WEBVTT 0 00:00.230 --> 00:06.050 This might seem a bit of an odd question, but have you ever been to an art museum and looked at the 1 00:06.050 --> 00:13.430 exhibition and thought to yourself, "I think I could probably do this, and I don't really understand why 2 00:13.430 --> 00:14.780 it's considered art." 3 00:15.050 --> 00:21.590 Well, this is a piece of art by Goldschmied and Chiari called, "Where Should We Go Dancing Tonight?" 4 00:21.800 --> 00:25.010 And it's meant to be a piece of concept art, 5 00:25.010 --> 00:31.820 but when the museum cleaners came here at the end of the day and looked at this mess of cigarette butts 6 00:31.820 --> 00:39.290 and shoes and random bottles, they thought it was rubbish, and they promptly cleaned everything up. 7 00:39.290 --> 00:44.090 So this is what the exhibition looked like after the cleaners were done the next day. 8 00:44.090 --> 00:52.430 So to the museum's horror, their star exhibition gets cleared up and mistaken for rubbish. 9 00:52.580 --> 00:58.550 So I was talking to a friend and I said, well, you know, a lot of these modern art pieces, I could 10 00:58.550 --> 00:59.450 probably do it, right? 11 00:59.450 --> 01:04.670 We could probably get together, put some bottles on the floor, make a mess, call it art. 12 01:04.700 --> 01:07.070 And he told me, "Actually, no, you can't. 13 01:07.070 --> 01:10.970 And you should read this book called, the $12 million Stuffed Shark." 14 01:10.970 --> 01:14.090 And it talks all about the Economics of Contemporary Art. 15 01:14.090 --> 01:15.620 And it's a fascinating read. 16 01:15.620 --> 01:22.190 I've learned so much more about how contemporary art is priced, and why they're priced the way they are, 17 01:22.190 --> 01:26.900 but it also led me to discover some pretty big, shocking discoveries. 18 01:26.900 --> 01:33.830 Like, for example, the art piece that's referred to on the cover of the page is this shark. 19 01:33.830 --> 01:39.980 It's a shark that was fished off of the coast of Australia, and then it was preserved in formaldehyde, 20 01:40.070 --> 01:44.000 but it was preserved really badly, like they didn't use enough chemicals, 21 01:44.000 --> 01:50.300 and the shark started going a bit green and a bit of gray in other places, and it also started molding, 22 01:50.300 --> 01:56.120 but this moldy, green shark was bought for $12 million. 23 01:56.120 --> 01:59.390 And this is not even the craziest piece. 24 01:59.390 --> 02:04.910 So, for example, this diamond encrusted skull was reportedly sold for $50 million. 25 02:04.910 --> 02:12.990 And these butterfly wings, which have been dissected and stuck to a canvas, was sold for 8.5 million. 26 02:13.020 --> 02:20.040 So who exactly is this crazy man who's creating all of these pieces of art that's fetching these insane 27 02:20.040 --> 02:21.060 valuations? 28 02:21.450 --> 02:27.390 Well, it's a guy called Damien Hirst, and I think out of all his paintings, the craziest ones are 29 02:27.390 --> 02:36.440 the spot paintings, because it's literally spots of color that are organized randomly on a page. 30 02:36.440 --> 02:38.060 And check this out, 31 02:38.060 --> 02:49.190 this particular piece was sold for a whopping £1.275 million, so maybe like $1.5 million. 32 02:49.340 --> 02:53.390 It's insane for a painting of dots. 33 02:53.390 --> 02:59.240 But what I thought was even crazier is if you look at this painting, at least it's got quite a few 34 02:59.240 --> 03:00.020 dots, right? 35 03:00.050 --> 03:02.360 You're getting your money's worth of dots, 36 03:02.360 --> 03:04.130 but look at this one. 37 03:04.130 --> 03:06.590 This is the same artist, Damien Hirst, 38 03:06.590 --> 03:15.230 and this painting I counted only has 25 spots, and this sold for £0.5 million. 39 03:15.230 --> 03:21.590 It's not even about the number of spots, so I personally still, as you can see, can't really work 40 03:21.590 --> 03:22.550 out contemporary art. 41 03:22.940 --> 03:31.730 But what I can work out is how to get Python code to crack Damien Hirst's colour palette, and use those 42 03:31.730 --> 03:42.710 colours to generate a random canvas of spot paintings that look equally, if not more appealing, than 43 03:42.740 --> 03:44.870 some of the original Damien Hirst's. 44 03:44.870 --> 03:48.740 So this is what we're going to be making as our project. 45 03:48.800 --> 03:55.490 And we're going to be using that Package, colorgram to get hold of the color palette, 46 03:55.490 --> 04:02.450 and then we're going to use those colors to create our own spot painting that's going to look like this. 47 04:02.750 --> 04:11.450 Firstly, we're going to be using a package called colorgram, and colorgram is a library of code written 48 04:11.450 --> 04:14.870 in Python that lets you extract colors from images. 49 04:14.870 --> 04:21.920 So for example, if you take a look at this picture, once it was run through color gram and ten of 50 04:21.920 --> 04:28.580 the most common colors extracted, you end up with a palette that pretty much looks the same as the 51 04:28.580 --> 04:29.840 colors in the image. 52 04:30.200 --> 04:37.970 This is the example of how you would use this colorgram package, and in this case, they're extracting 53 04:37.970 --> 04:39.530 six colors from an image. 54 04:39.530 --> 04:44.900 So it basically takes two inputs an image, and the number of colors. 55 04:45.260 --> 04:51.140 If we go into Google image search and we search for a Hirst spot painting, you can see that there's 56 04:51.140 --> 04:54.200 a lot of spot paintings that this guy created. 57 04:54.230 --> 04:59.030 I want you to pick a painting with a color palette that you like. 58 04:59.150 --> 05:04.640 Maybe something like this, or maybe a bit brighter, like this one. 59 05:04.640 --> 05:09.950 But essentially, pick one of these images and then go ahead and download it. 60 05:09.950 --> 05:16.520 So save image, and then you're going to save that image as image.jpeg. 61 05:16.520 --> 05:21.080 So jpg, and then go ahead and hit Save. 62 05:22.280 --> 05:28.190 Now go ahead and create a new PyCharm project I've called mine hirst-painting. 63 05:28.190 --> 05:30.350 You can call yours whatever you want. 64 05:30.500 --> 05:38.870 Now we're going to drag our newly downloaded image, image.jpg into this project folder, hirst-painting. 65 05:38.870 --> 05:43.790 And it's going to ask you whether, if you want to move the file from, in my case, the downloads folder 66 05:43.790 --> 05:46.250 to my hirst-painting project. 67 05:46.310 --> 05:49.640 So then we're going to agree and click Refactor. 68 05:49.670 --> 05:54.710 So now this painting is inside my hirst-painting project, 69 05:54.710 --> 06:02.090 and I'm ready to go ahead and create a new file called main.py, which is what I'm going to use to extract 70 06:02.090 --> 06:04.310 the colors from this image. 71 06:05.570 --> 06:06.980 Here's a challenge for you. 72 06:07.010 --> 06:14.780 See if you can figure out how to use this package, colorgram, using their project description here and 73 06:14.780 --> 06:16.550 using the example code. 74 06:16.550 --> 06:24.350 What you're aiming for is to be able to print out a list of all the colors extracted from the image, 75 06:24.350 --> 06:29.390 and each item in the list to be a tuple that you create. 76 06:29.390 --> 06:37.220 So remember previously we saw how the turtle module likes to work with color tuples, which are created 77 06:37.220 --> 06:44.660 with an amount of red, the amount of green, and the amount of blue, all contained inside a tuple. 78 06:44.660 --> 06:48.140 This is the format that you're going to try and get your data into. 79 06:48.170 --> 06:50.930 It might take a little bit of wrangling and a little bit of thought. 80 06:51.200 --> 06:53.780 Pause the video and complete this challenge. 81 06:55.370 --> 06:55.940 All right. 82 06:55.940 --> 07:02.280 So we know that in order to use external packages, ones that weren't installed with the Python standard 83 07:02.280 --> 07:06.390 library, we have to first install the package. 84 07:06.720 --> 07:12.900 To install a package, we go to our Preferences and then we select our project, 85 07:12.900 --> 07:15.720 and then we go to our Project Interpreter. 86 07:15.720 --> 07:19.740 Now we click the plus button and we search for colorgram. 87 07:19.740 --> 07:21.420 And there it is. 88 07:21.450 --> 07:24.150 Now let's go ahead and install the package. 89 07:24.150 --> 07:30.060 And once that's installed successfully, then we can close all of this and click Okay. 90 07:30.930 --> 07:34.800 Now that we've got our package we can actually import it. 91 07:34.800 --> 07:36.960 So we'll import colorgram. 92 07:37.110 --> 07:46.410 And we can use the method that's described here called color gram.extract() in order to get hold of 93 07:46.410 --> 07:47.460 six colors. 94 07:47.490 --> 07:53.520 Now, if we want more colors, for example, in this image there's probably a good 30 colors, 95 07:53.520 --> 07:57.870 so let's go ahead and extract 30 colors from this image, 96 07:57.870 --> 08:02.160 but of course our image is not called, sweet_pic.jpg, 97 08:02.190 --> 08:04.800 ours is actually called image.jpg. 98 08:04.800 --> 08:10.110 So let's go ahead and rename this image so that it matches with our file. 99 08:10.110 --> 08:17.160 And remember that your image has to be at the same level as your main.py for this kind of code to work. 100 08:17.160 --> 08:23.010 So make sure that you've got it inside the hirst-painting project folder on the same indentation level 101 08:23.010 --> 08:23.910 as main. 102 08:23.940 --> 08:29.130 Now let's go ahead and print out our colors that are generated. 103 08:29.130 --> 08:33.600 And remember that when you hit Run, this process might take a little while. 104 08:33.600 --> 08:41.280 And the author of the library says that for a 512 * 512 image, it takes about 0.66 of 105 08:41.280 --> 08:42.180 a second. 106 08:42.360 --> 08:45.990 Obviously, if the image is larger, it'll probably take a bit longer. 107 08:46.020 --> 08:50.760 Let's go ahead and hit Run and see what we get. 108 08:51.600 --> 09:00.300 Once the process is finished, you can see it's created some colors and these colors are in a list. 109 09:00.480 --> 09:03.600 And it's got some different formats for the color. 110 09:04.620 --> 09:08.700 To understand that we have to go back to our documentation. 111 09:08.700 --> 09:14.580 You can see that the color that's extracted can either be a RGB color, which is what we're interested 112 09:14.580 --> 09:19.080 in, or a HSL color, which is not quite what we want. 113 09:19.080 --> 09:25.770 So let's go ahead and write a for loop that taps into each of those colors. 114 09:28.260 --> 09:37.830 So 'for color in colors:' let's go ahead and create a new list called rgb_colors, 115 09:39.060 --> 09:41.730 and we'll just leave it as an empty list. 116 09:41.730 --> 09:49.890 And then for each of these colors, we're going to add to our rgb_colors by appending. 117 09:49.890 --> 09:54.450 And the thing we're going to append() is each of these individual color objects, 118 09:54.450 --> 09:57.270 and then we're going to get the rgb value. 119 09:57.690 --> 10:04.260 And once this process is done then let's go ahead and print our rgb_colors. 120 10:05.070 --> 10:11.610 So now you can see we've got a whole bunch of rgb_colors with r equals what, g equals what, and b equals 121 10:11.610 --> 10:11.940 what, 122 10:11.940 --> 10:14.940 but this is not quite the format that we need it. 123 10:14.940 --> 10:20.340 In order to use it inside the turtle, we actually have to go one step further, 124 10:20.340 --> 10:29.730 we have to create an r, which is from the color.rgb.r. 125 10:29.880 --> 10:34.230 And then we repeat this process for green. 126 10:35.370 --> 10:37.050 and blue. 127 10:39.060 --> 10:48.270 And then we create our tuple by creating our new_color, which is going to be equal to a tuple with 128 10:48.270 --> 10:49.500 a parentheses. 129 10:49.500 --> 10:52.740 And then r, g, and b. 130 10:53.400 --> 11:00.210 Finally, we're going to add to our rgb colors by appending this new_color. 131 11:00.360 --> 11:04.170 Now let's print our rgb colors, 132 11:08.940 --> 11:16.230 and you can see we've finally got it in the format that we need, a tuple with the r value, g value, 133 11:16.230 --> 11:20.460 and b value that we can use in our project. 134 11:20.880 --> 11:27.630 Now we've managed to get the first part of the problem solved, so we can go ahead and copy this entire 135 11:27.630 --> 11:32.640 list, and paste it into our main.py. 136 11:32.970 --> 11:39.630 I recommend actually testing out these colors using the W3 schools, RGB tool. 137 11:39.660 --> 11:44.670 Go ahead and paste your color to replace this current tuple here. 138 11:44.670 --> 11:50.610 Make sure that you've still got that word rgb there, and then hit Enter and it will insert each of 139 11:50.610 --> 11:53.100 these values into the r, g, and b. 140 11:53.100 --> 11:56.760 So you can see the first color we got is actually a shade of white. 141 11:56.760 --> 11:59.070 So it probably one of the backgrounds. 142 11:59.160 --> 12:01.980 So we can go ahead and delete that one, 143 12:01.980 --> 12:04.710 because we're not going to be painting the background in our dot, 144 12:04.710 --> 12:06.000 it's just going to be white. 145 12:06.000 --> 12:08.040 Now let's check the next one. 146 12:08.040 --> 12:15.270 As you can see, the closer the numbers are to 255, the more likely it is that it's a shade of white. 147 12:15.270 --> 12:20.070 So this one is probably one of the other background colors in this image. 148 12:20.070 --> 12:22.230 So we can delete that one as well. 149 12:22.230 --> 12:27.480 And finally, this last one is probably going to be also quite whitish, 150 12:27.480 --> 12:29.970 and we can probably delete that one too. 151 12:30.030 --> 12:34.290 This one because of the blue that's 109, 152 12:34.290 --> 12:38.640 I'm going to be pretty sure that this is actually a real color, 153 12:38.640 --> 12:39.450 and there we go. 154 12:39.450 --> 12:45.570 We get this kind of sand color, which looks very similar to some of these colors that we see on here. 155 12:45.570 --> 12:51.360 So we can presume that the rest of these, because they're sampled by the frequency of occurrence, 156 12:51.360 --> 12:55.290 are probably going to be real colors that we can put into our painting. 157 12:55.290 --> 12:59.280 Let's save this list as our color_list. 158 13:01.530 --> 13:08.340 And we can comment out the rest of this code, because we don't need to run this computation every single 159 13:08.340 --> 13:08.820 time, 160 13:08.820 --> 13:11.070 we just want to extract the colors, 161 13:11.070 --> 13:15.960 and then you can delete or comment out the colorgram-related code. 162 13:16.530 --> 13:22.170 In the next lesson, we're going to be using this color_list to create our hirst-painting. 163 13:22.170 --> 13:24.840 So for all of that and more, I'll see you there.