def predict_ann(self):
filename = tkFileDialog.askopenfilename(initialdir='images/')
image = cv2.imread(filename)
if image is None:
return
image_bin = self.prepare_image(image)
groups = self.get_groups(image)
image_orig,selected_regions, positions,reg_details = imgFunctions.select_roi(image.copy(), image_bin, groups)
self.inputs = ann_fun.prepare_for_ann(selected_regions)
self.outputs = ann_fun.convert_output(self.alphabet)
self.sheet = np_fun.get_notes(image)
with open('saved_ann/trained_ann.pkl', 'rb') as input:
self.ann = pickle.load(input)
results = self.ann.predict(np.array(self.inputs, np.float32))
print 'results = ',results
results = ann_fun.display_result(results, self.alphabet)
results = self.replace_beams(results)
print results
self.chords = self.sheet.set_chords_duration(results[2:])
tkMessageBox.showinfo("Done", "Sheet loaded. Click on Play Sheet button.")
img_fun.show_image('Sheet',image)
def train_ann(self):
image = cv2.imread("images/train.png")
image_bin = self.prepare_image(image)
groups = self.get_groups(image)
image_orig,selected_regions, positions,reg_details = imgFunctions.select_roi(image.copy(), image_bin,groups)
# cv2.imshow('bin',image_bin)
# cv2.waitKey(0)
#
# cv2.imshow('bin',image_orig)
# cv2.waitKey(0)
inputs = ann_fun.prepare_for_ann(selected_regions)
outputs = ann_fun.convert_output(self.alphabet)
# notes = np_fun.get_notes(image)
self.ann = ann_fun.create_ann(len(self.alphabet))
self.ann = ann_fun.train_ann(self.ann, inputs, outputs)
saver = se.Serialize('trained_ann.pkl',self.ann)
saver.save()
results = self.ann.predict(np.array(inputs, np.float32))
results = ann_fun.display_result(results, self.alphabet)
self.progressbar.stop()
self.label["text"] = "Done"
self.label["foreground"] = '#437C17'
tkMessageBox.showinfo("Info", "Done!")
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)
alphabet = ["g-key", "time-sig-4/4", "1", "2", "4", "8"]
inputs = ann_fun.prepare_for_ann(selected_regions)
outputs = ann_fun.convert_output(alphabet)
notes = np_fun.get_notes(image)
"""
for i in range(len(notes)):
notes[i].print_note()
sgen.playNote(notes[i].frequency, notes[i].duration)
"""
# ann = ann_fun.create_ann()
# ann = ann_fun.train_ann(ann, inputs, outputs)
print "done"
# results = ann.predict(np.array(inputs, np.float32))
# results = ann_fun.display_result(results,alphabet)
