images

01_intro.ipynb

@@ -660,7 +660,7 @@
     }
    ],
    "source": [
-    "img = PILImage.create('images/chapter1_cat_example.jpg')\n",
+    "img = PILImage.create(image_cat())\n",
     "img.to_thumb(192)"
    ]
   },
@@ -730,7 +730,7 @@
    "source": [
     "#hide\n",
     "# For the book, we can't actually click an upload button, so we fake it\n",
-    "uploader = SimpleNamespace(data = ['images/chapter1_cat_example.jpg'])"
+    "# uploader = SimpleNamespace(data = ['images/chapter1_cat_example.jpg'])"
    ]
   },
   {

13_convolutions.ipynb

@@ -2570,7 +2570,7 @@
     }
    ],
    "source": [
-    "im = image2tensor(Image.open('images/grizzly.jpg'))\n",
+    "im = image2tensor(Image.open(image_bear()))\n",
     "im.shape"
    ]
   },

18_CAM.ipynb

@@ -158,7 +158,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "img = PILImage.create('images/chapter1_cat_example.jpg')\n",
+    "img = PILImage.create(image_cat())\n",
     "x, = first(dls.test_dl([img]))"
    ]
   },

clean/01_intro.ipynb

@@ -133,7 +133,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "img = PILImage.create('images/chapter1_cat_example.jpg')\n",
+    "img = PILImage.create(image_cat())\n",
     "img.to_thumb(192)"
    ]
   },
@@ -162,7 +162,7 @@
    "source": [
     "#hide\n",
     "# For the book, we can't actually click an upload button, so we fake it\n",
-    "uploader = SimpleNamespace(data = ['images/chapter1_cat_example.jpg'])"
+    "# uploader = SimpleNamespace(data = ['images/chapter1_cat_example.jpg'])"
    ]
   },
   {

clean/13_convolutions.ipynb

@@ -472,7 +472,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "im = image2tensor(Image.open('images/grizzly.jpg'))\n",
+    "im = image2tensor(Image.open(image_bear()))\n",
     "im.shape"
    ]
   },

clean/18_CAM.ipynb

@@ -57,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "img = PILImage.create('images/chapter1_cat_example.jpg')\n",
+    "img = PILImage.create(image_cat())\n",
     "x, = first(dls.test_dl([img]))"
    ]
   },

clean/app_jupyter.ipynb

@@ -7,11 +7,20 @@
    "outputs": [],
    "source": [
     "#hide\n",
-    "!pip install -Uqq fastbook\n",
+    "# !pip install -Uqq fastbook\n",
     "import fastbook\n",
     "fastbook.setup_book()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastbook import *"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -149,7 +158,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Image.open('images/chapter1_cat_example.jpg')"
+    "Image.open(image_cat())"
    ]
   },
   {

clean/utils.py

@@ -1,64 +0,0 @@
-# Numpy and pandas by default assume a narrow screen - this fixes that
-from fastai.vision.all import *
-from nbdev.showdoc import *
-from ipywidgets import widgets
-from pandas.api.types import CategoricalDtype
-
-import matplotlib as mpl
-# mpl.rcParams['figure.dpi']= 200
-mpl.rcParams['savefig.dpi']= 200
-mpl.rcParams['font.size']=12
-
-set_seed(42)
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
-pd.set_option('display.max_columns',999)
-np.set_printoptions(linewidth=200)
-torch.set_printoptions(linewidth=200)
-
-import graphviz
-def gv(s): return graphviz.Source('digraph G{ rankdir="LR"' + s + '; }')
-
-def get_image_files_sorted(path, recurse=True, folders=None): return get_image_files(path, recurse, folders).sorted()
-
-
-# +
-# pip install azure-cognitiveservices-search-imagesearch
-
-from azure.cognitiveservices.search.imagesearch import ImageSearchClient as api
-from msrest.authentication import CognitiveServicesCredentials as auth
-
-def search_images_bing(key, term, min_sz=128):
-    client = api('https://api.cognitive.microsoft.com', auth(key))
-    return L(client.images.search(query=term, count=150, min_height=min_sz, min_width=min_sz).value)
-
-
-# -
-
-def plot_function(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
-    x = torch.linspace(min,max)
-    fig,ax = plt.subplots(figsize=figsize)
-    ax.plot(x,f(x))
-    if tx is not None: ax.set_xlabel(tx)
-    if ty is not None: ax.set_ylabel(ty)
-    if title is not None: ax.set_title(title)
-
-# +
-from sklearn.tree import export_graphviz
-
-def draw_tree(t, df, size=10, ratio=0.6, precision=0, **kwargs):
-    s=export_graphviz(t, out_file=None, feature_names=df.columns, filled=True, rounded=True,
-                      special_characters=True, rotate=False, precision=precision, **kwargs)
-    return graphviz.Source(re.sub('Tree {', f'Tree {{ size={size}; ratio={ratio}', s))
-
-
-# +
-from scipy.cluster import hierarchy as hc
-
-def cluster_columns(df, figsize=(10,6), font_size=12):
-    corr = np.round(scipy.stats.spearmanr(df).correlation, 4)
-    corr_condensed = hc.distance.squareform(1-corr)
-    z = hc.linkage(corr_condensed, method='average')
-    fig = plt.figure(figsize=figsize)
-    hc.dendrogram(z, labels=df.columns, orientation='left', leaf_font_size=font_size)
-    plt.show()