fix figures in augmentation how-to

Author: lorenzoh

docs/howto/augmentvision.md

@@ -3,45 +3,30 @@
 Data augmentation is important to train models with good generalization ability, especially when the size of your dataset is limited. FastAI.jl gives you high-level helpers to use data augmentation in vision learning methods, but also allows directly using [DataAugmentation.jl](https://github.com/lorenzoh/DataAugmentation.jl), the underlying data augmentation library.
 
 By default, the only augmentation that will be used in computer vision tasks is a random crop, meaning that after images, keypoints and masks are resized to a similar size a random portion will be cropped during training. We can demonstrate this on the image classification task.
-{cell=main output=false result=false style="display:none;"}
-```julia
-using Images: load
-function showfig(f)
-    save("fig.png", f)
-    load("fig.png")
-end
-```
 
 {cell=main result=false output=false}
 ```julia
 using FastAI
-using CairoMakie
+using CairoMakie; CairoMakie.activate!(type="png")
 
 dir = joinpath(datasetpath("dogscats"), "train")
 data = loadtaskdata(dir, ImageClassificationTask)
 classes = Datasets.getclassesclassification(dir)
 method = ImageClassification(classes, (100, 128))
 xs, ys = FastAI.makebatch(method, data, fill(4, 9))
-f = FastAI.plotbatch(method, xs, ys)
-```
-{cell=main output=false style="display:none;"}
-```julia
-showfig(f)
+FastAI.plotbatch(method, xs, ys)
 ```
 
 
 Most learning methods let you pass additional augmentations as keyword arguments. For example, `ImageClassification` takes the `aug_projection` and `aug_image` arguments. FastAI.jl provides the [`augs_projection`](#) helper to quickly construct a set of projective data augmentations.
 
-{cell=main result=false}
+{cell=main}
 ```julia
 method2 = ImageClassification(classes, (100, 128), aug_projection=augs_projection())
 xs2, ys2 = FastAI.makebatch(method2, data, fill(4, 9))
 f = FastAI.plotbatch(method2, xs2, ys2)
 ```
-{cell=main output=false style="display:none;"}
-```julia
-showfig(f)
-```
+
 
 Likewise, there is an [`augs_lighting`](#) helper that adds contrast and brightness augmentation:
 
@@ -51,7 +36,7 @@ method3 = ImageClassification(
     classes, (100, 128),
     aug_projection=augs_projection(), aug_image=augs_lighting())
 xs3, ys3 = FastAI.makebatch(method3, data, fill(4, 9))
-f = FastAI.plotbatch(method3, xs3, ys3)
+FastAI.plotbatch(method3, xs3, ys3)
 ```
 
 ## Augmentation in custom learning methods