def prepare_sharps(image):
image_gray = img_fun.image_gray(image)
image_bin = img_fun.image_bin(image_gray)
image_bin = img_fun.invert(image_bin)
image_bin = cv2.erode(image_bin,np.ones((1,3)),iterations=1)
def get_notes(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, image_bin = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
image_bin=img_fun.invert(image_bin)
horizontalsize = 200;
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontalsize, 1));
image_bin = cv2.erode(image_bin, horizontalStructure, iterations=1)
image_bin = cv2.dilate(image_bin,horizontalStructure, iterations=1)
image_orig, selected_regions, lines = select_horizontal_lines(image.copy(), image_bin)
#cv2.imshow('lines', image_orig)
lines,groups = add_additional_lines(lines)
sheet = chp.get_note_positions(image,groups)
notes = []
#notes = generate_notes(lines, positions)
return sheet
def __init__(self,image):
self.image = image
image_gray = img_fun.image_gray(image)
ret, self.image_bin = cv2.threshold(image_gray, 150, 255, cv2.THRESH_BINARY)
self.image_bin = img_fun.invert(self.image_bin)
self.remove_accs()
def prepare_bar_lines(image):
image_gray = img_fun.image_gray(image)
image_bin = img_fun.image_bin(image_gray)
image_bin = img_fun.invert(image_bin)
# cv2.imshow('bar lines',image_bin)
return image_bin
def prepare_half_notes(image):
image_gray = img_fun.image_gray(image)
image_bin = img_fun.image_bin(image_gray)
image_bin = cv2.morphologyEx(image_bin, cv2.MORPH_OPEN, np.ones((4,4)))
image_bin = cv2.erode(image_bin,np.ones((2,2)),iterations=1)
image_bin = img_fun.invert(image_bin)
return image_bin
def get_note_positions(image,groups):
image_gray = img_fun.image_gray(image)
image_bin = img_fun.image_bin(image_gray)
image_bin = img_fun.invert(image_bin)
image_bin = cv2.dilate(image_bin, np.ones((6,2)), iterations=1)
verticalSize = 4;
verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (verticalSize,1));
image_bin = cv2.erode(image_bin, verticalStructure, iterations=1)
image_bin = cv2.erode(image_bin, np.ones((7,1)), iterations=1)
#image_bin = cv2.dilate(image_bin, np.ones((6,2)), iterations=1)
#image_bin = img_fun.erode(image_bin)
#image_bin = disconnect_note_heads(image.copy(), image_bin)
# image_bin = cv2.erode(image_bin, verticalStructure, iterations=1)
# image_bin = cv2.erode(image_bin, verticalStructure, iterations=3)
# image_bin = disconnect_note_heads(image.copy(), image_bin)
#
# image_bin = cv2.erode(image_bin, np.ones((2,2)), iterations=1)
# image_bin = img_fun.dilate(image_bin)
# kernel = np.ones((3,3)) # strukturni element 3x3 blok
#
# image_bin = img_fun.dilate(image_bin)
# #image_bin = img_fun.dilate(image_bin)
#
# kernel_vet = np.ones((6,1))
# kernel_hor = np.ones((1,5))
# image_bin = cv2.erode(image_bin, kernel_hor, iterations=1)
# image_bin = cv2.dilate(image_bin, kernel_vet, iterations=1)
#
# image_bin = cv2.dilate(image_bin, kernel, iterations=1)
# #image_bin = cv2.erode(image_bin, kernel, iterations=1)
cv2.imshow('preparet image', image_bin)
image_orig,selected_regions, positions = select_note_heads(image.copy(), image_bin, groups)
cv2.imshow('asd', image_orig)
return positions
def prepare_whole_notes(image):
image_gray = img_fun.image_gray(image)
image_bin = img_fun.image_bin(image_gray)
# image_bin = cv2.erode(image_bin,np.ones((1,2)),iterations=3)
#
# image_bin = cv2.dilate(image_bin,np.ones((2,4)),iterations=2)
image_bin = img_fun.invert(image_bin)
# img_fun.show_image('whole_gray',image_gray)
# img_fun.show_image('whole_bin',image_bin)
return image_bin
def get_groups(self,image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, image_bin = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
image_bin=img_fun.invert(image_bin)
horizontalsize = 200;
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontalsize, 1));
image_bin = cv2.erode(image_bin, horizontalStructure, iterations=1)
image_bin = cv2.dilate(image_bin,horizontalStructure, iterations=1)
image_orig, selected_regions, lines = nt_fun.select_horizontal_lines(image.copy(), image_bin)
lines,groups = nt_fun.add_additional_lines(lines)
return groups
def prepare_quarter_notes(image):
image_gray = img_fun.image_gray(image)
image_bin = img_fun.image_bin(image_gray)
# img_fun.show_image(image_bin)
# img_fun.show_image(image_gray)
image_bin = img_fun.invert(image_bin)
verticalSize = 3;
horSize = 3
verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (verticalSize,1));
horStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1,horSize));
image_bin = cv2.erode(image_bin, verticalStructure, iterations=1)
image_bin = cv2.morphologyEx(image_bin, cv2.MORPH_OPEN, np.ones((5,5)))
image_bin = disconnect_note_heads(image.copy(),image_bin)
# cv2.imshow('quarter bin',image_bin)
return image_bin
from sklearn import datasets
from sklearn.cluster import KMeans
import ann_functions as ann_fun
import imageProcessingFunctions as imgFunctions
import note_positions_fun as np_fun
import soundGenerator as sgen
image = cv2.imread("images/sheet4.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, image_bin = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
image_bin = imgFunctions.invert(image_bin)
image_orig, selected_regions, positions = imgFunctions.select_roi(image.copy(), image_bin)
positions = np.array(positions).reshape(len(positions), 1)
print positions
"""
k_means = KMeans(n_clusters=8, max_iter=5000, init='random', tol=0.00001, n_init=10)
k_means.fit(positions)
print k_means.cluster_centers_
"""
cv2.imshow("binary", image_orig)
cv2.waitKey(0)
import cv2
import numpy as np
import matplotlib.pyplot as plt
import imageProcessingFunctions as img_fun
import collections
import note_head_position
import note as nt
import soundGenerator as sgen
image = cv2.imread("images/sheet6.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, image_bin = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
image_bin=img_fun.invert(image_bin)
horizontalsize = 200;
horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontalsize, 1));
image_bin = cv2.erode(image_bin, horizontalStructure, iterations=1)
image_bin = cv2.dilate(image_bin,horizontalStructure, iterations=1)
def select_horizontal_lines(image_orig, image_bin):
img, contours, hierarchy = cv2.findContours(image_bin.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
region_positions = []
regions_dict = {}
for contour in contours:
