def run(self):
global Prediction_Flag, Prediction_Perfect, exitFlag
while exitFlag:
if Prediction_Flag:
print("Strat Prediction")
for i in range(10):
threadLock.acquire()
ret_1, frame_1 = cap.read()
threadLock.release()
time.sleep(0.1)
if ret_1:
frame_1 = cv2.flip(frame_1, 1) # 第二个参数大于0：就表示是沿y轴翻转
Box = dection.binaryMask(frame_1, dection.x0,
dection.y0, 300, 300)
Box_P = cv2.resize(Box,
(dection.width, dection.height),
interpolation=cv2.INTER_CUBIC)
Box_P = np.reshape(Box_P,
[dection.width, dection.height, 1])
Gesture.append(CNN.Gussgesture(Box_P))
else:
print('Predictive thread read failed, unpredictable！')
Prediction_Perfect = True
Prediction_Flag = False
key = cv2.waitKey(5) & 0xFF
if (key == 27):
break
cv2.destroyWindow('Thread_box')
def Main():
global x0, y0, binaryMode, saveImg, gesturename, banner, guessGesturees, path
# 选择模式
while (True):
ans = int(input(banner))
if (ans == 1): # 训练模型 并保存
#mod = CNN.load()
print("········开始训练模型········")
CNN.TRAIN()
input("按任意键继续")
break
if (ans == 2):
print("········载入模型·······")
break
else:
print("········输入有误!········")
return 0
#打开摄像头
cap = cv2.VideoCapture(0)
while (True):
#一帧一帧的捕捉视频
ret, frame = cap.read()
frame = cv2.flip(frame, 2) # 图像翻转 如果不翻转，视频中看起来的刚好和我们是左右对称的
roi = binaryMask(frame, x0, y0, width, height)
cv2.putText(frame, "select: ", (fx, fy), font, size,
(0, 255, 0)) # 标注字体
cv2.putText(frame, "b-'Binary Model'/ r- 'RGB Model' ", (fx, fy + fh),
font, size, (0, 255, 0)) # 标注字体
cv2.putText(frame, "p-'predict Model", (fx, fy + 2 * fh), font, size,
(0, 255, 0)) # 标注字体
cv2.putText(frame, "q-'quit'", (fx, fy + 3 * fh), font, size,
(0, 255, 0)) # 标注字体
key = cv2.waitKey(1) & 0xFF
if key == ord('b'):
"""切换到二值模式"""
binaryMode = True
print("已切换到二值模式")
elif key == ord('r'):
"""切换到RGB模式"""
binaryMode = False
print("已切换到RGB模式……")
if key == ord('p'):
"""调用模型开始预测, 对二值图像预测，所以要在二值函数里面调用，预测新采集的手势"""
print()
Roi = np.reshape(roi, [width, height, 1])
gesture = CNN.Gussgesture(Roi)
gesture_copy = gesture
cv2.putText(frame, gesture_copy, (480, 440), font, 1,
(0, 0, 255)) # 标注字体
if key == ord('q'):
break
#展示处理之后的视频帧
cv2.imshow('frame', frame)
if (binaryMode):
cv2.imshow('ROI', roi)
else:
cv2.imshow("ROI", frame[y0:y0 + height, x0:x0 + width])
#释放捕捉
cap.release()
cv2.destroyAllWindows()
