def remove_staff(img, staff_candidates, T_length):
staff_removed = img.copy()
for c in range(len(staff_candidates)):
cand = staff_candidates[c]
for row, col in cand[1]:
mids = []
j = max(row - int(T_length * 1.5), 0)
while(j < height(staff_removed) - 1 and j <= row + T_length):
while staff_removed[j][col] == 255:
j+=1
while(j < height(staff_removed) - 1 and j <= row + int(T_length * 1.5) and staff_removed[j][col] == 0):
j += 1
if (staff_removed[j][col] == 255):
start = j
while(j < height(staff_removed) - 1 and j <= row + int(T_length * 1.5) and staff_removed[j][col] == 255):
j += 1
if (j - start <= T_length):
middle = start + ((j - start) // 2) + 1
if (np.abs(row - middle) < 10):
mids.append(middle)
for x in range(start, j):
staff_removed[x][col] = 0
if len(mids) > 0 and col > 5:
avg = int(np.round(sum([staff_candidates[c][1][col - i][0] for i in range(5)]) / 5.0))
dists = [np.abs(row - m) for m in mids]
staff_candidates[c][1][col] = ((mids[np.argmin(dists)] + avg) // 2, col)
elif col > 20:
avg = int(np.round(sum([staff_candidates[c][1][col - i][0] for i in range(20)]) / 20.0))
staff_candidates[c][1][col] = (avg, col)
return staff_removed, staff_candidates
def calc_slopes(img, chunks, lines):
chunk_size = height(img) // chunks
slopes = [[0 for _ in range(width(img))] for c in range(chunks)]
for i in range(width(img)):
s = [0 for _ in range(chunks)]
num = [0 for _ in range(chunks)]
for line in lines:
x1,y1,x2,y2 = line[0]
if (x1 <= i and x2 >= i) or (x1 <= i and x2 >= i):
for chunk in range(chunks):
c1 = y1 // chunk_size
c2 = y2 // chunk_size
if (c1 >= chunk and c2 <= chunk) or (c2 >= chunk and c1 <= chunk):
s[chunk] += float(y2 - y1) / float(x2 - x1)
num[chunk] += 1
for j in range(chunks):
if num[j] > 0:
slopes[j][i] = float(s[j]) / float(num[j])
img_blank = img.copy()
k = 0
for s in slopes:
row = int(np.round((height(img_blank) // len(slopes)) * (k + .5)))
k += 1
line = calc_staff_line(s, row, img)
for pt in line:
img_blank[pt[0]][pt[1]] = 255
return slopes, img_blank
def create_initial_staff_image(img, T_length):
img_copy = img.copy()
for col in range(width(img_copy)):
i = 0
while(i < height(img_copy)):
while(i < height(img_copy) and img_copy[i][col] == 0):
i+=1
mark = i
while(i < height(img_copy) and img_copy[i][col] == 255):
i+=1
if (i - mark > T_length):
for j in range(mark, i):
img_copy[j][col] = 0
return img_copy
def find_staff_candidates(img, slopes, T_staff_cand, T_length, num_chunks, staff_height, staff_space, min_y, max_y):
staff_candidates = []
chunk_size = height(img) // num_chunks
rows = [i for i in range(min_y - 50, max_y + 50)]
for row in rows:
pts, total_white = calc_staff_line_chunks(slopes, row, img, chunk_size)
if total_white > 0:
if (float(total_white) / width(img) > T_staff_cand):
if (len(staff_candidates) == 0
or (len(staff_candidates) > 0 and row - staff_candidates[-1][0] > T_length)):
staff_candidates.append((row, pts, total_white))
if (len(staff_candidates) > 0 and row - staff_candidates[-1][0] <= T_length and total_white > staff_candidates[-1][2]):
staff_candidates[len(staff_candidates) - 1] = (row, pts, total_white)
to_remove = []
T_staff = (5 * staff_height) + (7 * staff_space)
curr = []
for cand in staff_candidates:
curr.append(cand)
if (curr[-1][0] - curr[0][0] > T_staff):
curr = curr[:len(curr) - 1]
while (len(curr) > 5):
tr = [x for x in curr if x[2] == min([c[2] for c in curr])][0]
to_remove.append(tr)
curr = [x for x in curr if x != tr]
curr = []
for r in to_remove:
staff_candidates.remove(r)
print([str(x[0]) + " " + str(x[2]) for x in staff_candidates])
return staff_candidates
def calc_staff_line(slopes, row, img):
col = 0
r = row
line = []
for slope in slopes:
if (int(np.round(r)) < height(img) and col < width(img)):
line.append((int(np.round(r)), col))
r += slope
col += 1
return line
def calc_staff_line_chunks(slopes, row, img, chunk_size, interval = 15):
white_seen = 0
col = 0
r = row
line = []
for col in range(0, width(img), interval):
if (int(np.round(r)) < height(img) and col < width(img)):
for i in range(interval):
line.append((int(np.round(r)), col + i))
if(img[int(np.round(r))][col] == 255):
white_seen += 1
if (int(r // chunk_size) < len(slopes) and int(r // chunk_size) >= 0):
r += slopes[int(r // chunk_size)][col] * float(interval)
col += 1
return line, white_seen*interval