129 lines
No EOL
3.4 KiB
Python
129 lines
No EOL
3.4 KiB
Python
import numpy as np
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import argparse
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import cv2
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import copy
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import os
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import time
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#from gpiozero import Buzzer
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## Define our config values
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# What is our min dish count to alarm on?
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file = "dirty.jpeg"
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min_dishes = 2
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# Define areas we want to ignore
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# First value is the x range, second is the y range
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ignore_list = ["339-345,257-260"]
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# Set the GPIO our buzzer is connected to
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#buzzer = Buzzer(21)
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# Set how long we want to buzz
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buzz_seconds = 180
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# Set our timestamp
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time_stamp = time.strftime("%Y%m%d%H%M%S")
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# Set our circle detection variables
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circle_sensitivity = 40 # Larger numbers increase false positives
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min_rad = 30 # Tweak this if you're detecting circles that are too small
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max_rad = 75 # Tweak if you're detecting circles that are too big (Ie: round sinks)
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# Cropping the image allows us to only process areas of the image
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# that should have images. Set our crop values
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crop_left = 100
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crop_right = 2000
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crop_top = 0
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crop_bottom = 2000
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def should_ignore(ignore_list, x, y):
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# Loop through our ignore_list and check for this x/y
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ignore = False
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for range in ignore_list:
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x_range = range.split(',')[0]
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y_range = range.split(',')[1]
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x_min = int(x_range.split('-')[0])
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x_max = int(x_range.split('-')[1])
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y_min = int(y_range.split('-')[0])
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y_max = int(y_range.split('-')[1])
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if (x >= x_min and x <= x_max and y >= y_min and y <= y_max):
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ignore = True
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return ignore
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def main():
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print("Acquiring Image")
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# Note: Larger images require more processing power and have more false positives
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# os.system("raspistill -w 1024 -h 768 -o /var/www/html/images/process.jpg")
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image_original = cv2.imread(file)
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print("Cropping image to limit processing to just the sink")
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image = image_original[crop_left:crop_right, crop_top:crop_bottom]
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#image = image_original
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print("Copying image")
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output = copy.copy(image)
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print("Blurring image")
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blurred = cv2.GaussianBlur(image, (9, 9), 2, 2)
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cv2.imwrite('blurred.jpg', blurred)
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print("Converting to grey")
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gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
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cv2.imwrite('gray.jpg', gray)
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print("Detecting circles in blurred and greyed image")
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circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20,
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param1=100,
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param2=circle_sensitivity,
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minRadius=min_rad,
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maxRadius=max_rad)
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print("Checking if we found images")
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if circles is not None:
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dish_count = 0
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print("Dishes Found!")
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# convert the (x, y) coordinates and radius of the circles to integers
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circles = np.round(circles[0, :]).astype("int")
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# loop over the (x, y) coordinates and radius of the circles
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for (x, y, r) in circles:
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print("Tracing circle x:%s, y:%s, r:%s" % (x,y,r))
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# draw the circle in the output image, then draw a rectangle
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# corresponding to the center of the circle
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cv2.circle(output, (x, y), r, (0, 255, 0), 4)
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cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
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# Check our ignore_list
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if (should_ignore(ignore_list, x, y)):
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print("Circle in ignore_list: Ignoring")
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else:
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dish_count += 1
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print("Dish count:%s" % (str(dish_count)))
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print("Writing detected image")
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cv2.imwrite('/var/www/html/images/detected.jpg', output)
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if dish_count >= min_dishes:
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print("Starting Buzzer")
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timeout_start = time.time()
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while time.time() < timeout_start + buzz_seconds:
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buzzer.on()
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time.sleep(2)
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buzzer.off()
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time.sleep(1)
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else:
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print("No Dishes Found!")
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if __name__ == "__main__":
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main() |