KI-GESTÜTZTE KATZENKLAPPE

# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Main script to run the object detection routine."""
import argparse
import sys
import time

import cv2
from tflite_support.task import core
from tflite_support.task import processor
from tflite_support.task import vision
import utils


def run(model: str, camera_id: int, width: int, height: int, num_threads: int,
        enable_edgetpu: bool) -> None:
  """Continuously run inference on images acquired from the camera.

  Args:
    model: Name of the TFLite object detection model.
    camera_id: The camera id to be passed to OpenCV.
    width: The width of the frame captured from the camera.
    height: The height of the frame captured from the camera.
    num_threads: The number of CPU threads to run the model.
    enable_edgetpu: True/False whether the model is a EdgeTPU model.
  """

  # Variables to calculate FPS
  counter, fps = 0, 0
  start_time = time.time()

  # Start capturing video input from the camera
  cap = cv2.VideoCapture(camera_id)
  cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
  cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)

  # Visualization parameters
  row_size = 20  # pixels
  left_margin = 24  # pixels
  text_color = (0, 0, 255)  # red
  font_size = 1
  font_thickness = 1
  fps_avg_frame_count = 10

  # Initialize the object detection model
  base_options = core.BaseOptions(
      file_name=model, use_coral=True, num_threads=num_threads)
  detection_options = processor.DetectionOptions(
      max_results=3, score_threshold=0.3)
  options = vision.ObjectDetectorOptions(
      base_options=base_options, detection_options=detection_options)
  detector = vision.ObjectDetector.create_from_options(options)

  # Continuously capture images from the camera and run inference
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      sys.exit(
          'ERROR: Unable to read from webcam. Please verify your webcam settings.'
      )

    counter += 1
    image = cv2.flip(image, 1)

    # Convert the image from BGR to RGB as required by the TFLite model.
    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # Create a TensorImage object from the RGB image.
    input_tensor = vision.TensorImage.create_from_array(rgb_image)

    # Run object detection estimation using the model.
    detection_result = detector.detect(input_tensor)

    # Draw keypoints and edges on input image
    image = utils.visualize(image, detection_result)

    # Calculate the FPS
    if counter % fps_avg_frame_count == 0:
      end_time = time.time()
      fps = fps_avg_frame_count / (end_time - start_time)
      start_time = time.time()

    # Show the FPS
    fps_text = 'FPS = {:.1f}'.format(fps)
    text_location = (left_margin, row_size)
    cv2.putText(image, fps_text, text_location, cv2.FONT_HERSHEY_PLAIN,
                font_size, text_color, font_thickness)

    # Stop the program if the ESC key is pressed.
    if cv2.waitKey(1) == 27:
      break
    cv2.imshow('object_detector', image)

  cap.release()
  cv2.destroyAllWindows()


def main():
  parser = argparse.ArgumentParser(
      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  parser.add_argument(
      '--model',
      help='Path of the object detection model.',
      required=False,
      default='efficientdet_lite0.tflite')
  parser.add_argument(
      '--cameraId', help='Id of camera.', required=False, type=int, default=0)
  parser.add_argument(
      '--frameWidth',
      help='Width of frame to capture from camera.',
      required=False,
      type=int,
      default=640)
  parser.add_argument(
      '--frameHeight',
      help='Height of frame to capture from camera.',
      required=False,
      type=int,
      default=480)
  parser.add_argument(
      '--numThreads',
      help='Number of CPU threads to run the model.',
      required=False,
      type=int,
      default=4)
  parser.add_argument(
      '--enableEdgeTPU',
      help='Whether to run the model on EdgeTPU.',
      action='store_true',
      required=False,
      default=False)

  args = parser.parse_args()

  run(args.model, int(args.cameraId), args.frameWidth, args.frameHeight,
      int(args.numThreads), bool(args.enableEdgeTPU))


if __name__ == '__main__':
  main()

import cv2
import numpy as np
from tflite_support.task import processor

# Import necessary modules for accessing the GPIO pins and controlling the servo
import RPi.GPIO as GPIO
import time

_MARGIN = 10  # pixels
_ROW_SIZE = 10  # pixels
_FONT_SIZE = 1
_FONT_THICKNESS = 1
_TEXT_COLOR = (0, 0, 255)  # red

# Set the pin numbering mode
GPIO.setmode(GPIO.BCM)

# Set the GPIO pin that the servo is connected to
servo_pin = 23

# Set the servo pin as output
GPIO.setup(servo_pin, GPIO.OUT)

# Create a PWM instance with a frequency of 50 Hz
pwm = GPIO.PWM(servo_pin, 50)

# Start the PWM with a duty cycle of 7.5% (close position)
pwm.start(7.5)

def open_servo():
    # Change duty cycle to 12.5% to open the servo
    pwm.ChangeDutyCycle(12.5)
    # Delay for a short time
    time.sleep(2)

def close_servo():
    # Change duty cycle to 7.5% to close the servo
    pwm.ChangeDutyCycle(7.5)
    # Delay for a short time
    time.sleep(0.2)
    pwm.ChangeDutyCycle(0)

servo_state = False

def visualize(
    image: np.ndarray,
    detection_result: processor.DetectionResult,
) -> np.ndarray:
  """Draws bounding boxes on the input image and return it.

  Args:
    image: The input RGB image.
    detection_result: The list of all "Detection" entities to be visualize.

  Returns:
    Image with bounding boxes.
  """
  global servo_state
  detection_made = False
  for detection in detection_result.detections:
    detection_made = True
    # Draw bounding_box
    bbox = detection.bounding_box
    start_point = bbox.origin_x, bbox.origin_y
    end_point = bbox.origin_x + bbox.width, bbox.origin_y + bbox.height
    cv2.rectangle(image, start_point, end_point, _TEXT_COLOR, 3)

    # Draw label and score
    category = detection.categories[0]
    category_name = category.category_name # zuständig für name anzeigen
    if category_name == "cat+mouse":
        # Close the servo if it is open
        if servo_state:
            close_servo()
            servo_state = False
    elif category_name == "cat":
        probability = round(category.score, 2)
        if probability > 0.5:
            # Open the servo if it is closed
            if not servo_state:
                open_servo()
                servo_state = True
    else:
        # Close the servo if it is open
        if servo_state:
            close_servo()
            servo_state = False
    probability = round(category.score, 2)
    result_text = category_name + ' (' + str(probability) + ')'
    text_location = (_MARGIN + bbox.origin_x,
                     _MARGIN + _ROW_SIZE + bbox.origin_y)
    cv2.putText(image, result_text, text_location, cv2.FONT_HERSHEY_PLAIN,
                _FONT_SIZE, _TEXT_COLOR, _FONT_THICKNESS)
  if not detection_made:
      if servo_state:
            close_servo()
            servo_state = False
  return image