feat: pad_data 추가

+/juno

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+      <recent name="C:\Users\이준호\Desktop\nomad\dataset\juno\data" />
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+      <method v="2" />
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+      <module name="dataset" />
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+{
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+## Capstone Design: customData
+
+### 🌈 customData 소개
+```
+customData는 0 ~ 9 범위의 숫자로 이루어진 데이터셋 입니다.   
+데이터를 수집한 방법은 다음과 같습니다
+```
+- iPad를 이용하여 `0 ~ 9` 범위의 숫자를 사람들에게 직접 기록을 부탁하였습니다.
+- `25~27살` 남자 5명, `20~24살` 여자 5명에게 데이터를 받았습니다.
+- 받은 데이터를 가공하여 저장하고, 거기에 `blur` `color` `synthesis` 를 적용하였습니다.
+- 현재 숫자당 약 1,200 여개의 데이터가 존재합니다.
+
+<br/>
+
+### 👨🏻‍💻 OpenCV 를 이용한 customDataset 만들기
+1. 배경색 변경하기
+    ```python
+        def coloring(num, source)
+   ```
+   
+2. 이미지에 noise 추가하기 (blur 처리 등)
+    ```python
+        def bluring(num, source)
+    ```
+   
+3. 다른 이미지와 합성하기(원고지 등을 합성하여 배경으로 사용)
+    ```python
+        def synthesis(num, source, background)
+   ```
+<br/>
+
+### 🙆🏻‍♂️ How to use
+- `filename`에 원본 이미지 경로를 기입합니다.
+- 배경 합성시엔, `background`에 원본 배경 이미지 경로를 기입합니다.
+- `change.py` 파일을 실행합니다.
+```python
+    python3 change.py # 를 통해 실행합니다
+```

+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+import os
+
+background = ' ' # put background.png's location
+
+def bluring(num, source):
+    img = cv2.imread(source)
+    kernel = np.ones((5, 5), np.float32)/25
+    blur = cv2.filter2D(img, -1, kernel)
+    cv2.imshow("blur", blur)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    # cv2.imwrite(f"./juno/bluring/{num}/{source.split('/')[4].split('.')[0]}.png", blur)
+
+def coloring(num, source):
+    img = cv2.imread(source)
+    yuv_img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
+
+    cv2.imshow('yuv img', yuv_img)  # color rgb -> bgr
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    # color_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    # cv2.imshow('color img', color_img)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    # cv2.imwrite(f"./juno/coloring/{num}/{source.split('/')[4].split('.')[0]}.png", yuv_img)
+
+def rotate(num, source):
+    image = cv2.imread(source)
+    # 행과 열 정보만 저장합니다.
+    height, width = image.shape[:2]
+
+    M = cv2.getRotationMatrix2D((width / 2, height / 2), 90, 0.5)
+    dst = cv2.warpAffine(image, M, (width, height))
+    result = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
+    cv2.imshow('rotate', result)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    # cv2.imwrite(f"./juno/rotate/{num}/{source.split('/')[4].split('.')[0]}.png", result)
+
+
+def synthesis(num, source, background):
+    background_image = cv2.imread(background)
+    background_resize = cv2.resize(background_image, dsize=(28, 28), interpolation=cv2.INTER_AREA)
+    # image resize 확인
+    cv2.imshow('background_resize', background_resize)
+    cv2.waitKey(0)
+
+    # image load
+    source_image = cv2.imread(source)
+    result = cv2.add(source_image, background_resize)
+    cv2.imshow('synthesis',result)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    # cv2.imwrite(f"./juno/synthesis6/{num}/{source.split('/')[4].split('.')[0]}.png", result)
+
+for i in range(10):
+    file_dir = f"./juno/data/{i}"
+    file_list = os.listdir(file_dir)
+    for file in file_list:
+        pass
+        # coloring(i, f"{file_dir}/{file}")
+        # bluring(i, f"{file_dir}/{file}")
+        # synthesis(i, f"{file_dir}/{file}", background)

+import cv2
+import os
+import numpy as np
+
+filenames = ['13.jpg', '14.jpg', '15.jpg']
+
+def generate_dataset(filename):
+    src = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)  # 그레이 스케일로 변환
+    src = cv2.resize(src, (int(src.shape[1] / 5), int(src.shape[0] / 5)))  # 이미지의 크기가 워낙 크기 때문에 5분의1로 줄여서 확인
+    cv2.imshow('gray', src)
+    k = cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    src = src[0:src.shape[0] - 10, 15:src.shape[1] - 25]  # 양 옆의 노이즈를 제거
+    ret, binary = cv2.threshold(src, 170, 255, cv2.THRESH_BINARY_INV)  # 영상 이진화
+
+    cv2.imshow('binary', binary)
+    k = cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    # binary = cv2.morphologyEx(binary , cv2.MORPH_OPEN , cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2)), iterations = 2)
+    # cv2.imshow('binary',binary)
+    # k = cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # 외곽선 검출
+    color = cv2.cvtColor(binary, cv2.COLOR_GRAY2BGR)  # 이진화 이미지를 color이미지로 복사(확인용)
+    cv2.drawContours(color, contours, -1, (0, 255, 0), 3)  # 초록색으로 외곽선을 그려준다.
+
+    # 리스트연산을 위해 초기변수 선언
+    bR_arr = []
+    digit_arr = []
+    digit_arr2 = []
+    count = 0
+
+    # 검출한 외곽선에 사각형을 그려서 배열에 추가
+    for i in range(len(contours)):
+        bin_tmp = binary.copy()
+        x, y, w, h = cv2.boundingRect(contours[i])
+        bR_arr.append([x, y, w, h])
+
+    bR_arr = sorted(bR_arr, key=lambda num: num[0], reverse=False)
+
+    print(bR_arr[:5])
+    print(len(bR_arr))
+
+    # 작은 노이즈데이터 버림,사각형그리기, 10개씩 리스트로 다시 묶어서 저장
+    for x, y, w, h in bR_arr:
+        tmp_y = bin_tmp[y - 2:y + h + 2, x - 2:x + w + 2].shape[0]
+        tmp_x = bin_tmp[y - 2:y + h + 2, x - 2:x + w + 2].shape[1]
+        if tmp_x and tmp_y > 10:
+            count += 1
+            cv2.rectangle(color, (x - 2, y - 2), (x + w + 2, y + h + 2), (0, 0, 255), 1)
+            digit_arr.append(bin_tmp[y - 2:y + h + 2, x - 2:x + w + 2])
+            if count == 10:
+                digit_arr2.append(digit_arr)
+                digit_arr = []
+                count = 0
+    cv2.imshow('contours', color)
+
+    k = cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    for i in range(0, len(digit_arr2)):
+        for j in range(len(digit_arr2[i])):
+            count += 1
+            if i == 0:  # 1일 경우 비율 유지를 위해 마스크를 만들어 그위에 얹어줌
+                width = digit_arr2[i][j].shape[1]
+                height = digit_arr2[i][j].shape[0]
+                tmp = (height - width) / 2
+                mask = np.zeros((height, height))
+                mask[0:height, int(tmp):int(tmp) + width] = digit_arr2[i][j]
+                digit_arr2[i][j] = cv2.resize(mask, (28, 28))
+            else:
+                digit_arr2[i][j] = cv2.resize(digit_arr2[i][j], (28, 28))
+            # if i == 9 : i = -1
+            cv2.imwrite(f"./juno/data/{i}/" + str(i) + '_' + str(j) + f"_{filename.split('.')[0]}.png", digit_arr2[i][j])
+
+for filename in filenames:
+    generate_dataset(filename)
+
+# def createdataset(directory): #sklearn사용을 위해 데이터세트를 생성
+#     files = os.listdir(directory)
+#     x = []
+#     y = []
+#     for file in files:
+#         attr_x = cv2.imread(directory+file, cv2.IMREAD_GRAYSCALE)
+#         attr_x = attr_x.flatten()
+#         attr_y = file[0]
+#         x.append(attr_x)
+#         y.append(attr_y)
+#
+#         x = np.array(x)
+#         y = np.array(y)
+#         return x , y
+#
+# train_dir = './juno/data/'
+# train_x ,train_y = createdataset(train_dir)
+# test_dir = './juno/test/'
+# test_x , test_y = createdataset(test_dir)