diff --git a/02-image-basics.html b/02-image-basics.html index 3371ea2d..74a89aaf 100644 --- a/02-image-basics.html +++ b/02-image-basics.html @@ -536,7 +536,7 @@
In the example above, form 1 loads the entire scikit-image library into the program as an object. Individual modules of the library are @@ -709,7 +709,7 @@
There are many possible solutions, but one method would be . . .
There are 24 total bits in an RGB colour of this type, and each bit can be on or off, and so there are 224 = 16,777,216 possible @@ -1134,7 +1134,7 @@
In such an image, there are 5,000 × 5,000 = 25,000,000 pixels, and 24 bits for each pixel, leading to 25,000,000 × 24 = 600,000,000 bits, or @@ -1237,7 +1237,7 @@
The BMP file, ws.bmp
, is 75,000,054 bytes, which matches
our prediction very nicely. The JPEG file, ws.jpg
, is
@@ -1283,7 +1283,7 @@
Here is a partial directory listing, showing the sizes of the relevant files there:
diff --git a/03-skimage-images.html b/03-skimage-images.html index 4b509e3a..b116d7cb 100644 --- a/03-skimage-images.html +++ b/03-skimage-images.html @@ -528,7 +528,7 @@Here is what your Python script might look like.
First, load the image file data/sudoku.png
as a
grayscale image. Note we may want to create a copy of the image array to
@@ -920,7 +920,7 @@
Here is the completed Python program to select only the plant and roots in the image.
diff --git a/04-drawing.html b/04-drawing.html index 5332494b..b276eaeb 100644 --- a/04-drawing.html +++ b/04-drawing.html @@ -519,7 +519,7 @@Drawing a circle:
When indexing the image using the mask, we access only those pixels
at positions where the mask is True
. So, when indexing with
@@ -720,7 +720,7 @@
Here is a Python program to produce the cropped remote control image shown above. Of course, your program should be tailored to your @@ -795,7 +795,7 @@
Here is a Python program that is able to create the masked image
without having to read in the centers.txt
file.
Generally speaking, the larger the sigma value, the more blurry the result. A larger sigma will tend to get rid of more noise in the image, @@ -766,7 +766,7 @@
First, let’s work through the process for one image:
Here is a modified function with the requested features. Note when calculating the Otsu threshold we don’t include the very bright pixels diff --git a/aio.html b/aio.html index 46c96a2d..3f4e4d6d 100644 --- a/aio.html +++ b/aio.html @@ -722,7 +722,7 @@
In the example above, form 1 loads the entire scikit-image library into the program as an object. Individual modules of the library are @@ -895,7 +895,7 @@
There are many possible solutions, but one method would be . . .
There are 24 total bits in an RGB colour of this type, and each bit can be on or off, and so there are 224 = 16,777,216 possible @@ -1356,7 +1356,7 @@
In such an image, there are 5,000 × 5,000 = 25,000,000 pixels, and 24 bits for each pixel, leading to 25,000,000 × 24 = 600,000,000 bits, or @@ -1462,7 +1462,7 @@
The BMP file, ws.bmp
, is 75,000,054 bytes, which matches
our prediction very nicely. The JPEG file, ws.jpg
, is
@@ -1508,7 +1508,7 @@
Here is a partial directory listing, showing the sizes of the relevant files there:
@@ -1966,7 +1966,7 @@Here is what your Python script might look like.
First, load the image file data/sudoku.png
as a
grayscale image. Note we may want to create a copy of the image array to
@@ -2361,7 +2361,7 @@
Here is the completed Python program to select only the plant and roots in the image.
@@ -2647,7 +2647,7 @@Drawing a circle:
When indexing the image using the mask, we access only those pixels
at positions where the mask is True
. So, when indexing with
@@ -2850,7 +2850,7 @@
Here is a Python program to produce the cropped remote control image shown above. Of course, your program should be tailored to your @@ -2925,7 +2925,7 @@
Here is a Python program that is able to create the masked image
without having to read in the centers.txt
file.
Generally speaking, the larger the sigma value, the more blurry the result. A larger sigma will tend to get rid of more noise in the image, @@ -4014,7 +4014,7 @@
First, let’s work through the process for one image:
Here is a modified function with the requested features. Note when calculating the Otsu threshold we don’t include the very bright pixels diff --git a/instructor/02-image-basics.html b/instructor/02-image-basics.html index 6c46cf35..688db79a 100644 --- a/instructor/02-image-basics.html +++ b/instructor/02-image-basics.html @@ -538,7 +538,7 @@
In the example above, form 1 loads the entire scikit-image library into the program as an object. Individual modules of the library are @@ -711,7 +711,7 @@
There are many possible solutions, but one method would be . . .
There are 24 total bits in an RGB colour of this type, and each bit can be on or off, and so there are 224 = 16,777,216 possible @@ -1136,7 +1136,7 @@
In such an image, there are 5,000 × 5,000 = 25,000,000 pixels, and 24 bits for each pixel, leading to 25,000,000 × 24 = 600,000,000 bits, or @@ -1239,7 +1239,7 @@
The BMP file, ws.bmp
, is 75,000,054 bytes, which matches
our prediction very nicely. The JPEG file, ws.jpg
, is
@@ -1285,7 +1285,7 @@
Here is a partial directory listing, showing the sizes of the relevant files there:
diff --git a/instructor/03-skimage-images.html b/instructor/03-skimage-images.html index 2eb46a04..669bbe76 100644 --- a/instructor/03-skimage-images.html +++ b/instructor/03-skimage-images.html @@ -530,7 +530,7 @@