forked from bahaaEldeen1999/Read-That-Note
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classification.py
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classification.py
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from matplotlib.pyplot import show
from commonfunctions import *
from preprocessing import *
from output_handler import *
import skimage as sk
import numpy as np
import matplotlib as mp
import scipy as sp
from heapq import *
import cv2
import joblib
import os.path
from operator import itemgetter
'''
get note head charachter basedon its position
'''
def getFlatHeadNotePos(staff_lines, note, staff_space, charPos, staff_height, img_o, isBeamOrChord):
# 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])
return output
'''
check if the note entered in chord or beam
'''
def check_chord_or_beam(img_input, staff_space):
'''
**img is assumed to be binarized
returns:
0 --> chord
1 --> beam /16
2 --> beam /32
-1 --> neither
'''
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:
return number_of_lines
'''
predict the note given
'''
def classfiyimg(img, staff_space):
out = check_chord_or_beam(img, staff_space)
if(out == -1):
features = extract_features(img)
print(loaded_model.predict([features]))
'''
extract features for the hog classifier
'''
def 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()
return h.flatten()
'''
load dataset to be trainedon/tested
'''
def load_dataset():
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)))
return features, labels
'''
load dataset and run and train dataset
'''
def run_experiment():
# Load dataset with extracted features
print('Loading dataset. This will take time ...')
features, labels = load_dataset()
# print(features)
# print(labels)
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)
print(model_name, 'accuracy:', accuracy*100, '%')