def get_strip(frame):
'''
Get the strip location with the following steps:
1. for a given img, find the location of qr code by utilizing the tool QR-Code-Extractor
2. find approximate position of the black background
3. find exact position of the black background
4. get the strip position
input: img frame
output: stripes background area squares, and the processed img contains only stripes background
:param frame: img frame
:return strip_squares: the vertices of rectangle that containing strip
:return strip_img: img containing only the strip
'''
# straighten the img based on the qr code angle
_, _, _, rotated_img = qe.extract(frame, False)
# get the qr code position
frame, qr_square, qr_area, rotated_img = qe.extract(rotated_img, False)
# get the approximate position of the black background to narrow down the search range
qr_pos = [qr_square[x][0][:] for x in range(len(qr_square))]
mask = get_background_mask_from_qr_pos(qr_pos)
# masked the travail part
masked_image = region_of_interest(frame.copy(), mask)
# get the exact position of the black background based on the approximate position and qr_area
bg_img, bg_squares = get_bg_rectangle(masked_image, qr_area)
bg_area = cv2.contourArea(bg_squares[0])
# straighten the img based on the strip angle
strip_img, _, rotate_strip = get_strip_rectangle(bg_img.copy(), bg_area,
bg_squares)
# get the strip position
strip_img, strip_squares, rotate_strip = get_strip_rectangle(
rotate_strip, bg_area, bg_squares)
return strip_squares, strip_img
import cv2
import qr_extractor as reader
cap = cv2.VideoCapture(0)
while True:
_, frame = cap.read()
codes, frame = reader.extract(frame, True)
cv2.imshow("frame", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
print("I quit!")
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
import cv2
import numpy as np
import qr_extractor as reader
# Load image
img = cv2.imread('testimg.jpg')
# Display image
# Extract qr code
codes, frame = reader.extract(img, True)
cv2.imshow('frame', frame)
print len(codes)
cv2.waitKey(0)
# Display
while True:
# Wait for the next frame
if not video.frame_available():
continue
time.sleep(0.1)
frame = video.frame()
# cv2.imwrite('frame.jpg', frame)
# remove the comment to show a video frame, not work inside docker container
# cv2.imshow('frame', frame)
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
codes, image_frame = reader.extract(frame, False)
if len(codes):
print(1)
timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S-%f')
ret_code, jpg_buffer = cv2.imencode(
".jpg", image_frame, [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality])
jsondata = {'sensor_id': node_name,
'timestamp': timestamp}
jsonstr = json.dumps(jsondata)
# Debug
print("MLO\n", flush=True)
print(jsonstr, flush=True)
sender.send_jpg(jsonstr, jpg_buffer)
# Main loop
# Constants
count = 0
threshold = 24
clearScreen = False
# Aquire webcam capture
webcamID = 1 # 0 for build in webcam, 1 for USB webcam
cap = cv2.VideoCapture(1)
while True:
count = count + 1
_, frame = cap.read() # Read frame from webcam
codes, frame = reader.extract(frame, True) # Extract QR codes from
cv2.imshow("frame", frame) # Display
labels = decodeImages(codes)
# Display data
if count > threshold:
if clearScreen:
cls()
clearScreen = False
else:
print(labels)
clearScreen = True
count = 0
# Quit loop if requested
'''
Orientation and position of QR
Add into Pi RC_main
'''
import cv2
import qr_extractor as reader
cap = cv2.VideoCapture(0)
# change it to pi USB cam
while True:
_, frame = cap.read()
codes, frame, angle_a, angle_b, center = reader.extract(frame, True)
cv2.imshow("frame", frame)
print(angle_a, angle_b, center)
if cv2.waitKey(1) & 0xFF == ord('q'):
print("I quit!")
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()