def record(state):
global config
config = state.app.config
global text_analyzer
text_analyzer = load_class(
config['CLASS_TEXT_ANALYZER']).from_config(config)
text_analyzer.init()
global candy_detector
candy_detector = CandyDetector.from_config(config)
global candy_classifier
candy_classifier = CandyClassifier.from_config(config)
candy_classifier.init()
global candy_trainer
candy_trainer = CandyTrainer.from_config(config)
global image_capture
image_capture = load_class(
config['CLASS_IMAGE_CAPTURE']).from_config(config)
global image_calibrator
image_calibrator = ImageCalibrator.from_config(config)
import os
import sys
import time
import cv2
import numpy as np
sys.path.append('../../webapp')
from candysorter.models.images.calibrate import ImageCalibrator
from candysorter.models.images.detect import CandyDetector
from candysorter.config import get_config
calibrator = ImageCalibrator(area=(1625, 1100), scale=550)
detector = CandyDetector.from_config(get_config(os.getenv('FLASK_ENV', 'dev')))
should_exit = False
def mouse_event(event, x, y, flags, param):
global should_exit
if event == cv2.EVENT_LBUTTONUP:
print("mouse_event:L-click")
should_exit = True
def write_message(image, msg, size=3, thickness=3):
cv2.putText(image, msg, (10, 130), font, size, (250, 30, 30), thickness)
def main():
parser = argparse.ArgumentParser(description='Gather images for training.')
parser.add_argument('--image_dir',
type=str,
default="training_dir",
help="location for new training images + /label_name")
detector = CandyDetector.from_config(config_dict)
td = Trainingdata()
args = parser.parse_args()
image_dir = args.image_dir
candy_list = config_img.BOX_CANDIES
if config.CANDY_TYPE == 0:
candy_list = config_img.TWIST_CANDIES
print('The following labels are available in directory: ')
for i in range(len(candy_list)):
print(i, ':', candy_list[i])
lid = int(input("input label number: "))
label = ""
if lid < 0 or lid >= len(candy_list):
raise ValueError("{} is not a valid input".format(lid))
else:
label = candy_list[lid]
path_to_label_dir = os.path.join(image_dir, label)
if not tf.gfile.Exists(path_to_label_dir):
tf.gfile.MakeDirs(path_to_label_dir)
capture = cv2.VideoCapture(0)
capture.set(3, 1920)
capture.set(4, 1080)
w2_size = (960, 540)
cv2.namedWindow('Detection', cv2.WINDOW_KEEPRATIO | cv2.WINDOW_NORMAL)
cv2.resizeWindow('Detection', *w2_size)
cv2.setMouseCallback('Detection', td.mouse_event)
candies = None
counter = 0
while True:
time.sleep(0.01)
if capture.isOpened:
ret, frame = capture.read()
if not ret:
break
corners = td.detect_corners(frame)
if corners is not None:
cropped = calibrator.calibrate(frame)
#write_message(cropped, label)
#td.write_message(cropped, 'click right mouse button to take image')
if (counter % 5 == 0):
candies = detector.detect(cropped)
td.set_variables(
candies, os.path.join(os.path.join(image_dir, label)))
td.draw_detection(cropped, candies)
cv2.imshow('Detection', cropped)
td.write_ok(frame)
counter += 1
else:
blank = np.zeros((w2_size[1], w2_size[0], 3), np.uint8)
td.write_message(blank,
'Marker detection failed',
size=1,
thickness=1)
cv2.imshow('Detection', blank)
key = cv2.waitKey(1)
if (key == ord(' ')):
td.take_pictures_and_save()
if (key == 27):
break
if should_exit:
break
print("Exit.")
cv2.destroyAllWindows()
sys.path.append('../../webapp')
from candysorter.models.images.calibrate import ImageCalibrator
from candysorter.models.images.detect import CandyDetector
from candysorter.config import get_config
calibrator = ImageCalibrator(area=(1625, 1100), scale=550)
config = get_config(os.getenv('FLASK_ENV', 'dev'))
detector = CandyDetector(
histgram_band=config.CANDY_DETECTOR_HISTGRAM_BAND,
histgram_thres=config.CANDY_DETECTOR_HISTGRAM_THRES,
bin_thres=config.CANDY_DETECTOR_BIN_THRES,
edge3_thres=config.CANDY_DETECTOR_EDGE3_THRES,
edge5_thres=config.CANDY_DETECTOR_EDGE5_THRES,
margin=config.CANDY_DETECTOR_MARGIN,
closing_iter=config.CANDY_DETECTOR_CLOSING_ITER,
opening_iter=config.CANDY_DETECTOR_OPENING_ITER,
erode_iter=config.CANDY_DETECTOR_ERODE_ITER,
dilate_iter=config.CANDY_DETECTOR_DILATE_ITER,
bg_size_filter=config.CANDY_DETECTOR_BG_SIZE_FILTER,
sure_fg_thres=config.CANDY_DETECTOR_SURE_FG_THRES,
restore_fg_thres=config.CANDY_DETECTOR_RESTORE_FG_THRES,
box_dim_thres=config.CANDY_DETECTOR_BOX_DIM_THRES
)
should_exit = False
def mouse_event(event, x, y, flags, param):
global should_exit
if event == cv2.EVENT_LBUTTONUP:
print("mouse_event:L-click")
sys.path.append('../../webapp')
from candysorter.models.images.calibrate import ImageCalibrator
from candysorter.models.images.detect import CandyDetector
from candysorter.config import get_config
calibrator = ImageCalibrator(area=(1625, 1100), scale=550)
config = get_config(os.getenv('FLASK_ENV', 'dev'))
# Convert config from class object to dictionary for compatability with flask
config_dict = {}
for key in dir(config):
if key.isupper():
config_dict[key] = getattr(config, key)
detector = CandyDetector().from_config(config_dict)
should_exit = False
def mouse_event(event, x, y, flags, param):
global should_exit
if event == cv2.EVENT_LBUTTONUP:
print("mouse_event:L-click")
should_exit = True
def write_message(image, msg, size=3, thickness=3):
cv2.putText(image, msg, (10, 130), font, size, (250, 30, 30), thickness)