├── README.md
├── Resources
├── 1.jpg
├── Scanned
│ ├── NoPlate_0.jpg
│ ├── NoPlate_1.jpg
│ └── NoPlate_2.jpg
├── Thumbnail.jpg
├── book.jpg
├── cards.jpg
├── colorPicker.py
├── haarcascade_frontalface_default.xml
├── haarcascade_russian_plate_number.xml
├── lambo.PNG
├── land.jpg
├── lena.png
├── p1.jpg
├── p2.jpg
├── p3.jpg
├── paper.jpg
├── shapes.png
└── test_video.mp4
├── Tumbnails
├── Chapter1.gif
├── Chapter2.gif
├── Chapter3.gif
├── Chapter4.gif
├── Chapter5.gif
├── Chapter6.gif
├── Chapter7.gif
├── Chapter8.gif
├── Chapter9.gif
├── Installations.gif
├── Introduction.gif
├── Marker.gif
├── project1.gif
├── project2.gif
└── project3.gif
├── chapter1.py
├── chapter2.py
├── chapter3.py
├── chapter4.py
├── chapter5.py
├── chapter6.py
├── chapter7.py
├── chapter8.py
├── chapter9.py
├── project1.py
├── project2.py
└── project3.py
/README.md:
--------------------------------------------------------------------------------
1 | # LEARN OPENCV IN 3 HOURS USING PYTHON - INCLUDING EXAMPLE PROJECTS
2 | [](https://youtu.be/WQeoO7MI0Bs)
3 |
4 |
5 | # What will you learn ?
6 |
7 |
8 | |Virtual Painter|Document Scanner|Number Plate Detector|
9 | |:----:|:----:|:----:|
10 | |
|
|
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11 | |Contours Detection|Color Detection|Face Detection|
12 | |
|
|
|
13 |
14 |
15 |
16 | # Prerequisites
17 |
18 |
19 | |Index|Topic|Image|Video|Description|
20 | |:----:|:----:|:----:|:----:|:----:|
21 | |1|Introduction to Images| 
|[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=137) | Basic concept of Images. The RGB Channels and Gray Scale Images. |
22 | |2|Installations| 
|[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=277) | Installation process of Pyhton, OpenCV and the Pycharm IDE |
23 |
24 |
25 |
26 |
27 | # Chapters
28 |
29 | |Chapter|Topic|Image|Video|Description|
30 | |:----:|:----:|:----:|:----:|:----:|
31 | |1| [How to Read Image Video-Webcam](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/chapter1.py)| 
|[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=549) | Learn how to read images videos and webcam. |
32 | |2| [5 Must Know OpenCV Basic Functions](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/chapter2.py)| 
|[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=1021) | 5 Must know opencv functions for beginners. Gray Scale, Blur, Edge Detection, Dialation and Erosion. |
33 | |3| [How to Crop and Resize Images](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/chapter3.py)| 
|[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=1651) | How to crop and resize and iamge. Resize could be used to scale up or scale down an image where cropping can be used to get a part of the image. |
34 | |4| [How to Draw Shapes and Text](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/chapter4.py)| 
|[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=2052) | Learn to create blank images along with how to draw Lines, rectangles, circles and custom text. |
35 | |5| [Warp Prespective/BirdView](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/chapter5.py)| 
|[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=2699) | Learn how to creat a warp prespective of a selected area of an image using fixed points. |
36 | |6| [Joining Multiple Images to Display](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/chapter6.py)| 
|[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=3004) | Join multiple images together as one image for easy visualization of the workflow. Learn how to do it for smaller noumber of images and how it could be scaled up to have several images in the same image. |
37 | |7| [Color Detection](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/chapter7.py)| |[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=3374) |How to detect any color in an image using the HSV space with the help of opencv Trackbars. |
38 | |8| [Contour/Shape Detection](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/chapter8.py)| 
|[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=4537) |How to detect shapes of objects by finding their contours. Contours are basically outline that bound the shape or form of an object. So we will be detecting multiple shapes and how many corners points each shape has along with its area . |
39 | |9| [Face Detection](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/chapter9.py)| |[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=6031) |How to detect faces in realtime using Viola Jones method. |
40 | |___|___________________|______________________________|__________| ____________________________
41 |
42 |
43 | # Projects
44 |
45 |
46 |
47 | |Project|Topic|Image|Video|Description|
48 | |:----:|:----:|:----:|:----:|:----:|
49 | |1| [Virtual Paint](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/project1.py)| |[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=6363) | Detecting Color and using colored marker to draw virtually. |
50 | |2| [Documnet Scanner](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/project2.py)| |[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=8145) | In this project we are going to create a simple document scanner using opencv. We will learn how to run this in real time |
51 | |3|[Number Plate Detector](https://github.com/murtazahassan/Learn-OpenCV-in-3-hours/blob/master/project3.py)| |[Watch Now](https://youtu.be/WQeoO7MI0Bs?t=10594) | In this project we will detect number plates on cars in realtime and save them with a click of a button. |
52 |
53 |
54 | # Notes
55 |
56 |
57 | |Index|Comment|
58 | |:---|:---|
59 | |1.|[Recommended IDE: PyCharm Community edition](https://www.jetbrains.com/pycharm/download/)|
60 | |2.|[Python: 3.7.6](https://www.python.org/downloads/release/python-376/)|
61 |
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/Resources/1.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/1.jpg
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/Resources/Scanned/NoPlate_0.jpg:
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/Resources/Scanned/NoPlate_1.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/Scanned/NoPlate_1.jpg
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/Resources/Scanned/NoPlate_2.jpg:
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/Resources/Thumbnail.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/Thumbnail.jpg
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/Resources/book.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/book.jpg
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/Resources/cards.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/cards.jpg
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/Resources/colorPicker.py:
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1 | import cv2
2 | import numpy as np
3 |
4 | frameWidth = 640
5 | frameHeight = 480
6 | cap = cv2.VideoCapture(1)
7 | cap.set(3, frameWidth)
8 | cap.set(4, frameHeight)
9 | cap.set(10,150)
10 |
11 | def empty(a):
12 | pass
13 |
14 | cv2.namedWindow("HSV")
15 | cv2.resizeWindow("HSV",640,240)
16 | cv2.createTrackbar("HUE Min","HSV",0,179,empty)
17 | cv2.createTrackbar("SAT Min","HSV",0,255,empty)
18 | cv2.createTrackbar("VALUE Min","HSV",0,255,empty)
19 | cv2.createTrackbar("HUE Max","HSV",179,179,empty)
20 | cv2.createTrackbar("SAT Max","HSV",255,255,empty)
21 | cv2.createTrackbar("VALUE Max","HSV",255,255,empty)
22 |
23 | while True:
24 |
25 | _, img = cap.read()
26 | imgHsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
27 |
28 | h_min = cv2.getTrackbarPos("HUE Min","HSV")
29 | h_max = cv2.getTrackbarPos("HUE Max", "HSV")
30 | s_min = cv2.getTrackbarPos("SAT Min", "HSV")
31 | s_max = cv2.getTrackbarPos("SAT Max", "HSV")
32 | v_min = cv2.getTrackbarPos("VALUE Min", "HSV")
33 | v_max = cv2.getTrackbarPos("VALUE Max", "HSV")
34 | print(h_min)
35 |
36 | lower = np.array([h_min,s_min,v_min])
37 | upper = np.array([h_max,s_max,v_max])
38 | mask = cv2.inRange(imgHsv,lower,upper)
39 | result = cv2.bitwise_and(img,img, mask = mask)
40 |
41 | mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
42 | hStack = np.hstack([img,mask,result])
43 | #cv2.imshow('Original', img)
44 | #cv2.imshow('HSV Color Space', imgHsv)
45 | #cv2.imshow('Mask', mask)
46 | #cv2.imshow('Result', result)
47 | cv2.imshow('Horizontal Stacking', hStack)
48 | if cv2.waitKey(1) & 0xFF == ord('q'):
49 | break
50 |
51 | cap.release()
52 | cv2.destroyAllWindows()
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/Resources/haarcascade_russian_plate_number.xml:
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1 |
2 |
3 |
4 | BOOST
5 | HAAR
6 | 20
7 | 60
8 |
9 | GAB
10 | 9.9500000476837158e-001
11 | 5.0000000000000000e-001
12 | 9.4999999999999996e-001
13 | 1
14 | 100
15 |
16 | 0
17 | 1
18 | ALL
19 | 20
20 |
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22 | <_>
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25 |
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56 |
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89 |
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91 |
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269 |
270 | 5.8551025390625000e-001 -3.0389472842216492e-001
271 |
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275 |
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376 |
377 | <_>
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384 |
385 | -6.0168600082397461e-001 1.9141913950443268e-001
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1962 | <_>
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1976 | <_>
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1995 | <_>
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1997 | <_>
1998 | 26 3 22 3 -1.
1999 | <_>
2000 | 26 4 22 1 3.
2001 | 0
2002 | <_>
2003 |
2004 | <_>
2005 | 26 4 8 4 -1.
2006 | <_>
2007 | 30 4 4 4 2.
2008 | 0
2009 | <_>
2010 |
2011 | <_>
2012 | 26 4 20 6 -1.
2013 | <_>
2014 | 31 4 10 6 2.
2015 | 0
2016 | <_>
2017 |
2018 | <_>
2019 | 26 7 1 12 -1.
2020 | <_>
2021 | 22 11 1 4 3.
2022 | 1
2023 | <_>
2024 |
2025 | <_>
2026 | 26 9 3 3 -1.
2027 | <_>
2028 | 27 9 1 3 3.
2029 | 0
2030 | <_>
2031 |
2032 | <_>
2033 | 26 13 9 3 -1.
2034 | <_>
2035 | 26 14 9 1 3.
2036 | 0
2037 | <_>
2038 |
2039 | <_>
2040 | 27 3 15 6 -1.
2041 | <_>
2042 | 32 3 5 6 3.
2043 | 0
2044 | <_>
2045 |
2046 | <_>
2047 | 27 9 3 1 -1.
2048 | <_>
2049 | 28 9 1 1 3.
2050 | 0
2051 | <_>
2052 |
2053 | <_>
2054 | 27 9 3 2 -1.
2055 | <_>
2056 | 28 9 1 2 3.
2057 | 0
2058 | <_>
2059 |
2060 | <_>
2061 | 27 10 3 3 -1.
2062 | <_>
2063 | 28 10 1 3 3.
2064 | 0
2065 | <_>
2066 |
2067 | <_>
2068 | 27 11 3 2 -1.
2069 | <_>
2070 | 28 11 1 2 3.
2071 | 0
2072 | <_>
2073 |
2074 | <_>
2075 | 28 2 10 4 -1.
2076 | <_>
2077 | 28 2 10 2 2.
2078 | 1
2079 | <_>
2080 |
2081 | <_>
2082 | 28 8 32 6 -1.
2083 | <_>
2084 | 28 10 32 2 3.
2085 | 0
2086 | <_>
2087 |
2088 | <_>
2089 | 28 10 3 1 -1.
2090 | <_>
2091 | 29 10 1 1 3.
2092 | 0
2093 | <_>
2094 |
2095 | <_>
2096 | 28 11 3 1 -1.
2097 | <_>
2098 | 29 11 1 1 3.
2099 | 0
2100 | <_>
2101 |
2102 | <_>
2103 | 28 15 5 4 -1.
2104 | <_>
2105 | 28 16 5 2 2.
2106 | 0
2107 | <_>
2108 |
2109 | <_>
2110 | 28 16 23 4 -1.
2111 | <_>
2112 | 28 17 23 2 2.
2113 | 0
2114 | <_>
2115 |
2116 | <_>
2117 | 28 19 6 1 -1.
2118 | <_>
2119 | 30 19 2 1 3.
2120 | 0
2121 | <_>
2122 |
2123 | <_>
2124 | 29 3 9 4 -1.
2125 | <_>
2126 | 32 3 3 4 3.
2127 | 0
2128 | <_>
2129 |
2130 | <_>
2131 | 29 5 9 1 -1.
2132 | <_>
2133 | 32 5 3 1 3.
2134 | 0
2135 | <_>
2136 |
2137 | <_>
2138 | 29 8 3 6 -1.
2139 | <_>
2140 | 30 8 1 6 3.
2141 | 0
2142 | <_>
2143 |
2144 | <_>
2145 | 29 9 3 1 -1.
2146 | <_>
2147 | 30 9 1 1 3.
2148 | 0
2149 | <_>
2150 |
2151 | <_>
2152 | 29 11 10 4 -1.
2153 | <_>
2154 | 29 13 10 2 2.
2155 | 0
2156 | <_>
2157 |
2158 | <_>
2159 | 29 11 26 8 -1.
2160 | <_>
2161 | 29 13 26 4 2.
2162 | 0
2163 | <_>
2164 |
2165 | <_>
2166 | 30 0 16 6 -1.
2167 | <_>
2168 | 30 3 16 3 2.
2169 | 0
2170 | <_>
2171 |
2172 | <_>
2173 | 30 2 30 6 -1.
2174 | <_>
2175 | 30 2 15 3 2.
2176 | <_>
2177 | 45 5 15 3 2.
2178 | 0
2179 | <_>
2180 |
2181 | <_>
2182 | 30 3 9 4 -1.
2183 | <_>
2184 | 33 3 3 4 3.
2185 | 0
2186 | <_>
2187 |
2188 | <_>
2189 | 30 5 9 4 -1.
2190 | <_>
2191 | 30 6 9 2 2.
2192 | 0
2193 | <_>
2194 |
2195 | <_>
2196 | 30 10 3 2 -1.
2197 | <_>
2198 | 31 10 1 2 3.
2199 | 0
2200 | <_>
2201 |
2202 | <_>
2203 | 30 14 18 6 -1.
2204 | <_>
2205 | 36 14 6 6 3.
2206 | 0
2207 | <_>
2208 |
2209 | <_>
2210 | 31 3 4 3 -1.
2211 | <_>
2212 | 32 3 2 3 2.
2213 | 0
2214 | <_>
2215 |
2216 | <_>
2217 | 31 7 4 9 -1.
2218 | <_>
2219 | 32 7 2 9 2.
2220 | 0
2221 | <_>
2222 |
2223 | <_>
2224 | 31 11 3 2 -1.
2225 | <_>
2226 | 32 11 1 2 3.
2227 | 0
2228 | <_>
2229 |
2230 | <_>
2231 | 31 11 3 3 -1.
2232 | <_>
2233 | 32 11 1 3 3.
2234 | 0
2235 | <_>
2236 |
2237 | <_>
2238 | 32 4 3 2 -1.
2239 | <_>
2240 | 33 4 1 2 3.
2241 | 0
2242 | <_>
2243 |
2244 | <_>
2245 | 32 6 18 6 -1.
2246 | <_>
2247 | 32 6 9 3 2.
2248 | <_>
2249 | 41 9 9 3 2.
2250 | 0
2251 | <_>
2252 |
2253 | <_>
2254 | 33 1 22 6 -1.
2255 | <_>
2256 | 33 4 22 3 2.
2257 | 0
2258 | <_>
2259 |
2260 | <_>
2261 | 33 3 4 2 -1.
2262 | <_>
2263 | 34 3 2 2 2.
2264 | 0
2265 | <_>
2266 |
2267 | <_>
2268 | 33 3 4 4 -1.
2269 | <_>
2270 | 34 3 2 4 2.
2271 | 0
2272 | <_>
2273 |
2274 | <_>
2275 | 33 5 4 1 -1.
2276 | <_>
2277 | 34 5 2 1 2.
2278 | 0
2279 | <_>
2280 |
2281 | <_>
2282 | 33 9 3 6 -1.
2283 | <_>
2284 | 34 9 1 6 3.
2285 | 0
2286 | <_>
2287 |
2288 | <_>
2289 | 33 10 3 3 -1.
2290 | <_>
2291 | 34 10 1 3 3.
2292 | 0
2293 | <_>
2294 |
2295 | <_>
2296 | 34 8 4 7 -1.
2297 | <_>
2298 | 35 8 2 7 2.
2299 | 0
2300 | <_>
2301 |
2302 | <_>
2303 | 34 9 3 5 -1.
2304 | <_>
2305 | 35 9 1 5 3.
2306 | 0
2307 | <_>
2308 |
2309 | <_>
2310 | 34 18 9 2 -1.
2311 | <_>
2312 | 37 18 3 2 3.
2313 | 0
2314 | <_>
2315 |
2316 | <_>
2317 | 35 0 8 6 -1.
2318 | <_>
2319 | 37 0 4 6 2.
2320 | 0
2321 | <_>
2322 |
2323 | <_>
2324 | 35 9 3 2 -1.
2325 | <_>
2326 | 36 9 1 2 3.
2327 | 0
2328 | <_>
2329 |
2330 | <_>
2331 | 36 9 24 9 -1.
2332 | <_>
2333 | 42 9 12 9 2.
2334 | 0
2335 | <_>
2336 |
2337 | <_>
2338 | 37 1 16 18 -1.
2339 | <_>
2340 | 41 1 8 18 2.
2341 | 0
2342 | <_>
2343 |
2344 | <_>
2345 | 37 11 20 8 -1.
2346 | <_>
2347 | 42 11 10 8 2.
2348 | 0
2349 | <_>
2350 |
2351 | <_>
2352 | 38 8 15 12 -1.
2353 | <_>
2354 | 38 12 15 4 3.
2355 | 0
2356 | <_>
2357 |
2358 | <_>
2359 | 39 6 12 8 -1.
2360 | <_>
2361 | 45 6 6 8 2.
2362 | 0
2363 | <_>
2364 |
2365 | <_>
2366 | 40 8 8 4 -1.
2367 | <_>
2368 | 40 8 8 2 2.
2369 | 1
2370 | <_>
2371 |
2372 | <_>
2373 | 40 10 3 1 -1.
2374 | <_>
2375 | 41 10 1 1 3.
2376 | 0
2377 | <_>
2378 |
2379 | <_>
2380 | 40 10 3 5 -1.
2381 | <_>
2382 | 41 10 1 5 3.
2383 | 0
2384 | <_>
2385 |
2386 | <_>
2387 | 40 13 12 6 -1.
2388 | <_>
2389 | 43 13 6 6 2.
2390 | 0
2391 | <_>
2392 |
2393 | <_>
2394 | 41 5 7 15 -1.
2395 | <_>
2396 | 41 10 7 5 3.
2397 | 0
2398 | <_>
2399 |
2400 | <_>
2401 | 41 6 12 6 -1.
2402 | <_>
2403 | 45 6 4 6 3.
2404 | 0
2405 | <_>
2406 |
2407 | <_>
2408 | 41 7 12 7 -1.
2409 | <_>
2410 | 45 7 4 7 3.
2411 | 0
2412 | <_>
2413 |
2414 | <_>
2415 | 41 8 12 12 -1.
2416 | <_>
2417 | 45 8 4 12 3.
2418 | 0
2419 | <_>
2420 |
2421 | <_>
2422 | 41 9 3 6 -1.
2423 | <_>
2424 | 42 9 1 6 3.
2425 | 0
2426 | <_>
2427 |
2428 | <_>
2429 | 42 2 3 13 -1.
2430 | <_>
2431 | 43 2 1 13 3.
2432 | 0
2433 | <_>
2434 |
2435 | <_>
2436 | 42 4 18 10 -1.
2437 | <_>
2438 | 42 4 9 5 2.
2439 | <_>
2440 | 51 9 9 5 2.
2441 | 0
2442 | <_>
2443 |
2444 | <_>
2445 | 42 5 18 8 -1.
2446 | <_>
2447 | 42 5 9 4 2.
2448 | <_>
2449 | 51 9 9 4 2.
2450 | 0
2451 | <_>
2452 |
2453 | <_>
2454 | 42 7 2 7 -1.
2455 | <_>
2456 | 43 7 1 7 2.
2457 | 0
2458 | <_>
2459 |
2460 | <_>
2461 | 42 14 12 5 -1.
2462 | <_>
2463 | 46 14 4 5 3.
2464 | 0
2465 | <_>
2466 |
2467 | <_>
2468 | 43 1 10 9 -1.
2469 | <_>
2470 | 40 4 10 3 3.
2471 | 1
2472 | <_>
2473 |
2474 | <_>
2475 | 43 6 6 6 -1.
2476 | <_>
2477 | 43 9 6 3 2.
2478 | 0
2479 | <_>
2480 |
2481 | <_>
2482 | 44 0 8 20 -1.
2483 | <_>
2484 | 46 0 4 20 2.
2485 | 0
2486 | <_>
2487 |
2488 | <_>
2489 | 44 2 16 12 -1.
2490 | <_>
2491 | 44 2 8 6 2.
2492 | <_>
2493 | 52 8 8 6 2.
2494 | 0
2495 | <_>
2496 |
2497 | <_>
2498 | 44 5 3 8 -1.
2499 | <_>
2500 | 45 5 1 8 3.
2501 | 0
2502 | <_>
2503 |
2504 | <_>
2505 | 44 8 3 4 -1.
2506 | <_>
2507 | 45 8 1 4 3.
2508 | 0
2509 | <_>
2510 |
2511 | <_>
2512 | 44 12 16 4 -1.
2513 | <_>
2514 | 52 12 8 4 2.
2515 | 0
2516 | <_>
2517 |
2518 | <_>
2519 | 44 13 10 3 -1.
2520 | <_>
2521 | 49 13 5 3 2.
2522 | 0
2523 | <_>
2524 |
2525 | <_>
2526 | 45 19 9 1 -1.
2527 | <_>
2528 | 48 19 3 1 3.
2529 | 0
2530 | <_>
2531 |
2532 | <_>
2533 | 46 3 8 8 -1.
2534 | <_>
2535 | 50 3 4 8 2.
2536 | 0
2537 | <_>
2538 |
2539 | <_>
2540 | 47 12 10 6 -1.
2541 | <_>
2542 | 52 12 5 6 2.
2543 | 0
2544 | <_>
2545 |
2546 | <_>
2547 | 48 0 4 13 -1.
2548 | <_>
2549 | 49 0 2 13 2.
2550 | 0
2551 | <_>
2552 |
2553 | <_>
2554 | 48 5 3 12 -1.
2555 | <_>
2556 | 45 8 3 6 2.
2557 | 1
2558 | <_>
2559 |
2560 | <_>
2561 | 48 9 12 8 -1.
2562 | <_>
2563 | 54 9 6 8 2.
2564 | 0
2565 | <_>
2566 |
2567 | <_>
2568 | 48 13 12 4 -1.
2569 | <_>
2570 | 54 13 6 4 2.
2571 | 0
2572 | <_>
2573 |
2574 | <_>
2575 | 49 8 3 1 -1.
2576 | <_>
2577 | 50 8 1 1 3.
2578 | 0
2579 | <_>
2580 |
2581 | <_>
2582 | 49 8 3 2 -1.
2583 | <_>
2584 | 50 8 1 2 3.
2585 | 0
2586 | <_>
2587 |
2588 | <_>
2589 | 49 8 3 3 -1.
2590 | <_>
2591 | 50 8 1 3 3.
2592 | 0
2593 | <_>
2594 |
2595 | <_>
2596 | 50 9 3 3 -1.
2597 | <_>
2598 | 51 10 1 1 9.
2599 | 0
2600 | <_>
2601 |
2602 | <_>
2603 | 51 8 3 3 -1.
2604 | <_>
2605 | 52 8 1 3 3.
2606 | 0
2607 | <_>
2608 |
2609 | <_>
2610 | 52 6 6 10 -1.
2611 | <_>
2612 | 54 6 2 10 3.
2613 | 0
2614 | <_>
2615 |
2616 | <_>
2617 | 52 7 8 7 -1.
2618 | <_>
2619 | 56 7 4 7 2.
2620 | 0
2621 | <_>
2622 |
2623 | <_>
2624 | 52 8 8 4 -1.
2625 | <_>
2626 | 52 8 8 2 2.
2627 | 1
2628 | <_>
2629 |
2630 | <_>
2631 | 54 3 6 15 -1.
2632 | <_>
2633 | 57 3 3 15 2.
2634 | 0
2635 | <_>
2636 |
2637 | <_>
2638 | 54 8 6 7 -1.
2639 | <_>
2640 | 57 8 3 7 2.
2641 | 0
2642 | <_>
2643 |
2644 | <_>
2645 | 57 11 3 6 -1.
2646 | <_>
2647 | 57 13 3 2 3.
2648 | 0
2649 | <_>
2650 |
2651 | <_>
2652 | 59 8 1 3 -1.
2653 | <_>
2654 | 59 9 1 1 3.
2655 | 0
2656 |
2657 |
--------------------------------------------------------------------------------
/Resources/lambo.PNG:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/lambo.PNG
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/Resources/land.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/land.jpg
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/Resources/lena.png:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/lena.png
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/Resources/p1.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/p1.jpg
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/Resources/p2.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/p2.jpg
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/Resources/p3.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/p3.jpg
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/Resources/paper.jpg:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/paper.jpg
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/Resources/shapes.png:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/shapes.png
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/Resources/test_video.mp4:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Resources/test_video.mp4
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/Tumbnails/Chapter1.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Chapter1.gif
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/Tumbnails/Chapter2.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Chapter2.gif
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/Tumbnails/Chapter3.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Chapter3.gif
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/Tumbnails/Chapter4.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Chapter4.gif
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/Tumbnails/Chapter5.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Chapter5.gif
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/Tumbnails/Chapter6.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Chapter6.gif
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/Tumbnails/Chapter7.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Chapter7.gif
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/Tumbnails/Chapter8.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Chapter8.gif
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/Tumbnails/Chapter9.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Chapter9.gif
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/Tumbnails/Installations.gif:
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/Tumbnails/Introduction.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/Introduction.gif
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/Tumbnails/Marker.gif:
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/Tumbnails/project1.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/project1.gif
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/Tumbnails/project2.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/project2.gif
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/Tumbnails/project3.gif:
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https://raw.githubusercontent.com/murtazahassan/Learn-OpenCV-in-3-hours/d4d6a14c7151aa4ebe23eb2a7cc8f94db05384c3/Tumbnails/project3.gif
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/chapter1.py:
--------------------------------------------------------------------------------
1 | ######################## READ IMAGE ############################
2 | # import cv2
3 | # # LOAD AN IMAGE USING 'IMREAD'
4 | # img = cv2.imread("Resources/lena.png")
5 | # # DISPLAY
6 | # cv2.imshow("Lena Soderberg",img)
7 | # cv2.waitKey(0)
8 |
9 | ######################### READ VIDEO #############################
10 | # import cv2
11 | # frameWidth = 640
12 | # frameHeight = 480
13 | # cap = cv2.VideoCapture("Resources/test_ video.mp4")
14 | # while True:
15 | # success, img = cap.read()
16 | # img = cv2.resize(img, (frameWidth, frameHeight))
17 | # cv2.imshow("Result", img)
18 | # if cv2.waitKey(1) & 0xFF == ord('q'):
19 | # break
20 | ######################### READ WEBCAM ############################
21 | import cv2
22 | frameWidth = 640
23 | frameHeight = 480
24 | cap = cv2.VideoCapture(0)
25 | cap.set(3, frameWidth)
26 | cap.set(4, frameHeight)
27 | cap.set(10,150)
28 | while True:
29 | success, img = cap.read()
30 | cv2.imshow("Result", img)
31 | if cv2.waitKey(1) & 0xFF == ord('q'):
32 | break
33 |
--------------------------------------------------------------------------------
/chapter2.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 |
4 | img = cv2.imread("Resources/lena.png")
5 | kernel = np.ones((5,5),np.uint8)
6 |
7 | imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
8 | imgBlur = cv2.GaussianBlur(imgGray,(7,7),0)
9 | imgCanny = cv2.Canny(img,150,200)
10 | imgDialation = cv2.dilate(imgCanny,kernel,iterations=1)
11 | imgEroded = cv2.erode(imgDialation,kernel,iterations=1)
12 |
13 | cv2.imshow("Gray Image",imgGray)
14 | cv2.imshow("Blur Image",imgBlur)
15 | cv2.imshow("Canny Image",imgCanny)
16 | cv2.imshow("Dialation Image",imgDialation)
17 | cv2.imshow("Eroded Image",imgEroded)
18 | cv2.waitKey(0)
--------------------------------------------------------------------------------
/chapter3.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 |
4 | img = cv2.imread("Resources/shapes.png")
5 | print(img.shape)
6 |
7 | imgResize = cv2.resize(img,(1000,500))
8 | print(imgResize.shape)
9 |
10 | imgCropped = img[46:119,352:495]
11 |
12 | cv2.imshow("Image",img)
13 | #cv2.imshow("Image Resize",imgResize)
14 | cv2.imshow("Image Cropped",imgCropped)
15 |
16 | cv2.waitKey(0)
--------------------------------------------------------------------------------
/chapter4.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 |
4 | img = np.zeros((512,512,3),np.uint8)
5 | #print(img)
6 | #img[:]= 255,0,0
7 |
8 | cv2.line(img,(0,0),(img.shape[1],img.shape[0]),(0,255,0),3)
9 | cv2.rectangle(img,(0,0),(250,350),(0,0,255),2)
10 | cv2.circle(img,(400,50),30,(255,255,0),5)
11 | cv2.putText(img," OPENCV ",(300,200),cv2.FONT_HERSHEY_COMPLEX,1,(0,150,0),3)
12 |
13 |
14 | cv2.imshow("Image",img)
15 |
16 | cv2.waitKey(0)
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/chapter5.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 |
4 | img = cv2.imread("Resources/cards.jpg")
5 |
6 | width,height = 250,350
7 | pts1 = np.float32([[111,219],[287,188],[154,482],[352,440]])
8 | pts2 = np.float32([[0,0],[width,0],[0,height],[width,height]])
9 | matrix = cv2.getPerspectiveTransform(pts1,pts2)
10 | imgOutput = cv2.warpPerspective(img,matrix,(width,height))
11 |
12 |
13 | cv2.imshow("Image",img)
14 | cv2.imshow("Output",imgOutput)
15 |
16 | cv2.waitKey(0)
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/chapter6.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 |
4 |
5 | def stackImages(scale,imgArray):
6 | rows = len(imgArray)
7 | cols = len(imgArray[0])
8 | rowsAvailable = isinstance(imgArray[0], list)
9 | width = imgArray[0][0].shape[1]
10 | height = imgArray[0][0].shape[0]
11 | if rowsAvailable:
12 | for x in range ( 0, rows):
13 | for y in range(0, cols):
14 | if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
15 | imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
16 | else:
17 | imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
18 | if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
19 | imageBlank = np.zeros((height, width, 3), np.uint8)
20 | hor = [imageBlank]*rows
21 | hor_con = [imageBlank]*rows
22 | for x in range(0, rows):
23 | hor[x] = np.hstack(imgArray[x])
24 | ver = np.vstack(hor)
25 | else:
26 | for x in range(0, rows):
27 | if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
28 | imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
29 | else:
30 | imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
31 | if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
32 | hor= np.hstack(imgArray)
33 | ver = hor
34 | return ver
35 |
36 | img = cv2.imread('Resources/lena.png')
37 | imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
38 |
39 | imgStack = stackImages(0.5,([img,imgGray,img],[img,img,img]))
40 |
41 | # imgHor = np.hstack((img,img))
42 | # imgVer = np.vstack((img,img))
43 | #
44 | # cv2.imshow("Horizontal",imgHor)
45 | # cv2.imshow("Vertical",imgVer)
46 | cv2.imshow("ImageStack",imgStack)
47 |
48 | cv2.waitKey(0)
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/chapter7.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 |
4 | def empty(a):
5 | pass
6 |
7 | def stackImages(scale,imgArray):
8 | rows = len(imgArray)
9 | cols = len(imgArray[0])
10 | rowsAvailable = isinstance(imgArray[0], list)
11 | width = imgArray[0][0].shape[1]
12 | height = imgArray[0][0].shape[0]
13 | if rowsAvailable:
14 | for x in range ( 0, rows):
15 | for y in range(0, cols):
16 | if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
17 | imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
18 | else:
19 | imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
20 | if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
21 | imageBlank = np.zeros((height, width, 3), np.uint8)
22 | hor = [imageBlank]*rows
23 | hor_con = [imageBlank]*rows
24 | for x in range(0, rows):
25 | hor[x] = np.hstack(imgArray[x])
26 | ver = np.vstack(hor)
27 | else:
28 | for x in range(0, rows):
29 | if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
30 | imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
31 | else:
32 | imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
33 | if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
34 | hor= np.hstack(imgArray)
35 | ver = hor
36 | return ver
37 |
38 |
39 |
40 | path = 'Resources/lambo.png'
41 | cv2.namedWindow("TrackBars")
42 | cv2.resizeWindow("TrackBars",640,240)
43 | cv2.createTrackbar("Hue Min","TrackBars",0,179,empty)
44 | cv2.createTrackbar("Hue Max","TrackBars",19,179,empty)
45 | cv2.createTrackbar("Sat Min","TrackBars",110,255,empty)
46 | cv2.createTrackbar("Sat Max","TrackBars",240,255,empty)
47 | cv2.createTrackbar("Val Min","TrackBars",153,255,empty)
48 | cv2.createTrackbar("Val Max","TrackBars",255,255,empty)
49 |
50 | while True:
51 | img = cv2.imread(path)
52 | imgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
53 | h_min = cv2.getTrackbarPos("Hue Min","TrackBars")
54 | h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")
55 | s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")
56 | s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")
57 | v_min = cv2.getTrackbarPos("Val Min", "TrackBars")
58 | v_max = cv2.getTrackbarPos("Val Max", "TrackBars")
59 | print(h_min,h_max,s_min,s_max,v_min,v_max)
60 | lower = np.array([h_min,s_min,v_min])
61 | upper = np.array([h_max,s_max,v_max])
62 | mask = cv2.inRange(imgHSV,lower,upper)
63 | imgResult = cv2.bitwise_and(img,img,mask=mask)
64 |
65 |
66 | # cv2.imshow("Original",img)
67 | # cv2.imshow("HSV",imgHSV)
68 | # cv2.imshow("Mask", mask)
69 | # cv2.imshow("Result", imgResult)
70 |
71 | imgStack = stackImages(0.6,([img,imgHSV],[mask,imgResult]))
72 | cv2.imshow("Stacked Images", imgStack)
73 |
74 | cv2.waitKey(1)
75 |
76 |
77 |
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/chapter8.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 |
4 | def stackImages(scale,imgArray):
5 | rows = len(imgArray)
6 | cols = len(imgArray[0])
7 | rowsAvailable = isinstance(imgArray[0], list)
8 | width = imgArray[0][0].shape[1]
9 | height = imgArray[0][0].shape[0]
10 | if rowsAvailable:
11 | for x in range ( 0, rows):
12 | for y in range(0, cols):
13 | if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
14 | imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
15 | else:
16 | imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
17 | if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
18 | imageBlank = np.zeros((height, width, 3), np.uint8)
19 | hor = [imageBlank]*rows
20 | hor_con = [imageBlank]*rows
21 | for x in range(0, rows):
22 | hor[x] = np.hstack(imgArray[x])
23 | ver = np.vstack(hor)
24 | else:
25 | for x in range(0, rows):
26 | if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
27 | imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
28 | else:
29 | imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
30 | if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
31 | hor= np.hstack(imgArray)
32 | ver = hor
33 | return ver
34 |
35 | def getContours(img):
36 | contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
37 | for cnt in contours:
38 | area = cv2.contourArea(cnt)
39 | print(area)
40 | if area>500:
41 | cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
42 | peri = cv2.arcLength(cnt,True)
43 | #print(peri)
44 | approx = cv2.approxPolyDP(cnt,0.02*peri,True)
45 | print(len(approx))
46 | objCor = len(approx)
47 | x, y, w, h = cv2.boundingRect(approx)
48 |
49 | if objCor ==3: objectType ="Tri"
50 | elif objCor == 4:
51 | aspRatio = w/float(h)
52 | if aspRatio >0.98 and aspRatio <1.03: objectType= "Square"
53 | else:objectType="Rectangle"
54 | elif objCor>4: objectType= "Circles"
55 | else:objectType="None"
56 |
57 |
58 |
59 | cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)
60 | cv2.putText(imgContour,objectType,
61 | (x+(w//2)-10,y+(h//2)-10),cv2.FONT_HERSHEY_COMPLEX,0.7,
62 | (0,0,0),2)
63 |
64 |
65 |
66 |
67 | path = 'Resources/shapes.png'
68 | img = cv2.imread(path)
69 | imgContour = img.copy()
70 |
71 | imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
72 | imgBlur = cv2.GaussianBlur(imgGray,(7,7),1)
73 | imgCanny = cv2.Canny(imgBlur,50,50)
74 | getContours(imgCanny)
75 |
76 | imgBlank = np.zeros_like(img)
77 | imgStack = stackImages(0.8,([img,imgGray,imgBlur],
78 | [imgCanny,imgContour,imgBlank]))
79 |
80 | cv2.imshow("Stack", imgStack)
81 |
82 | cv2.waitKey(0)
83 |
--------------------------------------------------------------------------------
/chapter9.py:
--------------------------------------------------------------------------------
1 | import cv2
2 |
3 | faceCascade= cv2.CascadeClassifier("Resources/haarcascade_frontalface_default.xml")
4 | img = cv2.imread('Resources/lena.png')
5 | imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
6 |
7 | faces = faceCascade.detectMultiScale(imgGray,1.1,4)
8 |
9 | for (x,y,w,h) in faces:
10 | cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
11 |
12 |
13 | cv2.imshow("Result", img)
14 | cv2.waitKey(0)
--------------------------------------------------------------------------------
/project1.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 | frameWidth = 640
4 | frameHeight = 480
5 | cap = cv2.VideoCapture(1)
6 | cap.set(3, frameWidth)
7 | cap.set(4, frameHeight)
8 | cap.set(10,150)
9 |
10 | myColors = [[5,107,0,19,255,255],
11 | [133,56,0,159,156,255],
12 | [57,76,0,100,255,255],
13 | [90,48,0,118,255,255]]
14 | myColorValues = [[51,153,255], ## BGR
15 | [255,0,255],
16 | [0,255,0],
17 | [255,0,0]]
18 |
19 | myPoints = [] ## [x , y , colorId ]
20 |
21 | def findColor(img,myColors,myColorValues):
22 | imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
23 | count = 0
24 | newPoints=[]
25 | for color in myColors:
26 | lower = np.array(color[0:3])
27 | upper = np.array(color[3:6])
28 | mask = cv2.inRange(imgHSV,lower,upper)
29 | x,y=getContours(mask)
30 | cv2.circle(imgResult,(x,y),15,myColorValues[count],cv2.FILLED)
31 | if x!=0 and y!=0:
32 | newPoints.append([x,y,count])
33 | count +=1
34 | #cv2.imshow(str(color[0]),mask)
35 | return newPoints
36 |
37 | def getContours(img):
38 | contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
39 | x,y,w,h = 0,0,0,0
40 | for cnt in contours:
41 | area = cv2.contourArea(cnt)
42 | if area>500:
43 | #cv2.drawContours(imgResult, cnt, -1, (255, 0, 0), 3)
44 | peri = cv2.arcLength(cnt,True)
45 | approx = cv2.approxPolyDP(cnt,0.02*peri,True)
46 | x, y, w, h = cv2.boundingRect(approx)
47 | return x+w//2,y
48 |
49 | def drawOnCanvas(myPoints,myColorValues):
50 | for point in myPoints:
51 | cv2.circle(imgResult, (point[0], point[1]), 10, myColorValues[point[2]], cv2.FILLED)
52 |
53 |
54 | while True:
55 | success, img = cap.read()
56 | imgResult = img.copy()
57 | newPoints = findColor(img, myColors,myColorValues)
58 | if len(newPoints)!=0:
59 | for newP in newPoints:
60 | myPoints.append(newP)
61 | if len(myPoints)!=0:
62 | drawOnCanvas(myPoints,myColorValues)
63 |
64 |
65 | cv2.imshow("Result", imgResult)
66 | if cv2.waitKey(1) & 0xFF == ord('q'):
67 | break
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/project2.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 |
4 |
5 | ###################################
6 | widthImg=540
7 | heightImg =640
8 | #####################################
9 |
10 | cap = cv2.VideoCapture(1)
11 | cap.set(10,150)
12 |
13 |
14 | def preProcessing(img):
15 | imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
16 | imgBlur = cv2.GaussianBlur(imgGray,(5,5),1)
17 | imgCanny = cv2.Canny(imgBlur,200,200)
18 | kernel = np.ones((5,5))
19 | imgDial = cv2.dilate(imgCanny,kernel,iterations=2)
20 | imgThres = cv2.erode(imgDial,kernel,iterations=1)
21 | return imgThres
22 |
23 | def getContours(img):
24 | biggest = np.array([])
25 | maxArea = 0
26 | contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
27 | for cnt in contours:
28 | area = cv2.contourArea(cnt)
29 | if area>5000:
30 | #cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
31 | peri = cv2.arcLength(cnt,True)
32 | approx = cv2.approxPolyDP(cnt,0.02*peri,True)
33 | if area >maxArea and len(approx) == 4:
34 | biggest = approx
35 | maxArea = area
36 | cv2.drawContours(imgContour, biggest, -1, (255, 0, 0), 20)
37 | return biggest
38 |
39 | def reorder (myPoints):
40 | myPoints = myPoints.reshape((4,2))
41 | myPointsNew = np.zeros((4,1,2),np.int32)
42 | add = myPoints.sum(1)
43 | #print("add", add)
44 | myPointsNew[0] = myPoints[np.argmin(add)]
45 | myPointsNew[3] = myPoints[np.argmax(add)]
46 | diff = np.diff(myPoints,axis=1)
47 | myPointsNew[1]= myPoints[np.argmin(diff)]
48 | myPointsNew[2] = myPoints[np.argmax(diff)]
49 | #print("NewPoints",myPointsNew)
50 | return myPointsNew
51 |
52 | def getWarp(img,biggest):
53 | biggest = reorder(biggest)
54 | pts1 = np.float32(biggest)
55 | pts2 = np.float32([[0, 0], [widthImg, 0], [0, heightImg], [widthImg, heightImg]])
56 | matrix = cv2.getPerspectiveTransform(pts1, pts2)
57 | imgOutput = cv2.warpPerspective(img, matrix, (widthImg, heightImg))
58 |
59 | imgCropped = imgOutput[20:imgOutput.shape[0]-20,20:imgOutput.shape[1]-20]
60 | imgCropped = cv2.resize(imgCropped,(widthImg,heightImg))
61 |
62 | return imgCropped
63 |
64 |
65 | def stackImages(scale,imgArray):
66 | rows = len(imgArray)
67 | cols = len(imgArray[0])
68 | rowsAvailable = isinstance(imgArray[0], list)
69 | width = imgArray[0][0].shape[1]
70 | height = imgArray[0][0].shape[0]
71 | if rowsAvailable:
72 | for x in range ( 0, rows):
73 | for y in range(0, cols):
74 | if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
75 | imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
76 | else:
77 | imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
78 | if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
79 | imageBlank = np.zeros((height, width, 3), np.uint8)
80 | hor = [imageBlank]*rows
81 | hor_con = [imageBlank]*rows
82 | for x in range(0, rows):
83 | hor[x] = np.hstack(imgArray[x])
84 | ver = np.vstack(hor)
85 | else:
86 | for x in range(0, rows):
87 | if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
88 | imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
89 | else:
90 | imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
91 | if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
92 | hor= np.hstack(imgArray)
93 | ver = hor
94 | return ver
95 |
96 | while True:
97 | success, img = cap.read()
98 | img = cv2.resize(img,(widthImg,heightImg))
99 | imgContour = img.copy()
100 |
101 | imgThres = preProcessing(img)
102 | biggest = getContours(imgThres)
103 | if biggest.size !=0:
104 | imgWarped=getWarp(img,biggest)
105 | # imageArray = ([img,imgThres],
106 | # [imgContour,imgWarped])
107 | imageArray = ([imgContour, imgWarped])
108 | cv2.imshow("ImageWarped", imgWarped)
109 | else:
110 | # imageArray = ([img, imgThres],
111 | # [img, img])
112 | imageArray = ([imgContour, img])
113 |
114 | stackedImages = stackImages(0.6,imageArray)
115 | cv2.imshow("WorkFlow", stackedImages)
116 |
117 | if cv2.waitKey(1) & 0xFF == ord('q'):
118 | break
--------------------------------------------------------------------------------
/project3.py:
--------------------------------------------------------------------------------
1 | import cv2
2 |
3 | #############################################
4 | frameWidth = 640
5 | frameHeight = 480
6 | nPlateCascade = cv2.CascadeClassifier("Resources/haarcascade_russian_plate_number.xml")
7 | minArea = 200
8 | color = (255,0,255)
9 | ###############################################
10 | cap = cv2.VideoCapture("Resources/video12.mp4")
11 | cap.set(3, frameWidth)
12 | cap.set(4, frameHeight)
13 | cap.set(10,150)
14 | count = 0
15 |
16 | while True:
17 | success, img = cap.read()
18 | imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
19 | numberPlates = nPlateCascade.detectMultiScale(imgGray, 1.1, 10)
20 | for (x, y, w, h) in numberPlates:
21 | area = w*h
22 | if area >minArea:
23 | cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 255), 2)
24 | cv2.putText(img,"Number Plate",(x,y-5),
25 | cv2.FONT_HERSHEY_COMPLEX_SMALL,1,color,2)
26 | imgRoi = img[y:y+h,x:x+w]
27 | cv2.imshow("ROI", imgRoi)
28 |
29 | cv2.imshow("Result", img)
30 |
31 | if cv2.waitKey(1) & 0xFF == ord('s'):
32 | cv2.imwrite("Resources/Scanned/NoPlate_"+str(count)+".jpg",imgRoi)
33 | cv2.rectangle(img,(0,200),(640,300),(0,255,0),cv2.FILLED)
34 | cv2.putText(img,"Scan Saved",(150,265),cv2.FONT_HERSHEY_DUPLEX,
35 | 2,(0,0,255),2)
36 | cv2.imshow("Result",img)
37 | cv2.waitKey(500)
38 | count +=1
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