如何从 unet_learner (fastai) 获得正确的输出预测?
How to get a correct output predictions from unet_learner (fastai)?
拜托,我正在做一个图像分割项目,我使用了 fastai 库(特别是 unet_learner)。我已经训练了我的模型,一切都很好这是我的代码(在训练阶段):
#codes = np.loadtxt('codes.txt', dtype=str)
codes = np.array(['bg', 'edge'], dtype='<U4')# bg= background
get_y_fn = lambda x: path_lbl/f'{x.stem}{x.suffix}'
# fastai codes
data = (SegmentationItemList.from_folder(path_img)
.split_by_rand_pct()
.label_from_func(get_y_fn, classes=codes)
#.add_test_folder()
#.transform(get_transforms(), tfm_y=True, size=384)
.databunch(bs=2,path=dataset) # bs = mimi-patch size
.normalize(imagenet_stats))
learn = unet_learner(data, models.resnet34, wd=1e-2)
learn.lr_find() # find learning rate
learn.recorder.plot() # plot learning rate graph
lr = 1e-02 # pick a lr
learn.fit_one_cycle(3, slice(lr), pct_start=0.3) # train model ---- epochs=3
learn.unfreeze() # unfreeze all layers
# find and plot lr again
learn.lr_find()
learn.recorder.plot()
learn.fit_one_cycle(10, slice(lr/400, lr/4), pct_start=0.3)
learn.save('model-stage-1') # save model
learn.load('model-stage-1');
learn.export()
我的问题是当我尝试使用经过训练的模型进行预测时,输出始终是黑色图像。下面是预测阶段的代码:
img = open_image('/content/generated_samples_masks/545.png')
prediction = learn.predict(img)
prediction[0].show(figsize=(8,8))
请问关于如何解决此问题的任何想法?谢谢
我觉得预测还可以。你期待这样的事情吗?
此结果基于您发布的预测图像。
要检查事情进展如何,试试这个:
interp = SegmentationInterpretation.from_learner(learn)
mean_cm, single_img_cm = interp._generate_confusion()
df = interp._plot_intersect_cm(mean_cm, "Mean of Ratio of Intersection given
True Label")
i = 0 #Some image index
df = interp._plot_intersect_cm(single_img_cm[i], f"Ratio of Intersection given True Label, Image:{i}")
interp.show_xyz(i)
关于您的预测结果,它是基于您的 类 值的图像。如果从该图像中获取 (r,g,b) 值,则背景为 (r, g, b) == 0
,边缘为 (r, g, b) == 1
。如果你有更多的 类,下一个将是 (r, g, b) == 2
等等。
因此,您只需为预测结果着色即可。我是用 OpenCV 做的,像这样:
frame = cv2.imread("yourPredictionHere.png",1)
frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
for x in range(384): #width based on the size of your image.
for y in range(384): #height based on the size of your image.
b, g, r = frame[x, y]
if (b, g, r) == (0,0,0): #background
frame[x, y] = (0,0,0)
elif (b, g, r) == (1,1,1): #edges
frame[x, y] = (85,85,255)
cv2.imwrite("result.png",frame)
此致!
要查看叠加在原始图像上的 unet_learner
的预测,您可以执行以下操作:
img = open_image("your_test_image.png")
prediction = learn.predict(img)
img.show(y=prediction[0])
这是 fastai 文档中的示例:https://docs.fast.ai/tutorial.inference.html#A-segmentation-example。
拜托,我正在做一个图像分割项目,我使用了 fastai 库(特别是 unet_learner)。我已经训练了我的模型,一切都很好这是我的代码(在训练阶段):
#codes = np.loadtxt('codes.txt', dtype=str)
codes = np.array(['bg', 'edge'], dtype='<U4')# bg= background
get_y_fn = lambda x: path_lbl/f'{x.stem}{x.suffix}'
# fastai codes
data = (SegmentationItemList.from_folder(path_img)
.split_by_rand_pct()
.label_from_func(get_y_fn, classes=codes)
#.add_test_folder()
#.transform(get_transforms(), tfm_y=True, size=384)
.databunch(bs=2,path=dataset) # bs = mimi-patch size
.normalize(imagenet_stats))
learn = unet_learner(data, models.resnet34, wd=1e-2)
learn.lr_find() # find learning rate
learn.recorder.plot() # plot learning rate graph
lr = 1e-02 # pick a lr
learn.fit_one_cycle(3, slice(lr), pct_start=0.3) # train model ---- epochs=3
learn.unfreeze() # unfreeze all layers
# find and plot lr again
learn.lr_find()
learn.recorder.plot()
learn.fit_one_cycle(10, slice(lr/400, lr/4), pct_start=0.3)
learn.save('model-stage-1') # save model
learn.load('model-stage-1');
learn.export()
我的问题是当我尝试使用经过训练的模型进行预测时,输出始终是黑色图像。下面是预测阶段的代码:
img = open_image('/content/generated_samples_masks/545.png')
prediction = learn.predict(img)
prediction[0].show(figsize=(8,8))
请问关于如何解决此问题的任何想法?谢谢
我觉得预测还可以。你期待这样的事情吗?
此结果基于您发布的预测图像。
要检查事情进展如何,试试这个:
interp = SegmentationInterpretation.from_learner(learn)
mean_cm, single_img_cm = interp._generate_confusion()
df = interp._plot_intersect_cm(mean_cm, "Mean of Ratio of Intersection given
True Label")
i = 0 #Some image index
df = interp._plot_intersect_cm(single_img_cm[i], f"Ratio of Intersection given True Label, Image:{i}")
interp.show_xyz(i)
关于您的预测结果,它是基于您的 类 值的图像。如果从该图像中获取 (r,g,b) 值,则背景为 (r, g, b) == 0
,边缘为 (r, g, b) == 1
。如果你有更多的 类,下一个将是 (r, g, b) == 2
等等。
因此,您只需为预测结果着色即可。我是用 OpenCV 做的,像这样:
frame = cv2.imread("yourPredictionHere.png",1)
frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
for x in range(384): #width based on the size of your image.
for y in range(384): #height based on the size of your image.
b, g, r = frame[x, y]
if (b, g, r) == (0,0,0): #background
frame[x, y] = (0,0,0)
elif (b, g, r) == (1,1,1): #edges
frame[x, y] = (85,85,255)
cv2.imwrite("result.png",frame)
此致!
要查看叠加在原始图像上的 unet_learner
的预测,您可以执行以下操作:
img = open_image("your_test_image.png")
prediction = learn.predict(img)
img.show(y=prediction[0])
这是 fastai 文档中的示例:https://docs.fast.ai/tutorial.inference.html#A-segmentation-example。