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Camera.py
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Camera.py
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import logging
import time
import cv2
import imutils
import numpy as np
from pyzbar import pyzbar
class Camera:
def __init__(self):
# Set channels to the number of servo channels on your kit.
# 8 for FeatherWing, 16 for Shield/HAT/Bonnet.
self.blue = 27
self.green = 61
self.red = 114
self.color_lower, self.color_upper = self.get_colour_bound()
self.camera = cv2.VideoCapture(0)
self.frame = None
self.x = 0
self.y = 0
def show_image(self):
# show the frame to our screen
while True:
if self.frame is not None:
#cv2.imshow("Frame", self.frame)
key = cv2.waitKey(1) & 0xFF
time.sleep(0.3)
def get_colour_bound(self):
color = np.uint8([[[self.blue, self.green, self.red]]])
hsv_color = cv2.cvtColor(color, cv2.COLOR_BGR2HSV)
hue = hsv_color[0][0][0]
color_lower = np.array([hue - 10, 100, 100], dtype=np.uint8)
color_upper = np.array([hue + 10, 255, 255], dtype=np.uint8)
print("Lower bound is :{0}".format(color_lower))
print("Upper bound is :{0}".format(color_upper))
self.color_lower, self.color_upper = color_lower, color_upper
return color_lower, color_upper
def get_barcode(self):
(grabbed, self.frame) = self.camera.read()
barcodes = pyzbar.decode(self.frame)
logging.debug(barcodes)
return barcodes
def check_object(self):
# grab the current frame
(grabbed, self.frame) = self.camera.read()
# resize the frame, inverted ("vertical flip" w/ 180degrees),
# blur it, and convert it to the HSV color space
self.frame = imutils.resize(self.frame, width=600)
self.frame = imutils.rotate(self.frame, angle=180)
# blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
# construct a mask for the color "green", then perform
# a series of dilations and erosions to remove any small
# blobs left in the mask
mask = cv2.inRange(hsv, self.color_lower, self.color_upper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
# find contours in the mask and initialize the current
# (x, y) center of the ball
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
center = None
# only proceed if at least one contour was found
if len(cnts) > 0:
# find the largest contour in the mask, then use
# it to compute the minimum enclosing circle and
# centroid
c = max(cnts, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
# print((x,y))
logging.debug('Camera : {0}'.format((x, y)))
self.x = x
self.y = y
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
# only proceed if the radius meets a minimum size
if radius > 20:
# draw the circle and centroid on the frame,
# then update the list of tracked points
cv2.circle(self.frame, (int(x), int(y)), int(radius),
(0, 255, 255), 2)
cv2.circle(self.frame, center, 5, (0, 0, 255), -1)
else:
self.x = -1
self.y = -1
else:
self.x = -1
self.y = -1
cv2.imshow("Frame", self.frame)
key = cv2.waitKey(1) & 0xFF
return self.x, self.y
def camera(self):
# if a video path was not supplied, grab the reference
# to the webcam
# keep looping
self.check_object()
# cleanup the camera and close any open windows
# camera.release()
# cv2.destroyAllWindows()
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG)