added black to format code

backend/main.py

@@ -6,24 +6,23 @@
 # def hello_world():
 #     return "<p>asdasd!</p>"
 
-
 #: flask to get data from frontend (assume we have data for now) (last thing we build in backend)
 #: OCR or Pytesseract for extracting text from image
 #: Pillow library to edit on the images
 #: googletrans to translate the text from language 1 to English
-#: 
+#:
 
 import cv2
 import pytesseract
 from googletrans import Translator
 from manga_ocr import MangaOcr
 from PIL import Image
 
-MOCKIMAGEPATH = 'japanese.png'
+MOCKIMAGEPATH = "japanese.png"
 mockImage = cv2.imread(MOCKIMAGEPATH)
-pytesseract_config = r'--oem 3 --psm 5 -l jpn_vert'
+pytesseract_config = r"--oem 3 --psm 5 -l jpn_vert"
 
-grayscale = cv2.cvtColor(mockImage, cv2.COLOR_BGR2GRAY) # convert image to grayscale
+grayscale = cv2.cvtColor(mockImage, cv2.COLOR_BGR2GRAY)  # convert image to grayscale
 blur = cv2.GaussianBlur(grayscale, (9, 9), 0)
 # threshold = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
 
@@ -33,47 +32,48 @@
 # invert = 255 - opening
 img = cv2.imread(MOCKIMAGEPATH)
 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- 
+
 # Performing OTSU threshold
 ret, thresh1 = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)
- 
+
 # Specify structure shape and kernel size.
 # Kernel size increases or decreases the area
 # of the rectangle to be detected.
 # A smaller value like (10, 10) will detect
 # each word instead of a sentence.
 rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (18, 18))
- 
+
 # Applying dilation on the threshold image
-dilation = cv2.dilate(thresh1, rect_kernel, iterations = 1)
-cv2.imshow('dilation ', dilation)
- 
+dilation = cv2.dilate(thresh1, rect_kernel, iterations=1)
+cv2.imshow("dilation ", dilation)
+
 # Finding contours
-contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL,
-                                                 cv2.CHAIN_APPROX_NONE)
-
+contours, hierarchy = cv2.findContours(
+    dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
+)
+
 # Creating a copy of image
 im2 = img.copy()
- 
+
 # A text file is created and flushed
- 
+
 # Looping through the identified contours
 # Then rectangular part is cropped and passed on
 # to pytesseract for extracting text from it
 # Extracted text is then written into the text file
 for cnt in contours:
     x, y, w, h = cv2.boundingRect(cnt)
-     
+
     # Drawing a rectangle on copied image
     rect = cv2.rectangle(im2, (x, y), (x + w, y + h), (0, 255, 0), 2)
-    cv2.imshow('rectangle is ', rect)
-     
+    cv2.imshow("rectangle is ", rect)
+
     # Cropping the text block for giving input to OCR
-    cropped = im2[y:y + h, x:x + w]
-     
+    cropped = im2[y : y + h, x : x + w]
+
     # Open the file in append mode
     file = open("recognized.txt", "a")
-     
+
     # Apply OCR on the cropped image
     text = pytesseract.image_to_string(cropped)
     print(text)
@@ -92,4 +92,4 @@
 # translator = Translator()
 # translatedText = translator.translate(jp_text, dest="en", src="ja")
 
-# print('[translated]: ', translatedText.text)
+# print('[translated]: ', translatedText.text)

backend/requirements.txt

@@ -5,3 +5,4 @@ numpy
 googletrans==4.0.0-rc1
 opencv-python
 manga-ocr
+black