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Signed-off-by: James Ketrenos <james_git@ketrenos.com>

Dockerfile

@@ -40,4 +40,8 @@ COPY . /website
 WORKDIR /website
 RUN npm upgrade && npm install
 
+RUN DEBIAN_FRONTEND=NONINTERACTIVE apt-get install -y \
+  jhead
+RUN pip install piexif
+
 CMD [ "/entrypoint.sh" ]

test/detect.py

@@ -0,0 +1,124 @@
+import sys
+import json
+import piexif
+from PIL import Image
+from deepface import DeepFace
+from retinaface import RetinaFace
+import numpy as np
+
+class NpEncoder(json.JSONEncoder):
+  def default(self, obj):
+    if isinstance(obj, np.integer):
+      return int(obj)
+    if isinstance(obj, np.floating):
+      return float(obj)
+    if isinstance(obj, np.ndarray):
+      return obj.tolist()
+        
+models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"]
+
+def extract_faces(img_path, threshold=0.9, model = None, align = True, allow_upscaling = True):
+  img = RetinaFace.get_image(img_path)
+  faces = RetinaFace.detect_faces(img_path = img, threshold = threshold, model = model, allow_upscaling = allow_upscaling)
+  #faces = DeepFace.detectFace(img_path = img, target_size = (224, 224),    detector_backend = 'retinaface')
+
+  # Re-implementation of 'extract_faces' with the addition of keeping a 
+  # copy of the face image for caching on disk
+  if type(faces) == dict:
+    for key in faces:
+      print(key)
+      identity = faces[key]
+      facial_area = identity["facial_area"]
+
+      if align == True:
+        width = facial_area[2] - facial_area[0]
+        height = facial_area[3] - facial_area[1]
+        x = facial_area[0] + width * 0.5
+        y = facial_area[1] + height * 0.5
+
+        # Make thumbnail a square crop
+        if width > height:
+          height = width
+        else:
+          width = height
+
+        landmarks = identity["landmarks"]
+        left_eye = landmarks["left_eye"]
+        right_eye = landmarks["right_eye"]
+        nose = landmarks["nose"]
+
+        # translate the landmarks to be centered on array
+        left_eye[0] -= x
+        left_eye[1] -= y
+        right_eye[0] -= x
+        right_eye[1] -= y
+        nose[0] -= x
+        nose[1] -= y
+
+        width *= 0.25
+        height *= 0.25
+
+        left = round(x - width * 0.5)
+        right = round(left + width)
+        top = round(y - height * 0.5)
+        bottom = round(top + height)
+
+        facial_img = img[left: right, top: bottom]
+
+        print(left, right, top, bottom, left_eye, right_eye, nose)
+        aligned = RetinaFace.postprocess.alignment_procedure(facial_img, left_eye, right_eye, nose)
+
+        image = Image.fromarray(aligned)
+        image.resize(size = (224,224), resample = Image.ANTIALIAS)
+        resized = np.asarray(image)
+      else:
+        facial_img = img[facial_area[1]: facial_area[3], facial_area[0]: facial_area[2]]
+        resized = facial_img
+        
+      identity['vector'] = DeepFace.represent(img_path = resized, model_name = 'ArcFace', detector_backend = 'opencv', enforce_detection = False)
+
+      identity['image'] = resized[:, :, ::-1]
+
+    return faces
+
+#face verification
+img1_path = sys.argv[1]
+
+faces = extract_faces(img1_path)
+for key in faces:
+  face = faces[key]
+  print(f'Face {key}...')
+  image = Image.fromarray(face['image'])
+  
+  #face['analysis'] = DeepFace.analyze(img_path = img, actions = ['age', 'gender', 'race', 'emotion'], enforce_detection = False)
+  #face['analysis'] = DeepFace.analyze(img, actions = ['emotion'])
+
+  # TODO: Add additional meta-data allowing back referencing to original
+  # photo
+  face['version'] = 1 # version 1 doesn't add much...
+
+  data = {k: face[k] for k in set(list(face.keys())) - set(['image'])}
+  json_str = json.dumps(data, ensure_ascii=False, indent=2, cls=NpEncoder)
+
+  with open(f'{key}.json', 'w', encoding = 'utf-8') as f:
+    f.write(json_str)
+
+  # Encode this data into the JPG as Exif
+  exif_ifd = {piexif.ExifIFD.UserComment: json_str.encode()}
+  exif_dict = {"0th": {}, "Exif": exif_ifd, "1st": {},
+    "thumbnail": None, "GPS": {}}
+  image.save(f'{key}.jpg', exif = piexif.dump(exif_dict))
+
+  #df = DeepFace.find(img, db_path = '/db')
+  #print(df.head())
+
+#img2_path = sys.argv[2]
+#print("image 1: ", img1_path);
+#print("image 2: ", img2_path);
+#result = DeepFace.verify(img1_path = img1_path, img2_path = img2_path, #model_name = models[1])
+#print("result: ", result)
+
+#face recognition
+#df = DeepFace.find(img_path = img1_path, db_path = "./db/deepface", model_name = models[1])
+
+#print("df: ", df)