234 lines
9.0 KiB
Python
234 lines
9.0 KiB
Python
# import face_recognition
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import cv2
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import numpy as np
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import dotenv
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from pathlib import Path
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import os
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import time
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# import hjson as json
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import torch
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from ultralytics import YOLO
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import argparse
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from .utils import notify, config_utils
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DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
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args = None
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object_names = {}
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def main():
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global object_names
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global args
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# RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE") # Replace this with code to check for gpu
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if Path(".env").is_file():
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dotenv.load_dotenv()
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print("Loaded .env file")
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else:
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print("No .env file found")
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argparser = argparse.ArgumentParser(
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prog="Detect It",
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description="Detect it all!",
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epilog=":)",
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)
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# required='RUN_SCALE' not in os.environ,
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argparser.add_argument(
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"--run-scale",
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# Set it to the env RUN_SCALE if it isn't blank, otherwise set it to 0.25
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default=os.environ["RUN_SCALE"]
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if "RUN_SCALE" in os.environ and os.environ["RUN_SCALE"] != ""
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else 0.25, # noqa: E501
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type=float,
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help="The scale to run the detection at, default is 0.25",
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)
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# argparser.add_argument(
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# '--view-scale',
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# # Set it to the env VIEW_SCALE if it isn't blank, otherwise set it to 0.75
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# default=os.environ['VIEW_SCALE'] if 'VIEW_SCALE' in os.environ and os.environ['VIEW_SCALE'] != '' else 0.75, # noqa: E501
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# type=float,
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# help="The scale to view the detection at, default is 0.75",
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# )
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stream_source = argparser.add_mutually_exclusive_group()
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stream_source.add_argument(
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"--url",
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default=os.environ["URL"]
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if "URL" in os.environ and os.environ["URL"] != ""
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else None, # noqa: E501
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type=str,
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help="The URL of the stream to use",
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)
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stream_source.add_argument(
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"--capture-device",
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default=os.environ["CAPTURE_DEVICE"]
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if "CAPTURE_DEVICE" in os.environ and os.environ["CAPTURE_DEVICE"] != ""
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else 0, # noqa: E501
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type=int,
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help="The capture device to use. Can also be a url.",
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)
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notifcation_services = argparser.add_argument_group("Notification Services")
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notifcation_services.add_argument(
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"--ntfy-url",
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default=os.environ["NTFY_URL"]
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if "NTFY_URL" in os.environ and os.environ["NTFY_URL"] != ""
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else "https://ntfy.sh/set-detect-notify",
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type=str,
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help="The URL to send notifications to",
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)
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args = argparser.parse_args()
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# Check if a CUDA GPU is available. If it is, set it via torch. Ff not, set it to cpu
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# https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168
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# Currently, I have been unable to set up Poetry to use GPU for Torch
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for i in range(torch.cuda.device_count()):
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print(torch.cuda.get_device_properties(i).name)
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if torch.cuda.is_available():
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torch.cuda.set_device(0)
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print("Set CUDA device")
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else:
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print("No CUDA device available, using CPU")
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model = YOLO("yolov8n.pt")
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# Depending on if the user wants to use a stream or a capture device,
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# Set the video capture to the appropriate source
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if args.url:
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video_capture = cv2.VideoCapture(args.url)
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else:
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video_capture = cv2.VideoCapture(args.capture_device)
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# Eliminate lag by setting the buffer size to 1
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# This makes it so that the video capture will only grab the most recent frame
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# However, this means that the video may be choppy
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video_capture.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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# Print the resolution of the video
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print(
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f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}" # noqa: E501
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)
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print("Beginning video capture...")
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while True:
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# Grab a single frame of video
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ret, frame = video_capture.read()
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# Only process every other frame of video to save time
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# Resize frame of video to a smaller size for faster recognition processing
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run_frame = cv2.resize(frame, (0, 0), fx=args.run_scale, fy=args.run_scale)
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# view_frame = cv2.resize(frame, (0, 0), fx=`args.`view_scale, fy=args.view_scale)
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results = model(run_frame, verbose=False)
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for r in results:
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# Setup dictionary of object names
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if not object_names:
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for name in r.names.values():
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object_names[name] = {
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"last_detection_time": None,
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"detection_duration": None,
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# "first_detection_time": None,
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"last_notification_time": None,
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}
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for box in r.boxes:
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# Get the name of the object
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class_id = r.names[box.cls[0].item()]
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# Get the coordinates of the object
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cords = box.xyxy[0].tolist()
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cords = [round(x) for x in cords]
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# Get the confidence
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conf = round(box.conf[0].item(), 2)
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# Print it out, adding a spacer between each object
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# print("Object type:", class_id)
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# print("Coordinates:", cords)
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# print("Probability:", conf)
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# print("---")
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# Now do stuff (if conf > 0.5)
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if conf < 0.5:
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# If the confidence is less than 0.5, then SKIP!!!!
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continue
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# End goal: Send a notification when an object has been detected for 2 seconds in the past 15 seconds.
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# However, don't send a notification if the last notification was less than 15 seconds ago
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if (
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# If the object has not been detected before
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object_names[class_id]["last_detection_time"] is None
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# If the last detection was more than 15 seconds ago
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or time.time() - object_names[class_id]["last_detection_time"] > 15
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):
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object_names[class_id]["last_detection_time"] = time.time()
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object_names[class_id]["detection_duration"] = 0
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print(f"First detection of {class_id} in this detection window")
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else:
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# Check if the last detection was under 15 seconds ago
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# If it was, leave the detection duration as is
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if (
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time.time() - object_names[class_id]["last_detection_time"]
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<= 15
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):
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pass
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# If it was more than 15 seconds ago, reset the detection duration
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# This effectively resets the cycle
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else:
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object_names[class_id]["detection_duration"] = 0
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object_names[class_id]["detection_duration"] += (
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time.time() - object_names[class_id]["last_detection_time"]
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)
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object_names[class_id]["last_detection_time"] = time.time()
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# Check if detection has been ongoing for 2 seconds or more in the past 15 seconds
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if (
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object_names[class_id]["detection_duration"] >= 2
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and time.time() - object_names[class_id]["last_detection_time"]
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<= 15
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):
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# If the last notification was more than 15 seconds ago, then send a notification
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if (
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object_names[class_id]["last_notification_time"] is None
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or time.time()
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- object_names[class_id]["last_notification_time"]
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> 15
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):
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object_names[class_id]["last_notification_time"] = time.time()
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print(f"Detected {class_id} for 2 seconds")
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headers = notify.construct_ntfy_headers(
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title=f"{class_id} Detected",
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tag="rotating_light",
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priority="default",
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)
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notify.send_notification(
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data=f"{class_id} Detected",
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headers=headers,
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url=args.ntfy_url,
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)
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# Reset the detection duration
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object_names[class_id]["detection_duration"] = 0
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im_array = r.plot()
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# Scale back up the coordinates of the locations of detected objects.
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# im_array = np.multiply(im_array, 1/args.run_scale)
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# print(type(im_array))
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# print(im_array)
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# exit()
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cv2.imshow("View", im_array)
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# Hit 'q' on the keyboard to quit!
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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# Release handle to the webcam
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print("Releasing video capture")
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video_capture.release()
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cv2.destroyAllWindows()
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main()
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