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Update README.md
dofinator Feb 2, 2021
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halvvejs med week 1 opgaver
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Merge branch 'master' of https://github.com/dofinator/python_handin_t…
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432 changes: 409 additions & 23 deletions .ipynb_checkpoints/Week_1-checkpoint.ipynb
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345 changes: 345 additions & 0 deletions .ipynb_checkpoints/Week_11-checkpoint.ipynb
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6 changes: 6 additions & 0 deletions .ipynb_checkpoints/Week_11_2-checkpoint.ipynb
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
200 changes: 200 additions & 0 deletions .ipynb_checkpoints/Week_12-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise\n",
"### Part 1\n",
"1. Take a picture of an apple and put it in the image folder in the notebook environment\n",
"2. Find the contour of the apple\n",
"3. Draw a yellow circle with the center placed on the center of the apple contour\n",
"4. Draw a square that precisely holds the apple\n",
"5. Take a picture of three seperate apples.\n",
"6. Use contours to programmatically identify the number of apples in the photo"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# 1. Take a picture of an apple and put it in the image folder in the notebook environment\n",
"# DONE\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# 2. Find the contour of the apple\n",
"import cv2\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"\n",
"img = cv2.imread('../images/apple.jpg')\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cv2.inRange(img,lower,higher)<class 'numpy.ndarray'> Image shape: (720, 1080, 3) and mask shape: (720, 1080)\n",
" Image contains: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n",
" 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n",
" 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n",
" 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
" 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n",
" 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n",
" 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
" 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
" 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
" 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
" 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
" 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
" 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
" 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
" 252 253 254 255]\n",
" mask contains: [0] which is good cause object to be found should be white and background should be black.\n"
]
},
{
"data": {
"text/plain": [
"array([[0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" ...,\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def create_apple_mask(image):\n",
" \"\"\"From an image create a HSV mask by identifying lower and upper bounds of the three values\"\"\"\n",
" # Color values in HSV\n",
" green_lower = (0, 100, 50) # hsv (0-360, 0-255, 0-255) sometimes written as (0-360, 100%, 100%)\n",
" green_upper = (0, 71, 57)\n",
"\n",
" hsv_img = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n",
" \n",
" mask = cv2.inRange(hsv_img, green_lower, green_upper) # inRange: exclude all pixels outside of range\n",
" mask = cv2.dilate(mask, None, iterations=2) # dilate: (like pupils) expand pixels around the center\n",
" mask = cv2.erode(mask, None, iterations=2) # erode: opposite of dilate. remove pixels from outer layer\n",
" print(f'cv2.inRange(img,lower,higher){type(mask)} Image shape: {image.shape} and mask shape: {mask.shape}\\n Image contains: {np.unique(image)}\\n mask contains: {np.unique(mask)} which is good cause object to be found should be white and background should be black.')\n",
" \n",
" return mask\n",
"my_mask = create_apple_mask(img)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def mark_object(image, mask):\n",
" \"\"\"find the largest contour in the mask, then use it to compute the minimum enclosing circle and centroid\"\"\"\n",
" # Finds contours in a binary image. The contours are a useful tool for shape analysis and object detection and recognition.\n",
" contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\n",
"\n",
" c = max(contours, key=cv2.contourArea)\n",
" ((x, y), radius) = cv2.minEnclosingCircle(c)\n",
"\n",
" # draw the circle and centroid on the frame,\n",
" # then update the list of tracked points\n",
" cv2.circle(image, center=(int(x), int(y)), radius=int(radius), color=(255, 0, 0), thickness=3)\n",
" return image\n",
"mark_object(img, )"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cv2.inRange(img,lower,higher)<class 'numpy.ndarray'> Image shape: (720, 1080, 3) and mask shape: (720, 1080)\n",
" Image contains: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17\n",
" 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35\n",
" 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53\n",
" 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
" 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89\n",
" 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107\n",
" 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
" 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
" 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
" 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
" 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
" 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
" 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
" 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
" 252 253 254 255]\n",
" mask contains: [0] which is good cause object to be found should be white and background should be black.\n"
]
},
{
"ename": "ValueError",
"evalue": "max() arg is an empty sequence",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-31511dae7576>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_apple_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmark_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mimg_converted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOLOR_RGB2BGR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#cv2.imwrite('./images/week12apple.jpg', img_converted)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#cv.show()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-14-a7b900fb2a65>\u001b[0m in \u001b[0;36mmark_object\u001b[0;34m(image, mask)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mcontours\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindContours\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRETR_EXTERNAL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCHAIN_APPROX_SIMPLE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontours\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontourArea\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminEnclosingCircle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: max() arg is an empty sequence"
]
}
],
"source": [
"mask = create_apple_mask(img)\n",
"img = mark_object(img, mask)\n",
"img_converted = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
"#cv2.imwrite('./images/week12apple.jpg', img_converted)\n",
"#cv.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
97 changes: 72 additions & 25 deletions .ipynb_checkpoints/Week_2-checkpoint.ipynb
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Expand Up @@ -4,72 +4,119 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 2 handin"
"# 1. Create a python file with 3 functions:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assignment\n",
"Here is the text for the assignment linked to"
" 1.`def print_file_content(file)` that can print content of a csv file to the console\n",
" 2. `def write_list_to_file(output_file, lst)` that can take a list of tuple and write each element to a new line in file\n",
" 1. rewrite the function so that it gets an arbitrary number of strings instead of a list\n",
" 3. `def read_csv(input_file)` that take a csv file and read each row into a list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Status\n",
"Here is a status on the handin. How far you got. What is implemented and what is not"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution part 1"
"# ex. 1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 52,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Mælkevejen', ' er en flyvende']\n",
"['glatåljens', 'lars']\n",
"['ko', 'ost']\n"
]
}
],
"source": [
"# 1.1 some code here with docstrings"
"import csv\n",
"\n",
"test_file = \"./ost.csv\"\n",
"\n",
"def print_file_content(file_name):\n",
" with open(file_name, 'r') as file:\n",
" reader = csv.reader(file)\n",
" for row in reader:\n",
" print(row)\n",
" \n",
"print_file_content(test_file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"# 1.2 some code here with docstrings"
"# ex. 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 50,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('jens', 'lars')\n",
"('ko', 'ost')\n"
]
}
],
"source": [
"# 1.3"
"import csv\n",
"\n",
"test_file = \"./ost.csv\"\n",
"\n",
"random_list = [(\"jens\", \"lars\"), (\"ko\", \"ost\")]\n",
"\n",
"def write_list_to_file(output_file, lst):\n",
" with open(output_file, 'a') as file:\n",
" write = csv.writer(file)\n",
" for element in lst:\n",
" write.writerow(element)\n",
" print(element)\n",
" \n",
"write_list_to_file(test_file, random_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution part 2"
"## ex. 2 a\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": []
"source": [
"import csv\n",
"\n",
"test_file = \"./ost.csv\"\n",
"\n",
"random_list = [(\"jens\", \"lars\"), (\"ko\", \"ost\")]\n",
"\n",
"def write_arb_to_file(output_file, *strings):\n",
" with open(output_file, 'a') as file:\n",
" write = csv.writer(file)\n",
" write.writerow(*strings)\n",
" \n",
"write_arb_to_file(test_file, \"pat\", \"sovs\", \"osteklokke\")"
]
},
{
"cell_type": "code",
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