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-httpx = "^0.25.0" -opencv-python = "^4.8.1.78" -ultralytics = "^8.0.190" -hjson = "^3.1.0" -numpy = "^1.23.2" -# torch = [ -# { version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" }, -# { version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" }, -# ] -# torchaudio = [ -# { version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" }, -# { version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" }, -# ] -# torchvision = [ -# { version = "^0.15+cu118", source = "torch_cu118", markers = "extra=='cuda'" }, -# { version = "^0.15+cpu", source = "torch_cpu", markers = "extra!='cuda'" }, -# ] -torch = "^2.1.0" - - -[tool.poetry.group.dev.dependencies] -black = "^23.9.1" -ruff = "^0.0.291" -ipykernel = "^6.25.2" - - -# [[tool.poetry.source]] -# name = "torch_cpu" -# url = "https://download.pytorch.org/whl/cpu" -# priority = "supplemental" -# -# [[tool.poetry.source]] -# name = "torch_cu118" -# url = 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+# { version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" }, +# ] +# torchvision = [ +# { version = "^0.15+cu118", source = "torch_cu118", markers = "extra=='cuda'" }, +# { version = "^0.15+cpu", source = "torch_cpu", markers = "extra!='cuda'" }, +# ] +torch = "^2.1.0" + + +[tool.poetry.group.dev.dependencies] +black = "^23.9.1" +ruff = "^0.0.291" +ipykernel = "^6.25.2" + + +# [[tool.poetry.source]] +# name = "torch_cpu" +# url = "https://download.pytorch.org/whl/cpu" +# priority = "supplemental" +# +# [[tool.poetry.source]] +# name = "torch_cu118" +# url = "https://download.pytorch.org/whl/cu118" +# priority = "supplemental" +# +# [tool.poetry.extras] +# cuda = [] +# +# [[tool.poetry.source]] +# name = "PyPI" +# priority = "primary" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + diff --git a/set-detect-notify/__main__.py b/set-detect-notify/__main__.py index debcad1..b611370 100644 --- a/set-detect-notify/__main__.py +++ b/set-detect-notify/__main__.py @@ -13,6 +13,7 @@ from ultralytics import YOLO import argparse from .utils import notify +from .utils import utils DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S" args = None @@ -48,13 +49,13 @@ def main(): type=float, help="The scale to run the detection at, default is 0.25", ) - # argparser.add_argument( - # '--view-scale', - # # Set it to the env VIEW_SCALE if it isn't blank, otherwise set it to 0.75 - # default=os.environ['VIEW_SCALE'] if 'VIEW_SCALE' in os.environ and os.environ['VIEW_SCALE'] != '' else 0.75, # noqa: E501 - # type=float, - # help="The scale to view the detection at, default is 0.75", - # ) + argparser.add_argument( + '--view-scale', + # Set it to the env VIEW_SCALE if it isn't blank, otherwise set it to 0.75 + default=os.environ['VIEW_SCALE'] if 'VIEW_SCALE' in os.environ and os.environ['VIEW_SCALE'] != '' else 0.75, # noqa: E501 + type=float, + help="The scale to view the detection at, default is 0.75", + ) argparser.add_argument( "--confidence-threshold", @@ -160,10 +161,12 @@ def main(): # Only process every other frame of video to save time # 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) + # view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale) results = model(run_frame, verbose=False) - for r in results: + for i, r in enumerate(results): + # list of dicts with each dict containing a label, x1, y1, x2, y2 + plot_boxes = [] # Setup dictionary of object names if not object_names: for name in r.names.values(): @@ -174,6 +177,7 @@ def main(): "last_notification_time": None, } 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 @@ -192,6 +196,18 @@ def main(): # If the confidence is less than 0.5, then SKIP!!!! 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], + } + ) + # End goal: Send a notification when an object has been detected for 2 seconds in the past 15 seconds. # However, don't send a notification if the last notification was less than 15 seconds ago @@ -265,13 +281,17 @@ def main(): # Reset the detection duration print("Just sent a notification - resetting detection duration") object_names[class_id]["detection_duration"] = 0 - im_array = r.plot() - # Scale back up the coordinates of the locations of detected objects. - # im_array = np.multiply(im_array, 1/args.run_scale) - # print(type(im_array)) - # print(im_array) - # exit() - cv2.imshow("View", im_array) + 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) + cv2.imshow(f"Video{i}", frame_to_show) # Hit 'q' on the keyboard to quit! if cv2.waitKey(1) & 0xFF == ord("q"): diff --git a/set-detect-notify/utils/notify.py b/set-detect-notify/utils/notify.py index a4ba64e..efe6b09 100644 --- a/set-detect-notify/utils/notify.py +++ b/set-detect-notify/utils/notify.py @@ -1,32 +1,32 @@ -import datetime -import httpx - - -def construct_ntfy_headers( - title: str = "Object/Person Detected", - tag="rotating_light", # https://docs.ntfy.sh/publish/#tags-emojis - priority="default", # https://docs.ntfy.sh/publish/#message-priority -) -> dict: - return {"Title": title, "Priority": priority, "Tags": tag} - - -def send_notification(data: str, headers: dict, url: str): - if url is None or data is None: - raise ValueError("url and data cannot be None") - httpx.post(url, data=data.encode("utf-8"), headers=headers) - - -def check_last_seen(last_seen: datetime.datetime, seconds: int = 15): - """ - Check if a time is older than a given number of seconds - If it is, return True - If last_seen is empty/null, return True - """ - if ( - datetime.datetime.now() - last_seen > datetime.timedelta(seconds=seconds) - or last_seen == "" - or last_seen is None - ): - return True - else: - return False +import datetime +import httpx + + +def construct_ntfy_headers( + title: str = "Object/Person Detected", + tag="rotating_light", # https://docs.ntfy.sh/publish/#tags-emojis + priority="default", # https://docs.ntfy.sh/publish/#message-priority +) -> dict: + return {"Title": title, "Priority": priority, "Tags": tag} + + +def send_notification(data: str, headers: dict, url: str): + if url is None or data is None: + raise ValueError("url and data cannot be None") + httpx.post(url, data=data.encode("utf-8"), headers=headers) + + +def check_last_seen(last_seen: datetime.datetime, seconds: int = 15): + """ + Check if a time is older than a given number of seconds + If it is, return True + If last_seen is empty/null, return True + """ + if ( + datetime.datetime.now() - last_seen > datetime.timedelta(seconds=seconds) + or last_seen == "" + or last_seen is None + ): + return True + else: + return False diff --git a/set-detect-notify/utils/utils.py b/set-detect-notify/utils/utils.py new file mode 100644 index 0000000..5e58f31 --- /dev/null +++ b/set-detect-notify/utils/utils.py @@ -0,0 +1,46 @@ +import cv2 +import numpy as np +def plot_label( + # list of dicts with each dict containing a label, x1, y1, x2, y2 + boxes: list = None, + # opencv image + full_frame: np.ndarray = None, + # run_scale is the scale of the image that was used to run the model + # So the coordinates will be scaled up to the view frame size + run_scale: float = None, + # view_scale is the scale of the image, in relation to the full frame + # So the coordinates will be scaled appropriately when coming from run_frame + view_scale: float = None, + font: int = cv2.FONT_HERSHEY_SIMPLEX, +): + view_frame = cv2.resize(full_frame, (0, 0), fx=view_scale, fy=view_scale) + for thing in boxes: + cv2.rectangle( + # Image + view_frame, + # Start point + (int(thing["x1"] * (run_scale/view_scale)), int(thing["y1"] * (run_scale/view_scale))), + # End point + (int(thing["x2"] * (run_scale/view_scale)), int(thing["y2"] * (run_scale/view_scale))), + # Color + (0, 255, 0), + # Thickness + 2, + ) + cv2.putText( + # Image + view_frame, + # Text + thing["label"], + # Origin + (int(thing["x1"] * (run_scale/view_scale)), int(thing["y1"] * (run_scale/view_scale))), + # Font + font, + # Font Scale + 1, + # Color + (0, 255, 0), + # Thickness + 1 + ) + return view_frame \ No newline at end of file