Moved processing to `utils/utils.py`

Crashes when another face is introduced

wyzely_detect/__main__.py

@@ -7,17 +7,12 @@ import cv2
 import torch
 from ultralytics import YOLO
 
-from .utils import notify, utils
+from .utils import utils
 from .utils.cli_args import argparser
 
 DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
 args = None
 
-objects_and_peoples = {
-    "objects": {},
-    "peoples": {},
-}
-
 
 def main():
     global objects_and_peoples
@@ -70,121 +65,32 @@ def main():
     while True:
         # Grab a single frame of video
         ret, frame = video_capture.read()
-        # Resize frame of video to a smaller size for faster recognition processing
-        run_frame = cv2.resize(frame, (0, 0), fx=args.run_scale, fy=args.run_scale)
-        # view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
 
-        results = model(run_frame, verbose=False)
+        frame_to_show = utils.process_footage(
+            frame = frame,
+            run_scale = args.run_scale,
+            view_scale = args.view_scale,
 
-        path_to_faces = Path(args.faces_directory)
+            faces_directory=Path(args.faces_directory),
-        path_to_faces_exists = path_to_faces.is_dir()
+            face_confidence_threshold=args.face_confidence_threshold,
+            no_remove_representations=args.no_remove_representations,
 
-        for i, r in enumerate(results):
+            detection_window=args.detection_window,
-            # list of dicts with each dict containing a label, x1, y1, x2, y2
+            detection_duration=args.detection_duration,
-            plot_boxes = []
+            notification_window=args.notification_window,
 
-            # The following is stuff for people
+            ntfy_url=args.ntfy_url,
-            # This is still in the for loop as each result, no matter if anything is detected, will be present.
-            # Thus, there will always be one result (r)
 
-            # Only run if path_to_faces exists
+            model=model,
-            # May be better to check every iteration, but this also works
+            detect_object=args.detect_object,
-            if path_to_faces_exists:
+            object_confidence_threshold=args.object_confidence_threshold,
-                if face_details := utils.recognize_face(
+        )
-                    path_to_directory=path_to_faces,
+        # Display the resulting frame
-                    run_frame=run_frame,
+        # TODO: When multi-camera support is added, this needs to be changed to allow all feeds
-                    min_confidence=args.face_confidence_threshold,
+        if not args.no_display:
-                    no_remove_representations=args.no_remove_representations,
+            # When a face isn't recognized: "cv2.error: OpenCV(4.8.1) D:\a\opencv-python\opencv-python\opencv\modules\highgui\src\window.cpp:971: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'cv::imshow'"
-                ):
+            # Seems to be because frame_to_show is null
-                    plot_boxes.append(face_details)
+            cv2.imshow("Video", frame_to_show)
-                    objects_and_peoples = notify.thing_detected(
-                        thing_name=face_details["label"],
-                        objects_and_peoples=objects_and_peoples,
-                        detection_type="peoples",
-                        detection_window=args.detection_window,
-                        detection_duration=args.detection_duration,
-                        notification_window=args.notification_window,
-                        ntfy_url=args.ntfy_url,
-                    )
-
-            # The following is stuff for objects
-            # Setup dictionary of object names
-            if (
-                objects_and_peoples["objects"] == {}
-                or objects_and_peoples["objects"] is None
-            ):
-                for name in r.names.values():
-                    objects_and_peoples["objects"][name] = {
-                        "last_detection_time": None,
-                        "detection_duration": None,
-                        # "first_detection_time": None,
-                        "last_notification_time": None,
-                    }
-                # Also, make sure that the objects to detect are in the list of objects_and_peoples
-                # If it isn't, print a warning
-                for obj in args.detect_object:
-                    if obj not in objects_and_peoples:
-                        print(
-                            f"Warning: {obj} is not in the list of objects the model can detect!"
-                        )
-
-            for box in r.boxes:
-                # Get the name of the object
-                class_id = r.names[box.cls[0].item()]
-                # Get the coordinates of the object
-                cords = box.xyxy[0].tolist()
-                cords = [round(x) for x in cords]
-                # Get the confidence
-                conf = round(box.conf[0].item(), 2)
-                # Print it out, adding a spacer between each object
-                # print("Object type:", class_id)
-                # print("Coordinates:", cords)
-                # print("Probability:", conf)
-                # print("---")
-
-                # Now do stuff (if conf > 0.5)
-                if conf < args.object_confidence_threshold or (
-                    class_id not in args.detect_object and args.detect_object != []
-                ):
-                    # If the confidence is too low
-                    # or if the object is not in the list of objects to detect and the list of objects to detect is not empty
-                    # then skip this iteration
-                    continue
-
-                # Add the object to the list of objects to plot
-                plot_boxes.append(
-                    {
-                        "label": class_id,
-                        "x1": cords[0],
-                        "y1": cords[1],
-                        "x2": cords[2],
-                        "y2": cords[3],
-                    }
-                )
-
-                objects_and_peoples = notify.thing_detected(
-                    thing_name=class_id,
-                    objects_and_peoples=objects_and_peoples,
-                    detection_type="objects",
-                    detection_window=args.detection_window,
-                    detection_duration=args.detection_duration,
-                    notification_window=args.notification_window,
-                    ntfy_url=args.ntfy_url,
-                )
-
-            # To debug plotting, use r.plot() to cross reference the bounding boxes drawn by the plot_label() and r.plot()
-            frame_to_show = utils.plot_label(
-                boxes=plot_boxes,
-                full_frame=frame,
-                # full_frame=r.plot(),
-                run_scale=args.run_scale,
-                view_scale=args.view_scale,
-            )
-
-            # Display the resulting frame
-            # cv2.imshow("", r)
-            if not args.no_display:
-                cv2.imshow(f"Video{i}", frame_to_show)
 
         # Hit 'q' on the keyboard to quit!
         if cv2.waitKey(1) & 0xFF == ord("q"):

wyzely_detect/utils/cli_args.py

@@ -92,6 +92,7 @@ def set_argparse():
         help="The URL to send notifications to",
     )
 
+    # Various timers
     timers = argparser.add_argument_group("Timers")
     timers.add_argument(
         "--detection-duration",

wyzely_detect/utils/utils.py

@@ -2,9 +2,165 @@ import cv2
 import numpy as np
 from pathlib import Path
 from deepface import DeepFace
+from . import notify
 
 first_face_try = True
 
+# TODO: When multi-camera support is added, this will need to be changed so that each camera has its own dict
+objects_and_peoples = {
+    "objects": {},
+    "peoples": {},
+}
+
+
+def process_footage(
+    # Frame
+    frame: np.ndarray = None,
+
+    # scale
+    run_scale: float = None,
+    view_scale: float = None,
+
+    # Face stuff
+    faces_directory: str = None,
+    face_confidence_threshold: float = None,
+    no_remove_representations: bool = False,
+
+    # Timer stuff
+    detection_window: int = None,
+    detection_duration: int = None,
+    notification_window: int = None,
+
+    ntfy_url: str = None,
+
+    # Object stuff
+    # YOLO object
+    model = None,
+    detect_object: list = None,
+    object_confidence_threshold = None
+) -> np.ndarray:
+    """
+    Takes in a frame and processes it
+    """ 
+
+    global objects_and_peoples
+
+    
+    # Resize frame of video to a smaller size for faster recognition processing
+    run_frame = cv2.resize(frame, (0, 0), fx=run_scale, fy=run_scale)
+    # view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
+
+    results = model(run_frame, verbose=False)
+
+    path_to_faces = Path(faces_directory)
+    path_to_faces_exists = path_to_faces.is_dir()
+
+    for i, r in enumerate(results):
+        # list of dicts with each dict containing a label, x1, y1, x2, y2
+        plot_boxes = []
+
+        # The following is stuff for people
+        # This is still in the for loop as each result, no matter if anything is detected, will be present.
+        # Thus, there will always be one result (r)
+
+        # Only run if path_to_faces exists
+        # May be better to check every iteration, but this also works
+        if path_to_faces_exists:
+            if face_details := recognize_face(
+                path_to_directory=path_to_faces,
+                run_frame=run_frame,
+                # Perhaps make these names match?
+                min_confidence=face_confidence_threshold,
+                no_remove_representations=no_remove_representations,
+            ):
+                plot_boxes.append(face_details)
+                objects_and_peoples = notify.thing_detected(
+                    thing_name=face_details["label"],
+                    objects_and_peoples=objects_and_peoples,
+                    detection_type="peoples",
+                    detection_window=detection_window,
+                    detection_duration=detection_duration,
+                    notification_window=notification_window,
+                    ntfy_url=ntfy_url,
+                )
+
+        # The following is stuff for objects
+        # Setup dictionary of object names
+        if (
+            objects_and_peoples["objects"] == {}
+            or objects_and_peoples["objects"] is None
+        ):
+            for name in r.names.values():
+                objects_and_peoples["objects"][name] = {
+                    "last_detection_time": None,
+                    "detection_duration": None,
+                    # "first_detection_time": None,
+                    "last_notification_time": None,
+                }
+            # Also, make sure that the objects to detect are in the list of objects_and_peoples
+            # If it isn't, print a warning
+            for obj in detect_object:
+                if obj not in objects_and_peoples:
+                    print(
+                        f"Warning: {obj} is not in the list of objects the model can detect!"
+                    )
+
+        for box in r.boxes:
+            # Get the name of the object
+            class_id = r.names[box.cls[0].item()]
+            # Get the coordinates of the object
+            cords = box.xyxy[0].tolist()
+            cords = [round(x) for x in cords]
+            # Get the confidence
+            conf = round(box.conf[0].item(), 2)
+            # Print it out, adding a spacer between each object
+            # print("Object type:", class_id)
+            # print("Coordinates:", cords)
+            # print("Probability:", conf)
+            # print("---")
+
+            # Now do stuff (if conf > 0.5)
+            if conf < object_confidence_threshold or (
+                class_id not in detect_object and detect_object != []
+            ):
+                # If the confidence is too low
+                # or if the object is not in the list of objects to detect and the list of objects to detect is not empty
+                # then skip this iteration
+                continue
+
+            # Add the object to the list of objects to plot
+            plot_boxes.append(
+                {
+                    "label": class_id,
+                    "x1": cords[0],
+                    "y1": cords[1],
+                    "x2": cords[2],
+                    "y2": cords[3],
+                }
+            )
+
+            objects_and_peoples = notify.thing_detected(
+                thing_name=class_id,
+                objects_and_peoples=objects_and_peoples,
+                detection_type="objects",
+                detection_window=detection_window,
+                detection_duration=detection_duration,
+                notification_window=notification_window,
+                ntfy_url=ntfy_url,
+            )
+            
+
+            # To debug plotting, use r.plot() to cross reference the bounding boxes drawn by the plot_label() and r.plot()
+            frame_to_show = plot_label(
+                boxes=plot_boxes,
+                full_frame=frame,
+                # full_frame=r.plot(),
+                run_scale=run_scale,
+                view_scale=view_scale,
+            )
+            # Unsure if this should also return the objects_and_peoples dict
+            return frame_to_show
+
 
 def plot_label(
     # list of dicts with each dict containing a label, x1, y1, x2, y2
@@ -18,7 +174,7 @@ def plot_label(
     # So the coordinates will be scaled appropriately when coming from run_frame
     view_scale: float = None,
     font: int = cv2.FONT_HERSHEY_SIMPLEX,
-):
+) -> np.ndarray:
     # x1 and y1 are the top left corner of the box
     # x2 and y2 are the bottom right corner of the box
     # Example scaling: full_frame: 1 run_frame: 0.5 view_frame: 0.25
@@ -176,6 +332,7 @@ def recognize_face(
             f"Cosine similarity: {cosine_similarity}, filname: {path_to_image.name}, to_return: {to_return}"
         )
         return to_return
+    return None
 
     """
     Example dataframe, for reference