505 lines
878 KiB
Plaintext
505 lines
878 KiB
Plaintext
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{
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"cells": [
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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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"## Bean counting using connected components and more to do multiple object detection\n",
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"\n",
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"Now we have an image of many coffee beans and we want to count the number of coffee beans in the image."
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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": 1,
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"metadata": {},
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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\n",
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"import scipy.misc\n",
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"from scipy import ndimage\n",
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"import imageio.v2 as imageio"
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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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"outputs": [],
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"source": [
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"im_rgb = imageio.imread('data/IMG_4272.JPG')\n",
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"original_gray = im_rgb[:,:,1] # take green channel to make grayscale image"
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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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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 2 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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"plt.imshow(original_gray, cmap='gray')\n",
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"plt.colorbar();"
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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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"Let's view our image is pseudocolor. This will be helpful for getting absolute intensity values from the 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": 4,
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"metadata": {
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"scrolled": true
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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": [
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"<Figure size 640x480 with 2 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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"plt.imshow(original_gray, cmap='jet')\n",
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"plt.colorbar();"
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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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"One thing we may notice are some very small details in the image which will not help us perform our bean counting task."
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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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"outputs": [],
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"source": [
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"im = ndimage.gaussian_filter(original_gray, sigma=10.0)"
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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": 6,
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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": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(im, cmap='jet')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-349688ed72efb990",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Question Part A\n",
|
||
|
|
"\n",
|
||
|
|
"We now want to pick a single threshold. Ideally, this will perfectly decompose our image into connected components of coffee beans and a background.\n",
|
||
|
|
"\n",
|
||
|
|
"Enter a threshold and describe why you chose this value. Use the variable `threshold`."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-6e1c2b89bd8a66f5",
|
||
|
|
"locked": false,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Enter your value here\n",
|
||
|
|
"threshold=60"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-4590afe7907cebe0",
|
||
|
|
"locked": false,
|
||
|
|
"points": 0,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"(Here you should describe why you chose your particular threshold value.)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"thresholded = (im < threshold).astype(np.uint8)\n",
|
||
|
|
"plt.imshow(thresholded, cmap='gray')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Now, we are once again going to use [connected components labeling](https://en.wikipedia.org/wiki/Connected-component_labeling) to detect objects in our binary image."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"labels, num_labels = ndimage.label(thresholded)\n",
|
||
|
|
"plt.imshow(labels, cmap='jet')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Detour: erosion and dilation (morphological operations)\n",
|
||
|
|
"\n",
|
||
|
|
"Here we will take a small detour to demonstrate some morphological operations. You can read about morphological image processing [here](https://en.wikipedia.org/wiki/Mathematical_morphology).\n",
|
||
|
|
"\n",
|
||
|
|
"We will first perform `erosion` 30 times in a row on our thresholded image. What does the erosion do?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"eroded = thresholded\n",
|
||
|
|
"for i in range(30):\n",
|
||
|
|
" eroded = ndimage.binary_erosion(eroded)\n",
|
||
|
|
"plt.imshow(eroded.astype(np.uint8), cmap='gray')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Now we will perform `dilation` 30 times in a row on our thresholded image. What does the dilation do?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.colorbar.Colorbar at 0x12794c9d0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"dilated = thresholded\n",
|
||
|
|
"for i in range(30):\n",
|
||
|
|
" dilated = ndimage.binary_dilation(dilated)\n",
|
||
|
|
"plt.imshow(dilated.astype(np.uint8), cmap='gray')\n",
|
||
|
|
"plt.colorbar()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Now what about the combination of `erosion` with `dilation`?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"ed = thresholded\n",
|
||
|
|
"for i in range(30):\n",
|
||
|
|
" ed = ndimage.binary_erosion(ed)\n",
|
||
|
|
"for i in range(30):\n",
|
||
|
|
" ed = ndimage.binary_dilation(ed)\n",
|
||
|
|
"plt.imshow(ed.astype(np.uint8), cmap='gray')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# End of detour - no more morphological operations\n",
|
||
|
|
"\n",
|
||
|
|
"Let's view our connected components labels a bit larger."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x1000 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))\n",
|
||
|
|
"labels, num_labels = ndimage.label(thresholded)\n",
|
||
|
|
"plt.imshow(labels, cmap='jet')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"So, above are the labels for each connected component. Note that some individual coffee beans may be broken into two or more components and that sometimes multiple coffee beans may have been combined into one connected component."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Now we can calculate the \"center of mass\" of each connected component."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"coms = ndimage.center_of_mass(thresholded, labels, index=range(1,num_labels))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-1213340a2a7c110e",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Question Part B\n",
|
||
|
|
"\n",
|
||
|
|
"Change the parameters (and, if you like, change the processing steps) to label each coffee bean individually. Make a plot where each bean is marked with an \"x\".\n",
|
||
|
|
"\n",
|
||
|
|
"You may like to change:\n",
|
||
|
|
"\n",
|
||
|
|
" - the image channel used (red, green, blue, an average of all channels, ...)\n",
|
||
|
|
" - gaussian blur size\n",
|
||
|
|
" - threshold used for binarization\n",
|
||
|
|
" - optionally, any morphological image operations"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-9a32e77f790bb4da",
|
||
|
|
"locked": false,
|
||
|
|
"points": 2,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x1000 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))\n",
|
||
|
|
"plt.imshow(im, cmap='gray')\n",
|
||
|
|
"plt.colorbar()\n",
|
||
|
|
"for com in coms:\n",
|
||
|
|
" ax.plot([com[1]],[com[0]], 'x') "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-5b9512404535239b",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Question Part C\n",
|
||
|
|
"\n",
|
||
|
|
"Describe what can go wrong when performing this connected components analysis to count and identify individual coffee beans. Name two possible problems and what do you see if each problem happens?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-b109315aba1b058b",
|
||
|
|
"locked": false,
|
||
|
|
"points": 2,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"(Answer here.)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
}
|