video_fps = 25.0
if mode == "predict":
#该代码无法直接进行批量预测，如果想要批量预测，可以利用os.listdir()遍历文件夹，利用Image.open打开图片文件进行预测。
#如果想要保存，利用r_image.save("img.jpg")即可保存。
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = unet.detect_image(image)
r_image.show()
elif mode == "video":
capture = cv2.VideoCapture(video_path)
if video_save_path != "":
fourcc = cv2.VideoWriter_fourcc(*'XVID')
size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size)
fps = 0.0
while (True):
t1 = time.time()
# 读取某一帧
ref, frame = capture.read()
'''
predict.py有几个注意点
1、无法进行批量预测，如果想要批量预测，可以利用os.listdir()遍历文件夹，利用Image.open打开图片文件进行预测。
2、如果想要保存，利用r_image.save("img.jpg")即可保存。
3、如果想要原图和分割图不混合，可以把blend参数设置成False。
4、如果想根据mask获取对应的区域，可以参考detect_image中，利用预测结果绘图的部分。
seg_img = np.zeros((np.shape(pr)[0],np.shape(pr)[1],3))
for c in range(self.num_classes):
seg_img[:, :, 0] += ((pr == c)*( self.colors[c][0] )).astype('uint8')
seg_img[:, :, 1] += ((pr == c)*( self.colors[c][1] )).astype('uint8')
seg_img[:, :, 2] += ((pr == c)*( self.colors[c][2] )).astype('uint8')
'''
from PIL import Image
from unet import Unet
unet = Unet()
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = unet.detect_image(image)
r_image.show()
# 调用摄像头
# capture=cv2.VideoCapture("1.mp4")
#-------------------------------------#
capture = cv2.VideoCapture(0)
fps = 0.0
while (True):
t1 = time.time()
# 读取某一帧
ref, frame = capture.read()
# 格式转变，BGRtoRGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 转变成Image
frame = Image.fromarray(np.uint8(frame))
# 进行检测
frame = np.array(unet.detect_image(frame))
# RGBtoBGR满足opencv显示格式
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
fps = (fps + (1. / (time.time() - t1))) / 2
print("fps= %.2f" % (fps))
frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow("video", frame)
c = cv2.waitKey(30) & 0xff
if c == 27:
capture.release()
break
