def __init__(self, *args, **kwargs):
"""
Constructor for CollectProcess class.
Initialize the fast, brisk and bf detectors. Loads the templates data and
create the directory for collection.
:param args:
:param kwargs:
"""
self.__fast = cv2.FastFeatureDetector_create(threshold=50,
nonmaxSuppression=50)
self.__br = cv2.BRISK_create()
self.__bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
try:
model_type = kwargs['model_type']
except KeyError:
model_type = 'CT'
self.__templates = make_templates(model_type, self.__fast, self.__br)
self.__modes = args
self.__kwargs = kwargs
self.__dp = DataPath()
self.__file_index = self.__dp.get_index('collected')
self.__dir = os.path.join(self.__dp.collected, str(self.__file_index))
os.mkdir(self.__dir)
self.__index = 0
col_names = ('detections', 'intensity', 'fast_kp', 'process_time')
self.__df = pd.DataFrame(columns=col_names)
def live_demo():
# initialises fastfeaturedetector to get initial points of interests
fast = cv2.FastFeatureDetector_create(threshold=50, nonmaxSuppression=50)
# initialises Binary Robust Invariant Scalable Keypoints for
# keypoint descriptor analyze
br = cv2.BRISK_create()
# BruteForce matcher to compare matches
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
from src.process import ImageProcess as ip
from src.templates import make_templates
# initialize the data of the templates
ct_models = make_templates('CT', fast, br)
start = time()
while True:
# captures the screen with ImageGrab in RGB.
screen = np.array(ImageGrab.grab(bbox=(0, 27, 800, 627)))
# converts it to BGR
screen = cv2.cvtColor(screen, cv2.COLOR_RGB2BGR)
process = ip(screen, ct_models, fast, br, bf, 'draw')
print('Loop took: {:.5} seconds'.format(time() - start))
cv2.imshow('AimAssistant', process.image)
start = time()
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
def steps(example=0):
from src.process import ImageProcess as ip
from src.templates import make_templates
from src.paths import DataPath
options = [
'norm', 'gray', 'blurred1', 'opening1', 'threshold1', 'masked1',
'opening2', 'blurred2', 'threshold2'
]
fast = cv2.FastFeatureDetector_create(threshold=50, nonmaxSuppression=50)
br = cv2.BRISK_create()
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
ct_models = make_templates('CT', fast, br)
dp = DataPath()
try:
file = os.listdir(dp.examples)[example]
except KeyError:
print('Invalid example')
for option in options:
image = cv2.imread(os.path.join(dp.examples, file))
process = ip(image, ct_models, fast, br, bf, 'draw', debug=option)
if option == 'norm':
test_image = cv2.cvtColor(process.test, cv2.COLOR_BGR2RGB)
plt.imshow(test_image)
else:
plt.imshow(process.test, cmap='gray')
plt.show()
def samples():
from src.process import ImageProcess as ip
from src.templates import make_templates
from src.paths import DataPath
fast = cv2.FastFeatureDetector_create(threshold=50, nonmaxSuppression=50)
br = cv2.BRISK_create()
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
ct_models = make_templates('CT', fast, br)
dp = DataPath()
for file in os.listdir(dp.examples):
image = cv2.imread(os.path.join(dp.examples, file))
process = ip(image, ct_models, fast, br, bf, 'draw')
image = cv2.cvtColor(process.image, cv2.COLOR_BGR2RGB)
plt.imshow(image)
plt.show()