId % 10)}'
+path = f'{faces_path}/{"{:02d}".format(faceId % 100)}'
 
 img = Image.open(f'{path}/{faceId}.jpg')
 exif_dict = piexif.load(img.info["exif"])