Added support for `ntfy.sh`

main.py

@@ -19,6 +19,7 @@ DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
 # VIEW_SCALE = 0.75
 DISPLAY = False
 RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE")
+NTFY_URL = os.getenv("NTFY_URL")
 
 
 def find_face_from_name(name):
@@ -46,14 +47,17 @@ process_this_frame = True
 # Load the config file, if it does not exist or is blank, create it
 config = {
     # If RUN_BY_COMPOSE is true, set url to rtsp://wyze-bridge:8554/wyze_cam_name, otherwise set it to "rtsp://localhost:8554/wyze_cam_name"
-    "URL": "rtsp://localhost:8554/wyze_cam_name" if not RUN_BY_COMPOSE else "rtsp://bridge:8554/wyze_cam_name",
+    "URL": "rtsp://localhost:8554/wyze_cam_name"
+    if not RUN_BY_COMPOSE
+    else "rtsp://bridge:8554/wyze_cam_name",
     "run_scale": "0.25",
     "view_scale": "0.75",
     "faces": {
         "example1": {"image": "config/example1.jpg", "last_seen": ""},
         "example2": {"image": "config/example2.jpg", "last_seen": ""},
     },
-    "display": True
+    "ntfy_url": "https://ntfy.sh/example",
+    "display": True,
 }
 config_path = pathlib.Path("config/config.json")
 if config_path.exists():
@@ -83,32 +87,12 @@ if DISPLAY:
     config["DISPLAY"] = DISPLAY
 else:
     DISPLAY = config["display"]
-
+if NTFY_URL:
+    config["ntfy_url"] = NTFY_URL
+else:
+    NTFY_URL = config["ntfy_url"]
 print(f"Current config: {config}")
 
-# Try this 5 times, 5 seconds apart. If the stream is not available, exit
-# for i in range(5):
-#     # Check if HLS stream is available using the requests library
-#     # If it is not, print an error and exit
-#     url = URL.replace("rtsp", "http").replace(":8554", ":8888")
-#     print(f"Checking if HLS stream is available at {url}...")
-#     try:
-#     #    Replace rtsp with http and the port with 8888
-#         r = requests.get(url)
-#         if r.status_code != 200:
-#             print("HLS stream not available, please check your URL")
-#             exit()
-#     except requests.exceptions.RequestException as e:
-#         print("HLS stream not available, please check your URL")
-#         if i == 4:
-#             exit()
-#         else: 
-#             print(f"Retrying in 5 seconds ({i+1}/5)")
-#             time.sleep(5)
-#             continue
-
-
-
 for face in config["faces"]:
     # Load a sample picture and learn how to recognize it.
     image = face_recognition.load_image_file(config["faces"][face]["image"])
@@ -124,70 +108,73 @@ video_capture = cv2.VideoCapture(URL)
 video_capture.set(cv2.CAP_PROP_BUFFERSIZE, 1)
 
 # Print the resolution of the video
-print(f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}")
+print(
+    f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}"
+)
 
+print("Beginning video capture...")
 while True:
     # Grab a single frame of video
     ret, frame = video_capture.read()
-
     # Only process every other frame of video to save time
-    # if process_this_frame:
-    if True:
     # Resize frame of video to a smaller size for faster face recognition processing
     run_frame = cv2.resize(frame, (0, 0), fx=RUN_SCALE, fy=RUN_SCALE)
     view_frame = cv2.resize(frame, (0, 0), fx=VIEW_SCALE, fy=VIEW_SCALE)
-
     # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
     rgb_run_frame = run_frame[:, :, ::-1]
-
     # Find all the faces and face encodings in the current frame of video
     # model cnn is gpu accelerated, but hog is cpu only
-        face_locations = face_recognition.face_locations(rgb_run_frame, model="hog")
+    face_locations = face_recognition.face_locations(rgb_run_frame, model="hog") # This crashes the program without output on my laptop when it's running without Docker compose
     face_encodings = face_recognition.face_encodings(rgb_run_frame, face_locations)
-
     face_names = []
     for face_encoding in face_encodings:
         # See if the face is a match for the known face(s)
-            matches = face_recognition.compare_faces(
+        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
-                known_face_encodings, face_encoding
-            )
         name = "Unknown"
-
         # Or instead, use the known face with the smallest distance to the new face
         face_distances = face_recognition.face_distance(
             known_face_encodings, face_encoding
         )
         best_match_index = np.argmin(face_distances)
         if matches[best_match_index]:
-                # print("For debugging, I found a face!!!! :D this should not be included in the final product lol :P")
             name = known_face_names[best_match_index]
             last_seen = config["faces"][name]["last_seen"]
-                # If it's never been seen, set the last seen time to six seconds ago so it will be seen
+            # If it's never been seen, set the last seen time to x+5 seconds ago so it will be seen
             # Kind of a hacky way to do it, but it works... hopefully
             if last_seen == "":
                 print(f"{name} has been seen")
                 config["faces"][name]["last_seen"] = (
-                        datetime.datetime.now() - datetime.timedelta(seconds=6)
+                    datetime.datetime.now() - datetime.timedelta(seconds=15)
                 ).strftime(DATETIME_FORMAT)
                 write_config()
             # Check if the face has been seen in the last 5 seconds
             if datetime.datetime.now() - datetime.datetime.strptime(
                 last_seen, DATETIME_FORMAT
-                ) > datetime.timedelta(seconds=5):
+            ) > datetime.timedelta(seconds=10):
                 print(f"{name} has been seen")
             # Update the last seen time
             config["faces"][name]["last_seen"] = datetime.datetime.now().strftime(
                 DATETIME_FORMAT
             )
+            # Send a notification
+            print(f"Sending notification to{NTFY_URL}")
+            requests.post(
+                NTFY_URL,
+                data=f'"{name}" has been seen',
+                headers={
+                    "Title": "Face Detected",
+                    "Priority": "urgent",
+                    "Tags": "neutral_face",
+                },
+            )
+            print("Writing config...")
             write_config()
         face_names.append(name)
-
-    process_this_frame = not process_this_frame
-
     # Display the results
     # Iterate over each face found in the frame to draw a box around it
     # Zip is used to iterate over two lists at the same time
     for (top, right, bottom, left), name in zip(face_locations, face_names):
+        print(f"Face found at {top}, {right}, {bottom}, {left} with name {name}")
         # Scale back up face locations since the frame we detected in was scaled to 1/4 size
         top = int(top * (VIEW_SCALE / RUN_SCALE))
         right = int(right * (VIEW_SCALE / RUN_SCALE))
@@ -215,5 +202,6 @@ while True:
         break
 
 # Release handle to the webcam
+print("Releasing video capture")
 video_capture.release()
 cv2.destroyAllWindows()