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100 lines
4.0 KiB
100 lines
4.0 KiB
2 years ago
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import configparser
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import cv2
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import os
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import time
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from discord_webhook import DiscordWebhook, DiscordEmbed
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import logging
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from datetime import datetime
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from pycoral.adapters.common import input_size
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from pycoral.adapters.detect import get_objects
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from pycoral.utils.dataset import read_label_file
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from pycoral.utils.edgetpu import make_interpreter
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from pycoral.utils.edgetpu import run_inference
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def main():
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last_time = datetime.now()
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time.sleep(5)
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webhook = DiscordWebhook(url=config["DEFAULT"]["url"])
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labels=config["DEFAULT"]["default_labels"]
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model=config["DEFAULT"]["default_model"]
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threshold=float(config["DEFAULT"]["default_threshold"])
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top_k= int(config["DEFAULT"]["top_K"])
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grace_time= int(config["DEFAULT"]["grace_time"])
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stream=config["DEFAULT"]["default_stream"]
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dump_path=config["DEFAULT"]["dump_path"]
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logging.warning('Loading {} with {} labels.'.format(model, labels))
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interpreter = make_interpreter(model)
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interpreter.allocate_tensors()
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labels = read_label_file(labels)
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inference_size = input_size(interpreter)
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cap = cv2.VideoCapture(stream)
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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cv2_im = frame
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cv2_im_rgb = cv2.cvtColor(cv2_im, cv2.COLOR_BGR2RGB)
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cv2_im_rgb = cv2.resize(cv2_im_rgb, inference_size)
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run_inference(interpreter, cv2_im_rgb.tobytes())
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objs = get_objects(interpreter,threshold)[:top_k]
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cv2_im,alarm,message = detect_and_alarm(cv2_im, inference_size, objs, labels,threshold)
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#print(alarm,message)
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if alarm and is_time(last_time,grace_time):
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last_time=datetime.now()
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logging.warning("people alarm",last_time)
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#im_resize = cv2.resize(cv2_im, (640, 480))
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is_success, im_buf_arr = cv2.imencode(".jpg", cv2_im)
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byte_im = im_buf_arr.tobytes()
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webhook.add_file(file=byte_im,filename='capture.png')
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embed = DiscordEmbed(title='Detected', description=message, color='ff2345')
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embed.set_image(url='attachment://capture.png')
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webhook.add_embed(embed)
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response = webhook.execute(remove_embeds=True)
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logging.debug(response)
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if config["DEFAULT"]["DUMP_VIDEO"]=="True":
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os.system(f"ffmpeg -i {stream} -acodec copy -vcodec copy -t {grace_time} {dump_path}record_{last_time.strftime('%d-%m-%Y_%H-%M-%S')}.mp4 ")
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if config["DEFAULT"]["CV_DEBUG"]=="True":
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cv2.imshow('frame', cv2_im)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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def detect_and_alarm (cv2_im, inference_size, objs, labels,threshold):
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height, width, channels = cv2_im.shape
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scale_x, scale_y = width / inference_size[0], height / inference_size[1]
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for obj in objs:
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if obj.id == 0 and obj.score>threshold*1.09:
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bbox = obj.bbox.scale(scale_x, scale_y)
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x0, y0 = int(bbox.xmin), int(bbox.ymin)
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x1, y1 = int(bbox.xmax), int(bbox.ymax)
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percent = int(100 * obj.score)
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label = '{}% {}'.format(percent, labels.get(obj.id, obj.id))
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cv2_im = cv2.rectangle(cv2_im, (x0, y0), (x1, y1), (0, 255, 0), 2)
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cv2_im = cv2.putText(cv2_im, label, (x0, y0+30),
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cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 2)
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cv2_im = cv2.putText(cv2_im, "ALARM !!!!", (10,30),
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cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
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return cv2_im,True,f'ALARM ! PERSON detected {round(obj.score,1)*100}'
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return cv2_im,False,"OKAY"
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def is_time(last_time,threshold):
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delta=int((datetime.now()-last_time).total_seconds())
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logging.debug(delta,last_time,threshold,delta >= threshold)
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return delta >= threshold
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if __name__ == '__main__':
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config = configparser.ConfigParser()
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configFilePath = '/home/pi/picoral_cctv/cctv.ini'
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config.read(configFilePath)
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main()
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