502 lines
472 KiB
Plaintext
502 lines
472 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# You must run this cell, but you can ignore its contents.\n",
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"\n",
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"import hashlib\n",
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"\n",
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"def ads_hash(ty):\n",
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" \"\"\"Return a unique string for input\"\"\"\n",
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" ty_str = str(ty).encode()\n",
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" m = hashlib.sha256()\n",
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" m.update(ty_str)\n",
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" return m.hexdigest()[:10]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Numpy array for images"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"nbgrader": {
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"grade": false,
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"grade_id": "cell-6a738215de5622a5",
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"locked": true,
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"schema_version": 3,
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"solution": false
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Images are saved in computers as arrays of numbers. In Python, the library called `numpy` (often abbreviated `np` for short). is the most common way to manipulate arrays of numbers. Here we will create an 8x8 pixel image and put it in the variable `check`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0., 1., 0., 1., 0., 1., 0., 1.],\n",
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" [1., 0., 1., 0., 1., 0., 1., 0.],\n",
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" [0., 1., 0., 1., 0., 1., 0., 1.],\n",
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" [1., 0., 1., 0., 1., 0., 1., 0.],\n",
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" [0., 1., 0., 1., 0., 1., 0., 1.],\n",
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" [1., 0., 1., 0., 1., 0., 1., 0.],\n",
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" [0., 1., 0., 1., 0., 1., 0., 1.],\n",
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" [1., 0., 1., 0., 1., 0., 1., 0.]])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Create a variable called `check` which will contain an 8x8 array of numbers.\n",
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"check = np.zeros((8, 8))\n",
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"check[::2, 1::2] = 1\n",
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"check[1::2, ::2] = 1\n",
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"check"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Now lets view our 8x8 pixel image:\n",
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"plt.imshow(check, cmap='gray');"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Questions Part A - Image representation\n",
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"\n",
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"Use numpy slicing to set the values in an array:\n",
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"\n",
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"- Use `plt.imshow` to show a new 8x8 pixel image in which the top half is white and the bottom half is black.\n",
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"- Use `plt.imshow` to show a new 8x8 pixel image in which the left half is white and the right half is black."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"nbgrader": {
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"grade": true,
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"grade_id": "cell-4ee9d19bc490e8bf",
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"locked": false,
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"points": 1,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# The top half is white and the bottom half is black.\n",
|
||
|
|
"arr = np.zeros((8,8))\n",
|
||
|
|
"arr[:4,:] = 1\n",
|
||
|
|
"plt.imshow(arr, cmap='gray');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-9d1baa0ef55db4c6",
|
||
|
|
"locked": false,
|
||
|
|
"points": 1,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# The left half is white and the right half is black.\n",
|
||
|
|
"arr = np.zeros((8,8))\n",
|
||
|
|
"arr[:,:4] = 1\n",
|
||
|
|
"plt.imshow(arr, cmap='gray');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Questions Part B - More image representation\n",
|
||
|
|
"\n",
|
||
|
|
"Write the numerical value corresponding with each pixel. In other words, what number corresponds with the black color and which number corresponds with the white color?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-2fd29ad721f075e0",
|
||
|
|
"locked": false,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Type your answer here and then run this and the following cell.\n",
|
||
|
|
"black = 'replace this with your answer'\n",
|
||
|
|
"white = 'replace this with your answer'\n",
|
||
|
|
"\n",
|
||
|
|
"black = 0\n",
|
||
|
|
"white = 1"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-4f11d45d5fbbe454",
|
||
|
|
"locked": true,
|
||
|
|
"points": 1,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# If this runs without error, it means the answer in your previous cell was correct.\n",
|
||
|
|
"assert ads_hash(float(black))=='8aed642bf5'\n",
|
||
|
|
"assert ads_hash(float(white))=='d0ff5974b6'"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Questions Part C - More image representation and contrast enhancement\n",
|
||
|
|
"\n",
|
||
|
|
"Now let's look at a more interesting image. We will use data included with the library `skimage` (also called scikit-image). The homepage for scikit-image is [here](http://scikit-image.org/)."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 400x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# If import skimage fails, you can install it like this in Jupyter:\n",
|
||
|
|
"# !pip install scikit-image\n",
|
||
|
|
"# or like this at the command line:\n",
|
||
|
|
"# python -m pip install scikit-image\n",
|
||
|
|
"from skimage import data\n",
|
||
|
|
"\n",
|
||
|
|
"# Load the sample image data into a variable called `camera`.\n",
|
||
|
|
"camera = data.camera()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(4, 4))\n",
|
||
|
|
"plt.imshow(camera, cmap='gray', interpolation='nearest', vmin=0, vmax=255)\n",
|
||
|
|
"plt.axis('off')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"So, let's checkout some things about the image. How many pixels are here? Put your answer in the `num_pixels` variable."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-35aec8ec167eaa1a",
|
||
|
|
"locked": false,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Type your answer here and then run this and the following cell.\n",
|
||
|
|
"num_pixels = camera.shape[0]*camera.shape[1]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-9eda0f4283380a85",
|
||
|
|
"locked": true,
|
||
|
|
"points": 1,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# If this runs without error, it means the answer in your previous cell was correct.\n",
|
||
|
|
"assert ads_hash(num_pixels)=='54faea9b3e'"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"What is the distribution of luminance values in the image?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.hist(camera.flat,bins=256);\n",
|
||
|
|
"plt.xlabel('luminance');\n",
|
||
|
|
"plt.ylabel('number of occurances');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"We can see that there are two main peaks in the intensity histogram. We can also see that the intensities go from 0 to 255. This happens to be the range of values that fit in an 8 bit *byte*. This is the unit of memory size on modern computers. Is it correct to think that each pixel in this image is stored as a byte?\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"dtype('uint8')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"camera.dtype"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"The `dtype` of a numpy array is the \"data type\" - the type for each individual element of the array. Above we see the answer is `uint8` which means \"unsigned integer, 8 bits\". So, yes, each pixel is stored here as a byte."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"If you look at the histogram, you can see that there is some part of the luminance space which has very few occurances, namely above a luminance value of about 220. We can probably make better use of the 0-255 dynamic range available.\n",
|
||
|
|
"\n",
|
||
|
|
"In the cell below, enter a scale factor which fills the histogram of possible luminances more completely but does not cause too much clipping of the image values. The tower in the background should still be visible, for example. Look at the figures below to judge the effect of changing the scale factor. Put this in the variable `scale_factor`."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-9ff4a4d60c8485d0",
|
||
|
|
"locked": false,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scale_factor = 255/220"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-484ef1503b6e25a0",
|
||
|
|
"locked": true,
|
||
|
|
"points": 1,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# assert that scale_factor is a number\n",
|
||
|
|
"assert scale_factor - 0.0 == scale_factor"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Let's look at the histogram of rescaled luminances."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 400x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"rescaled = np.clip(scale_factor*camera.astype(np.float32),0,255).astype(np.uint8)\n",
|
||
|
|
"plt.hist(rescaled.flat,bins=256);\n",
|
||
|
|
"plt.xlabel('luminance');\n",
|
||
|
|
"plt.ylabel('number of occurances');\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(4, 4))\n",
|
||
|
|
"plt.imshow(rescaled, cmap='gray', interpolation='nearest', vmin=0, vmax=255)\n",
|
||
|
|
"plt.axis('off')\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"celltoolbar": "Create Assignment",
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.11.10"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|