In the learning so far, I was learning from the state where the dataset was given, so I decided to create the dataset myself. I've collected images of men and women, so I created a dataset from them!
MacOS、Python3.6(anaconda)、VSCode
This is an article that I referred to when creating the dataset this time.
[How to create a dataset from an original image](https://intellectual-curiosity.tokyo/2019/07/02/%E3%82%AA%E3%83%AA%E3%82%B8%E3% 83% 8A% E3% 83% AB% E3% 81% AE% E7% 94% BB% E5% 83% 8F% E3% 81% 8B% E3% 82% 89% E3% 83% 87% E3% 83% BC% E3% 82% BF% E3% 82% BB% E3% 83% 83% E3% 83% 88% E3% 82% 92% E4% BD% 9C% E6% 88% 90% E3% 81% 99% E3% 82% 8B% E6% 96% B9 /)
This time I used 4 image folders. Two men and two women.
To describe the flow of creating this dataset in words ① Take out the files in the image folder one by one and grayscale them. (2) Obtain the index number from which folder you extracted when extracting. ③ Change the grayscale image to your favorite size ④ ** Store in the list in the order of [image, index number of the folder containing the image] **
When I wanted to retrieve the values listed in (4), I was surprised because I didn't know that if I prepared two variables in the for statement, I could retrieve the data by skipping one.
for feature, label in training_data:
In addition, I will leave it as a memo from here.
os.listdir(A)
You can display the list of files in A.
os.path.join(A, B)
You can generate a Path that combines A and B.
cv2.imread('image data')
You can convert image data into an array.
enumerate(Variable name)
When fetching with the for statement, it can be fetched with ** "index number element" **
try:
Conditional expression
except:
pass
** Write conditional expression in try + write pass in except = Through even if conditional expression of try comes **
Finally, I couldn't write in Japanese with matplotlib. Japanese display of matplotlib on Mac With reference to this article, I was able to write in Japanese.
import matplotlib as mpl
matplotlib.rcParams[‘font.family] = ‘AppleGothic’
If you write this code, you can write it in Japanese!
This time, I really wanted to cut out only the images of the faces of men and women, but I didn't understand, so for the time being, I created a dataset with the same data.
So, I would like to investigate again to make a data set by including the process of cutting out only the face.
For the time being, I checked if the datasets were separated and it worked. When I made it myself in this way, I was impressed that I was creating a data set like this, and there were many places to study.
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