# The cameramanΒΆ

Download the file `camera.txt`

and save it in your working
directory. It contains the pixel values for a picture, and it’s your
job to find what picture that is. The file contains a single floating
point value per line. Here are the first four lines of the file:

```
0.6118
0.6118
0.6196
0.6275
```

You will first need to read in the file as a list of floating point values.

Start at the IPython console. We recommend you begin your IPython console session with these standard lines:

```
# Compatibility with Python 3
from __future__ import print_function, division
# Interactive graphs for matplotlib at the IPython prompt
%matplotlib
# Standard imports of libraries
import numpy as np
import matplotlib.pyplot as plt
```

To read in the file, you probably want to start something like this:

`pixel_values = [] # empty list`

Now:

```
# Read lines from file and convert to list of floats
```

How many pixel values does this image contain? (Call this number
`P`

).

The array that forms the image is two-dimensional (it has a number of
rows `M`

and a number of columns `N`

). If we got the right number
of pixel values then `M * N == P`

. That is, `M`

and `N`

have to
be *factors* of `P`

(the number of pixel values).

We don’t know what `M`

and `N`

are, we will have to guess. Given
the number of pixel values, what would your guess be for `M`

and
`N`

?

```
# Guess M, N
```

Next we want to convert the list of pixel values to an array (that forms the image).

Convert the `pixel_values`

list to a pixel values array, and reshape
it to your guess for the shape `(M, N)`

.

```
# Convert list to array and reshape
```

Show this as an image using matplotlib’s `plt`

module:

```
# Show image using plt module
```

Is this the right shape already?

What do we need to put it into the right orientation on the plot?

How can we display the image in grayscale? Hint - remember `plt.cm`

.

```
# A nicer version of the original plot
```

Now let’s say we want to binarize this image at some threshold `T`

,
so the man’s coat and hair and the camera tripod are black (pixel
value of 0) and most everything else is white (pixel value of 1). How
would we choose a good threshold? One step might be to plot a line out
of the array (image) to get an idea of the values on that line.

```
# A plot of the pixel position in x and the pixel value in y, for an image line.
```

Now you need to apply your threshold to the image to make a binary image where values less than the threshold are == 0 and greater than the threshold are == 1. You might want to play with the threshold a little to get a good result.

```
# Apply threshold to make new binary image, and show binary image
```

For extra points - the camera guy has a pocket on side of his coat,
that you can’t see well at the moment, because the range ov values is
too large to distinguish the different dark levels on his coat. Can
you make a good picture to show the pocket? Hint: you might want to
explore the `np.clip`

function.

```
# Extra points - a good image of the man's pocket.
```