fixed

Dockerfile

@@ -1,4 +1,4 @@
-FROM ghcr.io/isaric/docker-dlib:python-opencv-4.8.0-dlib-19.24
+FROM ghcr.io/isaric/docker-dlib:python-opencv-4.8.0-dlib-19.24.2
 
 WORKDIR /python-docker
 

docker-compose.yml

@@ -4,14 +4,16 @@ services:
     build: .
     ports:
       - "5000:5000"
+      - "5678:5678"
     environment:
       - SLACK_API_TOKEN=xoxb-241070135347-1373084066946-RKPQf2uO6s7Oi0F8InCgwgkx
       - MONGO_CONNECTION_STRING=mongodb://mongo:27017
-      - READ_SLACK_CHANNEL_ID=C0692LSN85R
-      - WRITE_SLACK_CHANNEL_ID=G01ASA2SG22
+      - READ_SLACK_CHANNEL_ID=G01ASA2SG22
+      - WRITE_SLACK_CHANNEL_ID=C0692LSN85R
       - SLACK_VERIFICATION_TOKEN=u99mYutBGu9ckh4JnOk8YU3P
     depends_on:
       - mongo
+    command: 'python3 -m debugpy --listen 0.0.0.0:5678 -m flask --app main run --host=0.0.0.0'
   mongo:
     image: mongo
     ports:

face_recog/detector.py

@@ -1,86 +1,91 @@
-import requests
 import cv2
 import face_recognition
 from datetime import datetime
 import logging
 import os
-import tempfile
+import urllib.request as request
 
 class Detector:
 
-    haar_cascade = "haarcascade_frontalface_default.xml"
-
     def __init__(self, channel_id, slack_client, repository):
         self.channel_id = channel_id
         self.slack_client = slack_client
         self.repository = repository
-        self.detector = cv2.CascadeClassifier(self.haar_cascade)
-
 
     def detect(self, image_url):
         image = self._get_image(image_url)
-        detected_embeddings, boxes = self._get_embeddings(image)
+        detected_embeddings, faces = self._get_embeddings(image)
         if len(detected_embeddings) == 0:
             logging.info("No faces detected")
             return
 
         all_embeddings = self.repository.get_all_embeddings()
-        recognized = []
-        unknown = []
-        for existing in all_embeddings:
-            for d_embedding in detected_embeddings:
-                box = boxes[detected_embeddings.index(d_embedding)]
-                if self._is_match(existing, d_embedding):
-                    name = existing["name"]
-                    logging.info(f"Found match - {name}")
-                    recognized.append((name, box))
-                else:
-                    unknown.append(box)
+        recognized, unknown = self._compare_with_existing(all_embeddings, detected_embeddings, faces)
 
         if len(recognized) > 0:
             logging.info("Sending message with recognized faces")
             self._send_recognized_message(image,recognized)
 
         if len(unknown) > 0:
             logging.info("Sending message with unknown faces")
-            self._send_unknown_message(unknown, image.tobytes())
+            self._send_unknown_message(unknown, image)
             self._save_embeddings(unknown)
 
     def _get_image(self, image_url):
             logging.info("Downloading image from %s", image_url)
-            headers = {'Authorization': 'Bearer ' + os.environ['SLACK_API_TOKEN']}
-            img_stream = requests.get(image_url, headers=headers).content
-            return face_recognition.load_image_file(img_stream)
+            req = request.Request(image_url)
+            req.add_header('Authorization', 'Bearer ' + os.environ['SLACK_API_TOKEN'])
+            return face_recognition.load_image_file(request.urlopen(req))
 
     def _get_embeddings(self, image):
-            boxes = face_recognition.face_locations(image)
-            return face_recognition.face_encodings(image, boxes), boxes
+            faces = face_recognition.face_locations(image)
+            return face_recognition.face_encodings(image, faces), faces
+
+    def _compare_with_existing(self, existing, detected, faces):
+        if existing.collection.count_documents({}) == 0:
+            logging.info("No existing embeddings found")
+            return [], tuple(zip(detected, faces))
+
+        recognized = []
+        unknown = []
+
+        for e in existing:
+            for d in detected:
+                box = faces[detected.index(d)]
+                if self._is_match(e, d):
+                    name = e["name"]
+                    logging.info(f"Found match - {name}")
+                    recognized.append((name, box))
+                else:
+                    unknown.append((d,box))
+
+        return recognized, unknown
+
 
     def _is_match(self, existing, detected):
         match = face_recognition.compare_faces([existing['embedding']], detected)
         return True in match
 
     def _save_embeddings(self, embeddings):
-        for embedding in embeddings:
+        for embedding, _ in embeddings:
             self.repository.save_embedding({"name": "unknown", "embedding": embedding.tolist(), "created_date": datetime.now()})
 
-    def _send_unknown_message(self, boxes, image):
-        message = f"{len(boxes)} unknown faces detected"
-        for (top, right, bottom, left) in boxes:
+    def _send_unknown_message(self, unknown, image):
+        message = f"{len(unknown)} unknown faces detected"
+        for _, (top, right, bottom, left) in unknown:
             cv2.rectangle(image, (left, top), (right, bottom),
                 (0, 255, 225), 2)
-        self.slack_client.send_image(self.channel_id, message, image)
+        _, bts = cv2.imencode('.png', image)
+        self.slack_client.send_image(self.channel_id, message, bts.tostring())
 
     def _send_recognized_message(self, image, recognized):
         for (name,(top, right, bottom, left)) in recognized:
             cv2.rectangle(image, (left, top), (right, bottom),(0, 255, 225), 2)
             y = top - 15 if top - 15 > 15 else top + 15
             cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
                 .8, (0, 255, 255), 2)
-        with tempfile.NamedTemporaryFile(suffix=".png") as tmpfile:
-            cv2.imwrite(tmpfile.name, image)
-            f1 = open(tmpfile.name, 'rb')
-            self.slack_client.send_image_no_msg(channel_id=self.channel_id, image=f1.read())
+            _, bts = cv2.imencode('.png', image)
+            self.slack_client.send_image_no_msg(channel_id=self.channel_id, image=bts.tostring())