print('cvt color %f' % (time.time() - start_time))
# 切り出し
img_two = img_two[300:600, 200:600]
# ２倍に拡大
img_two = cv2.resize(img_two, None, fx=2, fy=2)
# 認識
detectionResults = detectMusic.yoloDetect_net(img_two, net)
print('detection %f' % (time.time() - start_time))
#detectionResults = detectMusic.yoloDetect(img_two)
print("detectionResults", detectionResults)
# 認識結果を楽譜形式に変換
currentNote = imageYOLO.makeSound(detectionResults)
print('currentNote %f' % (time.time() - start_time))
print("currentNote", currentNote)
# 認識結果を表示
detectMusic.writeBoundingBox(detectionResults, img_two, i)
print('bounding box %f' % (time.time() - start_time))
'''
if not detectionResults:
# 認識結果が空なら終了
# move.stop()
break
'''
# 新規楽譜の抽出
newNote = imageYOLO.findNewNotes(currentNote, musicList)
print('newNote %f' % (time.time() - start_time))
# 新規楽譜を結合
musicList.extend(newNote)
#楽譜をev3に送信する
#if newNote != []:
# postData.postMusic(newNote)
# 切り出し
img_two = img_two[300:600, 200:600]
# ２倍に拡大
img_two = cv2.resize(img_two, None, fx = 2, fy = 2)
# 認識
detectionResults = detectMusic.yoloDetect_net(img_two, net)
# detectionResults = [(b'u_do', 0.9993329048156738, (224.17959594726562, 378.47900390625, 178.75448608398438, 328.29620361328125))] # test
print('detection %f' % (time.time()-start_time))
#detectionResults = detectMusic.yoloDetect(img_two)
print("detectionResults", detectionResults)
# 認識結果を楽譜形式に変換
currentNote = imageYOLO.makeSound(detectionResults)
print('currentNote %f' % (time.time()-start_time))
print("currentNote", currentNote)
# 認識結果を表示
detectMusic.writeBoundingBox(detectionResults, img_two, int(imageNum))
#print('bounding box %f' % (time.time() - start_time))
# if not detectionResults:
# # 認識結果が空なら終了
# move.stop()
# break
# 新規楽譜の抽出
newNote = imageYOLO.findNewNotes(currentNote, musicList)
print('newNote %f' % (time.time()-start_time))
# 新規楽譜を結合
musicList.extend(newNote)
#楽譜をev3に送信する
if newNote != []:
#postData.postMusic(newNote)
pass