def _fscale(data, split=False, load=False, verbose=False):
# initialize scaler
scaler = pp.MinMaxScaler()
# initialize variables
config = _configinfo()
sdpath = config['root_data_path'] + 'scaled.npy'
# scale data
if verbose: print('scaling features............... ', end = '')
data = np.array(data, dtype='float64')
if load and os.path.isfile(sdpath):
m = np.load(sdpath)[0]
r = np.load(sdpath)[1]
r[r==0] = 1
data = (data - m) / r
elif split:
train = data[:config['train_d']]
valid = data[config['train_d']:]
scaler.fit(train)
m = scaler.data_min_
r = scaler.data_range_
train = scaler.transform(train)
valid = scaler.transform(valid)
data = np.vstack((train, valid))
else:
data = scaler.fit_transform(data)
m = scaler.data_min_
r = scaler.data_range_
if verbose: print('done')
# save scaled config
if not load: np.save(sdpath, np.vstack((m, r)))
# return scaled data
return data
def main():
# parse arguments
args = _args()
# begin time
t_beg = time.time()
# load config
config = _configinfo()
preprocess = config['preprocess']
shape_x = config['shape_x']
shape_y = config['shape_y']
clfrpath = config['clfr_data_path']
rootpath = config['root_data_path']
filepath = args.filepath
savename = rootpath + os.path.split(filepath)[-1].split('.')[0]
if args.verbose: print('\ninitializing configurations.... done')
# load classifier
if args.verbose: print('loading classifier............. ', end = '')
if os.path.isfile(clfrpath):
classifier = skex.joblib.load(clfrpath)
if args.verbose: print('done')
else:
if args.verbose: print('not found\n')
sys.exit()
# load image
if args.verbose: print('loading image.................. ', end = '')
if os.path.isfile(filepath):
(_, extn) = os.path.splitext(filepath)
if extn in ['.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff']:
image = cv.imread(filepath)
if args.verbose: print('done')
else:
if args.verbose: print('unsupported format\n')
sys.exit()
else:
if args.verbose: print('not found\n')
sys.exit()
image_input = image.copy()
# convert image to grayscale
if args.verbose: print('converting colorspace.......... ', end = '')
image_gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
if args.verbose: print('done')
# apply guassian blurring to remove noise
if args.verbose: print('removing noise from image...... ', end = '')
image_blur = cv.GaussianBlur(image_gray, (3, 3), 0, 0)
if args.verbose: print('done')
# threshold image
if args.verbose: print('thresholding image............. ', end = '')
(_, image_th) = cv.threshold(image_blur, 95, 255, cv.THRESH_BINARY_INV)
if args.verbose: print('done')
image_rois = image_th.copy()
# find contours
if args.verbose: print('finding contours in image..... ', end = '')
(_, contours, _) = cv.findContours(image_th.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
if args.verbose: print('done')
# find bounding rectangle around each contour
bn_rects = []
for cntr in contours:
bn_rects.append(cv.boundingRect(cntr))
if args.verbose: print('number of objects detected..... %d' %(len(bn_rects)))
# process each bounding rectangle
loop = 0
for rect in bn_rects:
if args.verbose: print('\rprocessing objects............. %d%%'
%(loop*100//len(bn_rects)), end = '')
# attributes of bounding rectangle
x = rect[0]
y = rect[1]
w = rect[2]
h = rect[3]
# ignore tiny objects assuming them as noise
if h <= 8:
continue
# draw bounding rectangle on images
cv.rectangle(image_rois, (x, y), (x+w, y+h), (255, 255, 255), 1)
cv.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
# extract region of interest from thresholded image using attributes of bounding rectangle
image_roi = image_th[y:y+h, x:x+w]
# pad region of interest
if w >= h:
length = int(1.8 * w)
else:
length = int(1.8 * h)
image_pad = np.zeros((length, length), dtype='uint8')
i = (length - h) // 2
j = (length - w) // 2
image_pad[i:i+h, j:j+w] = image_roi
# resize padded image
image_roi = cv.resize(image_pad, (shape_x, shape_y), interpolation=cv.INTER_AREA)
# perform dilation as morphological transformation
image_roi = cv.dilate(image_roi, (3, 3))
# preprocess roi
image_roi = image_roi.reshape(1, -1)
if preprocess == 'haar': X = _haar(image_roi, load=False)
elif preprocess == 'hogs': X = _hogs(image_roi, load=False)
elif preprocess == 'sift': X = _sift(image_roi, load=False)
elif preprocess == 'surf': X = _surf(image_roi, load=False)
else:X = _fscale(image_roi, load=True)
X = X.reshape(1, -1)
# predict label
pred = classifier.predict(X)
pred = pred[0]
# put predicted label on image
cv.putText(image, str(pred), (x, y-8), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
loop = loop + 1
if args.verbose: print('\rprocessing objects............. done')
# save images
if args.save:
if args.verbose: print('saving images.................. ', end = '')
cv.imwrite(savename+'_1_input.png' , image_input)
cv.imwrite(savename+'_2_gray.png' , image_gray)
cv.imwrite(savename+'_3_blur.png' , image_blur)
cv.imwrite(savename+'_4_th.png' , image_th)
cv.imwrite(savename+'_5_rois.png' , image_rois)
cv.imwrite(savename+'_6_output.png', image)
if args.verbose: print('done')
# end time
t_end = time.time()
if args.verbose: print('processing time................ %.2f sec\n'
%(t_end - t_beg))
# display image with predictions
cv.imshow('DIGIT RECOGNIZER', image)
cv.waitKey(0)
cv.destroyAllWindows()
