def getTrainDict():
'''
Creates a features list from given symbol images in train directory in form of a hashmap
:return: a hashmap(dictionary) of labelled symbols(key) and their features(value) from train directory
'''
trainDir = os.listdir(meta.getRootDir() + '/images/labelled_symbol/')
trainDict = {}
for symbolFile in trainDir:
image = cv2.imread(meta.getRootDir() + '/images/labelled_symbol/' + symbolFile, cv2.IMREAD_GRAYSCALE)
scaledImage = part3.scaleSymbol(image)
meta.saveImage(scaledImage,symbolFile)
symbolName = symbolFile.split('.')
trainDict[symbolName[0]] = scaledImage
return trainDict
def createMidi(mappedKeys, fileName):
track = 0
channel = 0
time = 0 # In beats
duration = 1 # In beats
tempo = 100 # In BPM
volume = 100 # 0-127, as per the MIDI standard
MyMIDI = MIDIFile(
1, adjust_origin=True
) # One track, defaults to format 1 (tempo track is created automatically)
MyMIDI.addTempo(track, time, tempo)
for i, pitch in enumerate(mappedKeys):
if (mappedKeys[i] != 00):
if (len(mappedKeys) > i + 1):
if (mappedKeys[i + 1] != 00):
MyMIDI.addNote(track, channel, pitch, time + i, duration,
volume)
else:
MyMIDI.addNote(track, channel, pitch, time + i,
duration + 1, volume)
else:
MyMIDI.addNote(track, channel, pitch, time + i, duration,
volume)
with open(meta.getRootDir() + '/MIDI_files/' + fileName + '.mid',
"wb") as output_file:
MyMIDI.writeFile(output_file)
# calculates vertical_histogram
i = y1
while (i <= y2):
while (i <= y2 and vertical_histogram[i] == 0):
i = i + 1
start = i
while (i <= y2 and vertical_histogram[i] > 0):
i = i + 1
end = i
if (end - start >= 7):
# Identified a symbol
output.append(Rectangle.Rectangle((start, x1), (end, x2)))
cv2.rectangle(binary_image, (start, x1), (end, x2), 0, 1)
meta.displayImage(binary_image, 'binary')
return output
if __name__ == "__main__":
color_image = cv2.imread(meta.getRootDir() + '/images/vasant_cont.jpeg',
cv2.IMREAD_COLOR)
input_lines = [((0, 0), (439, 30)), ((0, 90), (439, 120)),
((0, 213), (439, 243))]
rectList = []
for tuple in input_lines:
rectList.append(Rectangle.Rectangle(tuple[0], tuple[1]))
o, binary_image = segmentImage(color_image, rectList)
cv2.imshow('binary_image', binary_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
:param color_image: input image
:return: list of identified symbols
'''
gray_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
trainDict = getTrainDict()
testSymbols = getTestSymbols(color_image)
testResults = []
count = 0
for symbol in testSymbols:
gray_symbol = gray_image[symbol.colst:symbol.colen, symbol.rowst:symbol.rowen]
scaledsymbol = part3.scaleSymbol(gray_symbol)
count = count + 1
ans = trainDict.keys()[1]
anscount = len(np.where(scaledsymbol == trainDict[ans]))
for trainedSymbol in trainDict.keys():
matchcount = np.sum(scaledsymbol == trainDict[trainedSymbol])
if matchcount > anscount:
ans, anscount = trainedSymbol, matchcount
testResults.append(ans)
#meta.saveImage(scaledsymbol, ans+str(count)+"kafi.jpeg")
return testResults
if __name__ == "__main__":
color_image = cv2.imread(meta.getRootDir() + '/images/kafi.jpeg', cv2.IMREAD_COLOR)
testResults = main_first(color_image)
# for i in range(len(testResults)):
# if testResults[i] == 'hy' and i>0:
# testResults[i] = testResults[i-1]
print testResults
part4.keyMapping(testResults,'kafi')
symbolImage = meta.binary_threshold(symbolImage, 200)
#symbolImage = meta.gauss_otsu_threshold(symbolImage,ksize=3)
#meta.saveImage(symbolImage, "BinThres.jpeg")
height, width = symbolImage.shape
black_rows = np.where(np.any(symbolImage == 0, axis=1))[0]
black_cols = np.where(np.any(symbolImage == 0, axis=0))[0]
row_start, row_end = 0, height - 1
if len(black_rows) > 0:
row_start, row_end = min(black_rows), max(black_rows)
col_start, col_end = 0, width - 1
if len(black_cols) > 0:
col_start, col_end = min(black_cols), max(black_cols)
s = cv2.resize(symbolImage[row_start:row_end + 1, col_start:col_end + 1],
(32, 32))
return meta.otsu_threshold(s)
if __name__ == "__main__":
trainDir = os.listdir(meta.getRootDir() + '/images/labelled_symbol/')
trainDict = {}
for symbolFile in trainDir:
print symbolFile
image = cv2.imread(
meta.getRootDir() + '/images/labelled_symbol/' + symbolFile,
cv2.IMREAD_GRAYSCALE)
scaledImage = scaleSymbol(image)
meta.saveImage(scaledImage, "output.jpeg")
trainDict[symbolFile[:-5]] = scaledImage
print trainDict.keys()
# meta.displayImage(scaledImage,"ScaledImage")
'''
#meta.saveImage(symbolImage,"input.jpeg")
symbolImage = meta.adapt_mean_threshold(symbolImage,blockSize=9,C=7)
#meta.saveImage(symbolImage, "meanThres.jpeg")
symbolImage = meta.binary_threshold(symbolImage,200)
#meta.saveImage(symbolImage, "BinThres.jpeg")
height, width = symbolImage.shape
black_rows = np.where(np.any(symbolImage == 0, axis=1))[0]
black_cols = np.where(np.any(symbolImage == 0, axis=0))[0]
row_start,row_end = 0,height-1
if len(black_rows) > 0:
row_start, row_end = min(black_rows),max(black_rows)
col_start,col_end = 0,width-1
if len(black_cols) > 0:
col_start, col_end = min(black_cols),max(black_cols)
s = cv2.resize(symbolImage[row_start:row_end+1,col_start:col_end+1],(16,16))
return meta.otsu_threshold(s)
if __name__ == "__main__":
trainDir = os.listdir(meta.getRootDir() + '/images/labelled_symbol/')
trainDict = {}
for symbolFile in trainDir:
print symbolFile
image = cv2.imread(meta.getRootDir() + '/images/labelled_symbol/'+symbolFile, cv2.IMREAD_GRAYSCALE)
scaledImage = scaleSymbol(image)
meta.saveImage(scaledImage,"output.jpeg")
trainDict[symbolFile[:-5]] = scaledImage
print trainDict.keys()
'''
gray_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
trainDict = getTrainDict()
testSymbols = getTestSymbols(color_image)
testResults = []
count = 0
for symbol in testSymbols:
gray_symbol = gray_image[symbol.colst:symbol.colen,
symbol.rowst:symbol.rowen]
scaledsymbol = part3.scaleSymbol(gray_symbol)
count = count + 1
ans = trainDict.keys()[1]
anscount = len(np.where(scaledsymbol == trainDict[ans]))
for trainedSymbol in trainDict.keys():
matchcount = np.sum(scaledsymbol == trainDict[trainedSymbol])
if matchcount > anscount:
ans, anscount = trainedSymbol, matchcount
testResults.append(ans)
#meta.saveImage(scaledsymbol, ans+str(count)+"kafi.jpeg")
return testResults
if __name__ == "__main__":
color_image = cv2.imread(meta.getRootDir() + '/images/kafi.jpeg',
cv2.IMREAD_COLOR)
testResults = main_first(color_image)
# for i in range(len(testResults)):
# if testResults[i] == 'hy' and i>0:
# testResults[i] = testResults[i-1]
print testResults
part4.keyMapping(testResults, 'kafi')