# if charPos[3]-charPos[2] < staff_space:

# return [-1]

img = np.copy(note)

# show_images([img])

s_c = np.copy(staff_lines)

n_c = np.copy(note)

s_c = s_c > 0

n_c = n_c > 0

s_c[charPos[0]:charPos[1], charPos[2]:charPos[3]

] = s_c[charPos[0]:charPos[1], charPos[2]:charPos[3]] | n_c

err = staff_space//8

# edges = np.copy(note)

# edges = edges.astype(int)*255

# show_images([edges])

# se = sk.morphology.disk(staff_space//16)

# img = sk.morphology.binary_closing(img, se)

# img = sp.ndimage.morphology.binary_fill_holes(img)

# show_images([img])

# img = sk.morphology.binary_closing(img)

# img = sk.morphology.binary_dilation(img)

se = sk.morphology.disk(staff_space//2-2)

# se[staff_space//4:3*staff_space//4+1,

# staff_space//4:3*staff_space//4+1] = 0

img = sk.morphology.binary_opening(img, se)

# show_images([img])

# se = sk.morphology.disk((staff_space//3))

se = sk.morphology.disk(staff_space//4)

img = sk.morphology.binary_erosion(img)

# show_images([img])

img = sk.morphology.binary_closing(img)

se = sk.morphology.disk(staff_space//2-3)

img = sk.morphology.binary_erosion(img, se)

img = sk.morphology.binary_erosion(img)

se = sk.morphology.disk(staff_space//8+1)

img = sk.morphology.binary_dilation(img, se)

img = sk.morphology.binary_erosion(img)

bounding_boxes = sk.measure.find_contours(img, 0.8)

output = []

#print("no of notes")

# print(len(bounding_boxes))

# show_images([img])

cols = []

for box in bounding_boxes:

try:

[Xmin, Xmax, Ymin, Ymax] = [np.min(box[:, 1]), np.max(

box[:, 1]), np.min(box[:, 0]), np.max(box[:, 0])]

ar = (Xmax-Xmin)/(Ymax-Ymin)

if True:

r0 = int(Ymin)

r1 = int(Ymax)

r0 = max(r0, 0)

r1 = min(r1, staff_lines.shape[0])

col = int(Xmin)

center = (r0+r1)//2

center2 = staff_lines.shape[1]//2

# print("center "+str(center))

# x = np.copy(note)

# x[center-staff_space//2:center+staff_space//2, :] = False

# print(staff_lines[center-staff_height:center+staff_height, :])

# show_images(

# [note, x, staff_lines[center-staff_space//2:center+staff_space//2, :]])

# show_images(

# [s_c[center-staff_space//2:center+staff_space//2, :]])

horz_hist = np.sum(

staff_lines[center-staff_height:center+staff_height, :], axis=1)

# print(horz_hist)

maximum = np.sum(horz_hist)

up_arr = runs_of_ones_array(

staff_lines[0:center-staff_height//2, center2])

up = 0

t = staff_height//2

for x in up_arr:

if x >= t:

up += 1

# print(maximum)

cols.append(col)

if maximum < staff_lines.shape[1]:

# print("space")

if up == 0:

x = 0

i = center

while i < staff_lines.shape[0] and staff_lines[i, center2] == False:

x += 1

i += 1

if x < staff_space:

output.append("g2")

elif x < 1.5*staff_space:

output.append("a2")

else:

output.append("b2")

elif up == 1:

output.append("e2")

elif up == 2:

output.append("c2")

elif up == 3:

output.append("a1")

elif up == 4:

output.append("f1")

else:

x = 0

i = center

while i >= 0 and staff_lines[i, center2] == False:

x += 1

i -= 1

if abs(x) < staff_space:

output.append("d1")

else:

output.append("c1")

else:

# print("line")

if up == 5:

x = 0

i = center

while i >= 0 and staff_lines[i, center2] == False:

x += 1

i -= 1

if abs(x) < staff_space:

output.append("d1")

else:

output.append("c1")

elif up == 1:

output.append("d2")

elif up == 2:

output.append("b1")

elif up == 3:

output.append("g1")

elif up == 4:

output.append("e1")

elif up == 0:

x = 0

i = center

while i < staff_lines.shape[0] and staff_lines[i, center2] == False:

x += 1

i += 1

# print("X "+str(x))

if x < staff_space:

output.append("f2")

elif x < 1.5*staff_space:

output.append("a2")

else:

output.append("b2")

if isBeamOrChord == 0:

output = sorted(output)

else:

newArr = []

for i in range(len(cols)):

newArr.append([cols[i], output[i]])

newArr = sorted(newArr, key=itemgetter(0))

output = []

for i in range(len(cols)):

output.append(newArr[i][1])

except:

pass

output.insert(0, charPos[2])

'''

se = sk.morphology.disk(staff_space//2-1)

# img = sk.morphology.binary_opening(img_input, se)

# img = sk.morphology.binary_erosion(img, se)

# img = sk.morphology.binary_erosion(img)

# se = sk.morphology.disk(staff_space//4)

# img = sk.morphology.binary_dilation(img, se)

img = sk.morphology.binary_opening(img_input, se)

# show_images([img])

# se = sk.morphology.disk((staff_space//3))

se = sk.morphology.disk(staff_space//4)

img = sk.morphology.binary_erosion(img)

img = sk.morphology.binary_dilation(img)

se = sk.morphology.disk(staff_space//2-1)

img = sk.morphology.binary_erosion(img, se)

# img = sk.morphology.binary_erosion(img)

se = sk.morphology.disk(staff_space//8+1)

img = sk.morphology.binary_dilation(img, se)

img = sk.morphology.binary_erosion(img)

# show_images([img])

bounding_boxes = sk.measure.find_contours(img, 0.8)

if len(bounding_boxes) < 2:

return -1

newImg = img.copy()

centers, count_disks_spacing = [], 0

for box in bounding_boxes:

[Xmin, Xmax, Ymin, Ymax] = [np.min(box[:, 1]), np.max(

box[:, 1]), np.min(box[:, 0]), np.max(box[:, 0])]

centers.append([Ymin+Ymin//2, Xmin+Xmin//2])

for i in range(1, len(centers)):

if abs(centers[i][1] - centers[i-1][1]) > 2*staff_space:

count_disks_spacing += 1

if count_disks_spacing != len(centers)-1:

return 0

img = sk.morphology.thin(sk.img_as_bool(img_input))

h, theta, d = sk.transform.hough_line(img)

h, theta, d = sk.transform.hough_line_peaks(h, theta, d)

angels = np.rad2deg(theta)

number_of_lines = np.sum(np.abs(angels) > 10)

if number_of_lines < 1 or number_of_lines > 2:

return -1

else:

out = check_chord_or_beam(img, staff_space)

if(out == -1):

features = extract_features(img)

img = img.astype(int)

# show_images([img])

target_img_size = (78, 78)

img = cv2.resize(img.astype('uint8'), target_img_size)

win_size = (64, 64)

cell_size = (8, 8)

block_size_in_cells = (2, 2)

block_size = (block_size_in_cells[1] * cell_size[1],

block_size_in_cells[0] * cell_size[0])

block_stride = (cell_size[1], cell_size[0])

nbins = 15 # Number of orientation bins

hog = cv2.HOGDescriptor(win_size, block_size,

block_stride, cell_size, nbins)

h = hog.compute(img)

h = h.flatten()

path_to_dataset = r'dataset_mixed2\dataset_mixed'

features = []

labels = []

img_filenames = os.listdir(path_to_dataset)

for i, fn in enumerate(img_filenames):

if fn.split('.')[-1] != 'jpg' and fn.split('.')[-1] != 'bmp' and fn.split('.')[-1] != 'png':

continue

label = fn.split('-')[0]

# print(label)

labels.append(label)

path = os.path.join(path_to_dataset, fn)

img = cv2.imread(path)

features.append(extract_features(img))

# show an update every 1,000 images

if i > 0 and i % 1000 == 0:

print("[INFO] processed {}/{}".format(i, len(img_filenames)))

# Load dataset with extracted features

print('Loading dataset. This will take time ...')

features, labels = load_dataset()

print('Finished loading dataset.')

# Since we don't want to know the performance of our classifier on images it has seen before

# we are going to withhold some images that we will test the classifier on after training

train_features, test_features, train_labels, test_labels = train_test_split(

features, labels, test_size=0.2, random_state=random_seed)

for model_name, model in classifiers.items():

print('############## Training', model_name, "##############")

# Train the model only on the training features

model.fit(train_features, train_labels)

# save the model to disk

filename = 'finalized_model.sav'

joblib.dump(model, filename)

# Test the model on images it hasn't seen before

accuracy = model.score(test_features, test_labels)