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main.py
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main.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
6.819 Advances in Computer Vision
Bill Freeman, Antonio Torralba
Final Project - Optical Music Recognition Program
cadenCV
"""
__author__ = "Afika Nyati"
__email__ = "anyati@mit.edu"
__status__ = "Prototype"
# cv2.imshow("Input", no_staff_img)
# cv2.waitKey(0)
#-------------------------------------------------------------------------------
# Import Statements
#-------------------------------------------------------------------------------
import sys
import cv2
import numpy as np
from matplotlib import pyplot as plt
from collections import Counter
from copy import deepcopy
from PIL import Image
from midiutil.MidiFile import MIDIFile
from best_match import match
from box import BoundingBox
from staff import Staff
from primitive import Primitive
from bar import Bar
#-------------------------------------------------------------------------------
# Template Paths
#-------------------------------------------------------------------------------
clef_paths = {
"treble": [
"resources/template/clef/treble_1.jpg",
"resources/template/clef/treble_2.jpg"
],
"bass": [
"resources/template/clef/bass_1.jpg"
]
}
accidental_paths = {
"sharp": [
"resources/template/sharp-line.png",
"resources/template/sharp-space.png"
],
"flat": [
"resources/template/flat-line.png",
"resources/template/flat-space.png"
]
}
note_paths = {
"quarter": [
"resources/template/note/quarter.png",
"resources/template/note/solid-note.png"
],
"half": [
"resources/template/note/half-space.png",
"resources/template/note/half-note-line.png",
"resources/template/note/half-line.png",
"resources/template/note/half-note-space.png"
],
"whole": [
"resources/template/note/whole-space.png",
"resources/template/note/whole-note-line.png",
"resources/template/note/whole-line.png",
"resources/template/note/whole-note-space.png"
]
}
rest_paths = {
"eighth": ["resources/template/rest/eighth_rest.jpg"],
"quarter": ["resources/template/rest/quarter_rest.jpg"],
"half": ["resources/template/rest/half_rest_1.jpg",
"resources/template/rest/half_rest_2.jpg"],
"whole": ["resources/template/rest/whole_rest.jpg"]
}
flag_paths = ["resources/template/flag/eighth_flag_1.jpg",
"resources/template/flag/eighth_flag_2.jpg",
"resources/template/flag/eighth_flag_3.jpg",
"resources/template/flag/eighth_flag_4.jpg",
"resources/template/flag/eighth_flag_5.jpg",
"resources/template/flag/eighth_flag_6.jpg"]
barline_paths = ["resources/template/barline/barline_1.jpg",
"resources/template/barline/barline_2.jpg",
"resources/template/barline/barline_3.jpg",
"resources/template/barline/barline_4.jpg"]
#-------------------------------------------------------------------------------
# Template Images
#-------------------------------------------------------------------------------
# Clefs
clef_imgs = {
"treble": [cv2.imread(clef_file, 0) for clef_file in clef_paths["treble"]],
"bass": [cv2.imread(clef_file, 0) for clef_file in clef_paths["bass"]]
}
# Time Signatures
time_imgs = {
"common": [cv2.imread(time, 0) for time in ["resources/template/time/common.jpg"]],
"44": [cv2.imread(time, 0) for time in ["resources/template/time/44.jpg"]],
"34": [cv2.imread(time, 0) for time in ["resources/template/time/34.jpg"]],
"24": [cv2.imread(time, 0) for time in ["resources/template/time/24.jpg"]],
"68": [cv2.imread(time, 0) for time in ["resources/template/time/68.jpg"]]
}
# Accidentals
sharp_imgs = [cv2.imread(sharp_files, 0) for sharp_files in accidental_paths["sharp"]]
flat_imgs = [cv2.imread(flat_file, 0) for flat_file in accidental_paths["flat"]]
# Notes
quarter_note_imgs = [cv2.imread(quarter, 0) for quarter in note_paths["quarter"]]
half_note_imgs = [cv2.imread(half, 0) for half in note_paths["half"]]
whole_note_imgs = [cv2.imread(whole, 0) for whole in note_paths['whole']]
# Rests
eighth_rest_imgs = [cv2.imread(eighth, 0) for eighth in rest_paths["eighth"]]
quarter_rest_imgs = [cv2.imread(quarter, 0) for quarter in rest_paths["quarter"]]
half_rest_imgs = [cv2.imread(half, 0) for half in rest_paths["half"]]
whole_rest_imgs = [cv2.imread(whole, 0) for whole in rest_paths['whole']]
# Eighth Flag
eighth_flag_imgs = [cv2.imread(flag, 0) for flag in flag_paths]
# Bar line
bar_imgs = [cv2.imread(barline, 0) for barline in barline_paths]
#-------------------------------------------------------------------------------
# Template Thresholds
#-------------------------------------------------------------------------------
# Clefs
clef_lower, clef_upper, clef_thresh = 50, 150, 0.88
# Time
time_lower, time_upper, time_thresh = 50, 150, 0.85
# Accidentals
sharp_lower, sharp_upper, sharp_thresh = 50, 150, 0.70
flat_lower, flat_upper, flat_thresh = 50, 150, 0.77
# Notes
quarter_note_lower, quarter_note_upper, quarter_note_thresh = 50, 150, 0.70
half_note_lower, half_note_upper, half_note_thresh = 50, 150, 0.70
whole_note_lower, whole_note_upper, whole_note_thresh = 50, 150, 0.7011
# Rests
eighth_rest_lower, eighth_rest_upper, eighth_rest_thresh = 50, 150, 0.75 # Before was 0.7
quarter_rest_lower, quarter_rest_upper, quarter_rest_thresh = 50, 150, 0.70
half_rest_lower, half_rest_upper, half_rest_thresh = 50, 150, 0.80
whole_rest_lower, whole_rest_upper, whole_rest_thresh = 50, 150, 0.80
# Eighth Flag
eighth_flag_lower, eighth_flag_upper, eighth_flag_thresh = 50, 150, 0.8
# Bar line
bar_lower, bar_upper, bar_thresh = 50, 150, 0.85
#-------------------------------------------------------------------------------
# Mapping Functions
#-------------------------------------------------------------------------------
pitch_to_MIDI = {
"C8": 108,
"B7": 107,
"Bb7": 106,
"A#7": 106,
"A7": 105,
"Ab7": 104,
"G#7": 104,
"G7": 103,
"Gb7": 102,
"F#7": 102,
"F7": 101,
"E7": 100,
"Eb7": 99,
"D#7": 99,
"D7": 98,
"Db7": 97,
"C#7": 97,
"C7": 96,
"B6": 95,
"Bb6": 94,
"A#6": 94,
"A6": 93,
"Ab6": 92,
"G#6": 92,
"G6": 91,
"Gb6": 90,
"F#6": 90,
"F6": 89,
"E6": 88,
"Eb6": 87,
"D#6": 87,
"D6": 86,
"Db6": 85,
"C#6": 85,
"C6": 84,
"B5": 83,
"Bb5": 82,
"A#5": 82,
"A5": 81,
"Ab5": 80,
"G#5": 80,
"G5": 79,
"Gb5": 78,
"F#5": 78,
"F5": 77,
"E5": 76,
"Eb5": 75,
"D#5": 75,
"D5": 74,
"Db5": 73,
"C#5": 73,
"C5": 72,
"B4": 71,
"Bb4": 70,
"A#4": 70,
"A4": 69,
"Ab4": 68,
"G#4": 68,
"G4": 67,
"Gb4": 66,
"F#4": 66,
"F4": 65,
"E4": 64,
"Eb4": 63,
"D#4": 63,
"D4": 62,
"Db4": 61,
"C#4": 61,
"C4": 60,
"B3": 59,
"Bb3": 58,
"A#3": 58,
"A3": 57,
"Ab3": 56,
"G#3": 56,
"G3": 55,
"Gb3": 54,
"F#3": 54,
"F3": 53,
"E3": 52,
"Eb3": 51,
"D#3": 51,
"D3": 50,
"Db3": 49,
"C#3": 49,
"C3": 48,
"B2": 47,
"Bb2": 46,
"A#2": 46,
"A2": 45,
"Ab2": 44,
"G#2": 44,
"G2": 43,
"Gb2": 42,
"F#2": 42,
"F2": 41,
"E2": 40,
"Eb2": 39,
"D#2": 39,
"D2": 38,
"Db2": 37,
"C#2": 37,
"C2": 36,
"B1": 35,
"Bb1": 34,
"A#1": 34,
"A1": 33,
"Ab1": 32,
"G#1": 32,
"G1": 31,
"Gb1": 30,
"F#1": 30,
"F1": 29,
"E1": 28,
"Eb1": 27,
"D#1": 27,
"D1": 26,
"Db1": 25,
"C#1": 25,
"C1": 24,
"B0": 23,
"Bb0": 22,
"A#0": 22,
"A0": 21
}
MIDI_to_pitch = {
108: "C8",
107: "B7",
106: "A#7",
105: "A7",
104: "G#7",
103: "G7",
102: "F#7",
101: "F7",
100: "E7",
99: "D#7",
98: "D7",
97: "C#7",
96: "C7",
95: "B6",
94: "A#6",
93: "A6",
92: "G#6",
91: "G6",
90: "F#6",
89: "F6",
88: "E6",
87: "D#6",
86: "D6",
85: "C#6",
84: "C6",
83: "B5",
82: "A#5",
81: "A5",
80: "G#5",
79: "G5",
78: "F#5",
77: "F5",
76: "E5",
75: "D#5",
74: "D5",
73: "C#5",
72: "C5",
71: "B4",
70: "A#4",
69: "A4",
68: "G#4",
67: "G4",
66: "F#4",
65: "F4",
64: "E4",
63: "D#4",
62: "D4",
61: "C#4",
60: "C4",
59: "B3",
58: "A#3",
57: "A3",
56: "G#3",
55: "G3",
54: "F#3",
53: "F3",
52: "E3",
51: "D#3",
50: "D3",
49: "C#3",
48: "C3",
47: "B2",
46: "A#2",
45: "A2",
44: "G#2",
43: "G2",
42: "F#2",
41: "F2",
40: "E2",
39: "D#2",
38: "D2",
37: "C#2",
36: "C2",
35: "B1",
34: "A#1",
33: "A1",
32: "G#1",
31: "G1",
30: "F#1",
29: "F1",
28: "E1",
27: "D#1",
26: "D1",
25: "C#1",
24: "C1",
23: "B0",
22: "A#0",
21: "A0"
}
key_signature_changes = {
"sharp": ["", "F", "FC", "FCG", "FCGD", "FCGDA", "FCGDAE", "FCGDAEB"],
"flat": ["", "B", "BE", "BEA", "BEAD", "BEADG", "BEADGC", "BEADGCF"]
}
#-------------------------------------------------------------------------------
# General Functions
#-------------------------------------------------------------------------------
def deskew(img):
skew_img = cv2.bitwise_not(img) # Invert image
# grab the (x, y) coordinates of all pixel values that
# are greater than zero, then use these coordinates to
# compute a rotated bounding box that contains all
# coordinates
coords = np.column_stack(np.where(skew_img > 0))
angle = cv2.minAreaRect(coords)[-1]
# the `cv2.minAreaRect` function returns values in the
# range [-90, 0); as the rectangle rotates clockwise the
# returned angle trends to 0 -- in this special case we
# need to add 90 degrees to the angle
if angle < -45:
angle = -(90 + angle)
# otherwise, just take the inverse of the angle to make
# it positive
else:
angle = -angle
# rotate the image to deskew it
(h, w) = img.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img, M, (w, h),
flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
return angle, rotated
def get_ref_lengths(img):
num_rows = img.shape[0] # Image Height (number of rows)
num_cols = img.shape[1] # Image Width (number of columns)
rle_image_white_runs = [] # Cumulative white run list
rle_image_black_runs = [] # Cumulative black run list
sum_all_consec_runs = [] # Cumulative consecutive black white runs
for i in range(num_cols):
col = img[:, i]
rle_col = []
rle_white_runs = []
rle_black_runs = []
run_val = 0 # (The number of consecutive pixels of same value)
run_type = col[0] # Should be 255 (white) initially
for j in range(num_rows):
if (col[j] == run_type):
# increment run length
run_val += 1
else:
# add previous run length to rle encoding
rle_col.append(run_val)
if (run_type == 0):
rle_black_runs.append(run_val)
else:
rle_white_runs.append(run_val)
# alternate run type
run_type = col[j]
# increment run_val for new value
run_val = 1
# add final run length to encoding
rle_col.append(run_val)
if (run_type == 0):
rle_black_runs.append(run_val)
else:
rle_white_runs.append(run_val)
# Calculate sum of consecutive vertical runs
sum_rle_col = [sum(rle_col[i: i + 2]) for i in range(len(rle_col))]
# Add to column accumulation list
rle_image_white_runs.extend(rle_white_runs)
rle_image_black_runs.extend(rle_black_runs)
sum_all_consec_runs.extend(sum_rle_col)
white_runs = Counter(rle_image_white_runs)
black_runs = Counter(rle_image_black_runs)
black_white_sum = Counter(sum_all_consec_runs)
line_spacing = white_runs.most_common(1)[0][0]
line_width = black_runs.most_common(1)[0][0]
width_spacing_sum = black_white_sum.most_common(1)[0][0]
assert (line_spacing + line_width == width_spacing_sum), "Estimated Line Thickness + Spacing doesn't correspond with Most Common Sum "
return line_width, line_spacing
def find_staffline_rows(img, line_width, line_spacing):
num_rows = img.shape[0] # Image Height (number of rows)
num_cols = img.shape[1] # Image Width (number of columns)
row_black_pixel_histogram = []
# Determine number of black pixels in each row
for i in range(num_rows):
row = img[i]
num_black_pixels = 0
for j in range(len(row)):
if (row[j] == 0):
num_black_pixels += 1
row_black_pixel_histogram.append(num_black_pixels)
# plt.bar(np.arange(num_rows), row_black_pixel_histogram)
# plt.show()
all_staff_row_indices = []
num_stafflines = 5
threshold = 0.4
staff_length = num_stafflines * (line_width + line_spacing) - line_spacing
iter_range = num_rows - staff_length + 1
# Find stafflines by finding sum of rows that occur according to
# staffline width and staffline space which contain as many black pixels
# as a thresholded value (based of width of page)
#
# Filter out using condition that all lines in staff
# should be above a threshold of black pixels
current_row = 0
while (current_row < iter_range):
staff_lines = [row_black_pixel_histogram[j: j + line_width] for j in
range(current_row, current_row + (num_stafflines - 1) * (line_width + line_spacing) + 1,
line_width + line_spacing)]
pixel_avg = sum(sum(staff_lines, [])) / (num_stafflines * line_width)
for line in staff_lines:
if (sum(line) / line_width < threshold * num_cols):
current_row += 1
break
else:
staff_row_indices = [list(range(j, j + line_width)) for j in
range(current_row,
current_row + (num_stafflines - 1) * (line_width + line_spacing) + 1,
line_width + line_spacing)]
all_staff_row_indices.append(staff_row_indices)
current_row = current_row + staff_length
return all_staff_row_indices
def find_staffline_columns(img, all_staffline_vertical_indices, line_width, line_spacing):
num_rows = img.shape[0] # Image Height (number of rows)
num_cols = img.shape[1] # Image Width (number of columns)
# Create list of tuples of the form (column index, number of occurrences of width_spacing_sum)
all_staff_extremes = []
# Find start of staff for every staff in piece
for i in range(len(all_staffline_vertical_indices)):
begin_list = [] # Stores possible beginning column indices for staff
end_list = [] # Stores possible end column indices for staff
begin = 0
end = num_cols - 1
# Find staff beginning
for j in range(num_cols // 2):
first_staff_rows_isolated = img[all_staffline_vertical_indices[i][0][0]:all_staffline_vertical_indices[i][4][
line_width - 1], j]
num_black_pixels = len(list(filter(lambda x: x == 0, first_staff_rows_isolated)))
if (num_black_pixels == 0):
begin_list.append(j)
# Find maximum column that has no black pixels in staff window
list.sort(begin_list, reverse=True)
begin = begin_list[0]
# Find staff beginning
for j in range(num_cols // 2, num_cols):
first_staff_rows_isolated = img[all_staffline_vertical_indices[i][0][0]:all_staffline_vertical_indices[i][4][
line_width - 1], j]
num_black_pixels = len(list(filter(lambda x: x == 0, first_staff_rows_isolated)))
if (num_black_pixels == 0):
end_list.append(j)
# Find maximum column that has no black pixels in staff window
list.sort(end_list)
end = end_list[0]
staff_extremes = (begin, end)
all_staff_extremes.append(staff_extremes)
return all_staff_extremes
def remove_stafflines(img, all_staffline_vertical_indices):
no_staff_img = deepcopy(img)
for staff in all_staffline_vertical_indices:
for line in staff:
for row in line:
# Remove top and bottom line to be sure
no_staff_img[row - 1, :] = 255
no_staff_img[row, :] = 255
no_staff_img[row + 1, :] = 255
return no_staff_img
def open_file(path):
img = Image.open(path)
img.show()
def locate_templates(img, templates, start, stop, threshold):
locations, scale = match(img, templates, start, stop, threshold)
img_locations = []
for i in range(len(templates)):
w, h = templates[i].shape[::-1]
w *= scale
h *= scale
img_locations.append([BoundingBox(pt[0], pt[1], w, h) for pt in zip(*locations[i][::-1])])
return img_locations
def merge_boxes(boxes, threshold):
filtered_boxes = []
while len(boxes) > 0:
r = boxes.pop(0)
boxes.sort(key=lambda box: box.distance(r))
merged = True
while (merged):
merged = False
i = 0
for _ in range(len(boxes)):
if r.overlap(boxes[i]) > threshold or boxes[i].overlap(r) > threshold:
r = r.merge(boxes.pop(i))
merged = True
elif boxes[i].distance(r) > r.w / 2 + boxes[i].w / 2:
break
else:
i += 1
filtered_boxes.append(r)
return filtered_boxes
if __name__ == "__main__":
#-------------------------------------------------------------------------------
# Image Preprocessing (Blurring, Noise Removal, Binarization, Deskewing)
#-------------------------------------------------------------------------------
# Noise Removal: https://docs.opencv.org/3.3.1/d5/d69/tutorial_py_non_local_means.html
# Deskewing: https://www.pyimagesearch.com/2017/02/20/text-skew-correction-opencv-python/
# Binarization + Blurring (Otsu): https://docs.opencv.org/3.3.1/d7/d4d/tutorial_py_thresholding.html
# ============ Read Image ============
img_file = sys.argv[1:][0]
img = cv2.imread(img_file, 0)
# ============ Noise Removal ============
img = cv2.fastNlMeansDenoising(img, None, 10, 7, 21)
# ============ Binarization ============
# Global Thresholding
# retval, img = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
# Otsu's Thresholding
retval, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
cv2.imwrite('binarized.jpg', img)
# ============ Deskewing ============
# angle, img = deskew(img)
# print("[INFO] Deskew Angle: {:.3f}".format(angle))
# cv2.imshow("Input", img)
# cv2.waitKey(0)
# ============ Reference Lengths ============
# Reference lengths staff line thickness (staffline_height)
# and vertical line distance within the same staff (staffspace_height)
# computed, providing the basic scale for relative size comparisons
# Use run-length encoding on columns to estimate staffline height and staffspace height
line_width, line_spacing = get_ref_lengths(img)
print("[INFO] Staff line Width: ", line_width)
print("[INFO] Staff line Spacing: ", line_spacing)
#-------------------------------------------------------------------------------
# Staff Line Detection
#-------------------------------------------------------------------------------
# In practice, several horizontal projections on images with slightly different
# rotation angles are computed to deal with not completely horizontal staff lines.
# The projection with the highest local maxima is then chosen.
# ============ Find Staff Line Rows ============
all_staffline_vertical_indices = find_staffline_rows(img, line_width, line_spacing)
print("[INFO] Found ", len(all_staffline_vertical_indices), " sets of staff lines")
# ============ Find Staff Line Columns ============
# Find column with largest index that has no black pixels
all_staffline_horizontal_indices = find_staffline_columns(img, all_staffline_vertical_indices, line_width, line_spacing)
print("[INFO] Found all staff line horizontal extremes")
# ============ Show Detected Staffs ============
staffs = []
half_dist_between_staffs = (all_staffline_vertical_indices[1][0][0] - all_staffline_vertical_indices[0][4][line_width - 1])//2
for i in range(len(all_staffline_vertical_indices)):
# Create Bounding Box
x = all_staffline_horizontal_indices[i][0]
y = all_staffline_vertical_indices[i][0][0]
width = all_staffline_horizontal_indices[i][1] - x
height = all_staffline_vertical_indices[i][4][line_width - 1] - y
staff_box = BoundingBox(x, y, width, height)
# Create Cropped Staff Image
staff_img = img[max(0, y - half_dist_between_staffs): min(y+ height + half_dist_between_staffs, img.shape[0] - 1), x:x+width]
# Normalize Staff line Numbers to Cropped Image
pixel = half_dist_between_staffs
normalized_staff_line_vertical_indices = []
for j in range(5):
line = []
for k in range(line_width):
line.append(pixel)
pixel += 1
normalized_staff_line_vertical_indices.append(line)
pixel += line_spacing + 1
staff = Staff(normalized_staff_line_vertical_indices, staff_box, line_width, line_spacing, staff_img)
staffs.append(staff)
staff_boxes_img = img.copy()
staff_boxes_img = cv2.cvtColor(staff_boxes_img, cv2.COLOR_GRAY2RGB)
red = (0, 0, 255)
box_thickness = 2
for staff in staffs:
box = staff.getBox()
box.draw(staff_boxes_img, red, box_thickness)
x = int(box.getCorner()[0] + (box.getWidth() // 2))
y = int(box.getCorner()[1] + box.getHeight() + 35)
cv2.putText(staff_boxes_img, "Staff", (x, y), cv2.FONT_HERSHEY_DUPLEX, 0.9 , red)
cv2.imwrite('output/detected_staffs.jpg', staff_boxes_img)
# open_file('output/detected_staffs.jpg')
print("[INFO] Saving detected staffs onto disk")
#-------------------------------------------------------------------------------
# Symbol Segmentation, Object Recognition, and Semantic Reconstruction
#-------------------------------------------------------------------------------
# The score is then divided into regions of interest to localize and isolate the musical primitives.
# Music score is analyzed and split by staves
# Primitive symbols extracted
# Find all primitives on each stave first
# then move from left to right and create structure
# ============ Determine Clef, Time Signature ============
staff_imgs_color = []
for i in range(len(staffs)):
red = (0, 0, 255)
box_thickness = 2
staff_img = staffs[i].getImage()
staff_img_color = staff_img.copy()
staff_img_color = cv2.cvtColor(staff_img_color, cv2.COLOR_GRAY2RGB)
# ------- Clef -------
for clef in clef_imgs:
print("[INFO] Matching {} clef template on staff".format(clef), i + 1)
clef_boxes = locate_templates(staff_img, clef_imgs[clef], clef_lower, clef_upper, clef_thresh)
clef_boxes = merge_boxes([j for i in clef_boxes for j in i], 0.5)
if (len(clef_boxes) == 1):
print("[INFO] Clef Found: ", clef)
staffs[i].setClef(clef)
# print("[INFO] Displaying Matching Results on staff", i + 1)
clef_boxes_img = staffs[i].getImage()
clef_boxes_img = clef_boxes_img.copy()
for boxes in clef_boxes:
boxes.draw(staff_img_color, red, box_thickness)
x = int(boxes.getCorner()[0] + (boxes.getWidth() // 2))
y = int(boxes.getCorner()[1] + boxes.getHeight() + 10)
cv2.putText(staff_img_color, "{} clef".format(clef), (x, y), cv2.FONT_HERSHEY_DUPLEX, 0.9, red)
break
else:
# A clef should always be found
print("[INFO] No clef found on staff", i+1)
# # ------- Time -------
for time in time_imgs:
print("[INFO] Matching {} time signature template on staff".format(time), i + 1)
time_boxes = locate_templates(staff_img, time_imgs[time], time_lower, time_upper, time_thresh)
time_boxes = merge_boxes([j for i in time_boxes for j in i], 0.5)
if (len(time_boxes) == 1):
print("[INFO] Time Signature Found: ", time)
staffs[i].setTimeSignature(time)
# print("[INFO] Displaying Matching Results on staff", i + 1)
for boxes in time_boxes:
boxes.draw(staff_img_color, red, box_thickness)
x = int(boxes.getCorner()[0] - (boxes.getWidth() // 2))
y = int(boxes.getCorner()[1] + boxes.getHeight() + 20)
cv2.putText(staff_img_color, "{} time".format(time), (x, y), cv2.FONT_HERSHEY_DUPLEX, 0.9, red)
break
elif (len(time_boxes) == 0 and i > 0):
# Take time signature of previous staff
previousTime = staffs[i-1].getTimeSignature()
staffs[i].setTimeSignature(previousTime)
print("[INFO] No time signature found on staff", i + 1, ". Using time signature from previous staff line: ", previousTime)
break
else:
print("[INFO] No time signature available for staff", i + 1)
staff_imgs_color.append(staff_img_color)
# ============ Find Primitives ============
# always assert that notes in a bar equal duration dictated by time signature
for i in range(len(staffs)):
print("[INFO] Finding Primitives on Staff ", i+1)
staff_primitives = []
staff_img = staffs[i].getImage()
staff_img_color = staff_imgs_color[i]
red = (0, 0, 255)
box_thickness = 2
# ------- Find primitives on staff -------
print("[INFO] Matching sharp accidental template...")
sharp_boxes = locate_templates(staff_img, sharp_imgs, sharp_lower, sharp_upper, sharp_thresh)
sharp_boxes = merge_boxes([j for i in sharp_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in sharp_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "sharp"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))
y = int(box.getCorner()[1] + box.getHeight() + 20)
cv2.putText(staff_img_color, text, (x, y), font, fontScale=0.7, color=red, thickness=1)
sharp = Primitive("sharp", 0, box)
staff_primitives.append(sharp)
print("[INFO] Matching flat accidental template...")
flat_boxes = locate_templates(staff_img, flat_imgs, flat_lower, flat_upper, flat_thresh)
flat_boxes = merge_boxes([j for i in flat_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in flat_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "flat"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))
y = int(box.getCorner()[1] + box.getHeight() + 20)
cv2.putText(staff_img_color, text, (x, y), font, fontScale=0.7, color=red, thickness=1)
flat = Primitive("flat", 0, box)
staff_primitives.append(flat)
print("[INFO] Matching quarter note template...")
quarter_boxes = locate_templates(staff_img, quarter_note_imgs, quarter_note_lower, quarter_note_upper, quarter_note_thresh)
quarter_boxes = merge_boxes([j for i in quarter_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in quarter_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "1/4 note"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))
y = int(box.getCorner()[1] + box.getHeight() + 20)
cv2.putText(staff_img_color, text, (x, y), font, fontScale=0.7, color=red, thickness=1)
pitch = staffs[i].getPitch(round(box.getCenter()[1]))
quarter = Primitive("note", 1, box, pitch)
staff_primitives.append(quarter)
print("[INFO] Matching half note template...")
half_boxes = locate_templates(staff_img, half_note_imgs, half_note_lower, half_note_upper, half_note_thresh)
half_boxes = merge_boxes([j for i in half_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in half_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "1/2 note"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))
y = int(box.getCorner()[1] + box.getHeight() + 20)
cv2.putText(staff_img_color, text, (x, y), font, fontScale=0.7, color=red, thickness=1)
pitch = staffs[i].getPitch(round(box.getCenter()[1]))
half = Primitive("note", 2, box, pitch)
staff_primitives.append(half)
print("[INFO] Matching whole note template...")
whole_boxes = locate_templates(staff_img, whole_note_imgs, whole_note_lower, whole_note_upper, whole_note_thresh)
whole_boxes = merge_boxes([j for i in whole_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in whole_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "1 note"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))
y = int(box.getCorner()[1] + box.getHeight() + 20)
cv2.putText(staff_img_color, text, (x, y), font, fontScale=0.7, color=red, thickness=1)
pitch = staffs[i].getPitch(round(box.getCenter()[1]))
whole = Primitive("note", 4, box, pitch)
staff_primitives.append(whole)
print("[INFO] Matching eighth rest template...")
eighth_boxes = locate_templates(staff_img, eighth_rest_imgs, eighth_rest_lower, eighth_rest_upper, eighth_rest_thresh)
eighth_boxes = merge_boxes([j for i in eighth_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in eighth_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "1/8 rest"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))
y = int(box.getCorner()[1] + box.getHeight() + 20)
cv2.putText(staff_img_color, text, (x, y), font, fontScale=0.7, color=red, thickness=1)
eighth = Primitive("rest", 0.5, box)
staff_primitives.append(eighth)
print("[INFO] Matching quarter rest template...")
quarter_boxes = locate_templates(staff_img, quarter_rest_imgs, quarter_rest_lower, quarter_rest_upper, quarter_rest_thresh)
quarter_boxes = merge_boxes([j for i in quarter_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in quarter_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "1/4 rest"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))
y = int(box.getCorner()[1] + box.getHeight() + 20)
cv2.putText(staff_img_color, text, (x, y), font, fontScale=0.7, color=red, thickness=1)
quarter = Primitive("rest", 1, box)
staff_primitives.append(quarter)
print("[INFO] Matching half rest template...")
half_boxes = locate_templates(staff_img, half_rest_imgs, half_rest_lower, half_rest_upper, half_rest_thresh)
half_boxes = merge_boxes([j for i in half_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in half_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "1/2 rest"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))
y = int(box.getCorner()[1] + box.getHeight() + 20)
cv2.putText(staff_img_color, text, (x, y), font, fontScale=0.7, color=red, thickness=1)
half = Primitive("rest", 2, box)
staff_primitives.append(half)
print("[INFO] Matching whole rest template...")
whole_boxes = locate_templates(staff_img, whole_rest_imgs, whole_rest_lower, whole_rest_upper, whole_rest_thresh)
whole_boxes = merge_boxes([j for i in whole_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in whole_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "1 rest"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))
y = int(box.getCorner()[1] + box.getHeight() + 20)
cv2.putText(staff_img_color, text, (x, y), font, fontScale=0.7, color=red, thickness=1)
whole = Primitive("rest", 4, box)
staff_primitives.append(whole)
print("[INFO] Matching eighth flag template...")
flag_boxes = locate_templates(staff_img, eighth_flag_imgs, eighth_flag_lower, eighth_flag_upper, eighth_flag_thresh)
flag_boxes = merge_boxes([j for i in flag_boxes for j in i], 0.5)
print("[INFO] Displaying Matching Results on staff", i + 1)
for box in flag_boxes:
box.draw(staff_img_color, red, box_thickness)
text = "1/8 flag"
font = cv2.FONT_HERSHEY_DUPLEX
textsize = cv2.getTextSize(text, font, fontScale=0.7, thickness=1)[0]
x = int(box.getCorner()[0] - (textsize[0] // 2))