import sys import json import cv2 import numpy as np import pytesseract import re def preprocess_image(image_path): image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) _, binary = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) # コントラスト強調 image = cv2.equalizeHist(image) # ガウシアンブラーでノイズ除去 image = cv2.GaussianBlur(image, (3, 3), 0) horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1)) detected_lines = cv2.morphologyEx(binary, cv2.MORPH_OPEN, horizontal_kernel, iterations=2) binary = cv2.subtract(binary, detected_lines) vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 40)) detected_lines = cv2.morphologyEx(binary, cv2.MORPH_OPEN, vertical_kernel, iterations=2) binary = cv2.subtract(binary, detected_lines) temp_path = "/tmp/processed_image.png" cv2.imwrite(temp_path, binary) return temp_path def correct_six_misrecognition(text): corrected_text = re.sub(r'(?<=\d)e(?=\d)', '6', text) corrected_text = re.sub(r'-e', '-6', corrected_text) corrected_text = corrected_text.replace('ー', '-') # OCRの'ー'を'-'に変換 corrected_text = re.sub(r'(?<=\d)z(?=\d)', '2', corrected_text) # 数値に挟まれた'z'を'2'に変換 return corrected_text def clean_numeric(cell): return re.sub(r'[^0-9\.-]', '', cell) def merge_split_numbers(cells): """ -69022. 285 のように、数値が分割されている場合に結合する。 """ merged_cells = [] i = 0 while i < len(cells): if i < len(cells) - 1 and re.match(r'-?\d+\.$', cells[i]) and re.match(r'\d+', cells[i + 1]): merged_cells.append(cells[i] + cells[i + 1]) i += 2 else: merged_cells.append(cells[i]) i += 1 return merged_cells def extract_text_from_image(image_path): processed_image = preprocess_image(image_path) if processed_image is None: return {"success": False, "error": "Failed to process image"} ocr_result = pytesseract.image_to_string(processed_image, config="--oem 1 --psm 6 --dpi 1000", lang="jpn") ocr_text = correct_six_misrecognition(ocr_result.strip().replace('|', '')) lines = [line for line in ocr_text.split('\n') if line] structured_data = [] chiban_data = "" if lines: first_line_words = lines[0].split() if first_line_words: chiban_data = first_line_words[-1] # 最後の単語を抽出 for line in lines: cells = line.split() cleaned_cells = [clean_numeric(cell) for cell in cells if clean_numeric(cell)] merged_cells = merge_split_numbers(cleaned_cells) col1 = next((cell for cell in cells if not re.match(r'-?\d+\.\d+', cell)), '9') col2, col3, col4 = None, None, None for cell in merged_cells: if len(cell) >= 5 and re.match(r'-?\d+\.\d+', cell): # 5桁以上の数値、小数点を考慮 if col2 is None: col2 = cell elif col3 is None: col3 = cell elif col4 is None: col4 = cell break if col2 and col3 and col4: # 3つの数値がそろった行のみを出力 structured_data.append([ col1, col2, col3, col4 ]) return {"success": True, "raw_output": ocr_text, "structured_data": structured_data, "chiban_data": chiban_data} if __name__ == "__main__": if len(sys.argv) < 2: print(json.dumps({"success": False, "error": "Missing image path"})) sys.exit(1) image_path = sys.argv[1] result = extract_text_from_image(image_path) print(json.dumps(result, ensure_ascii=False, indent=2)) # import sys # import json # import cv2 # import numpy as np # import pytesseract # import re # def preprocess_image(image_path): # image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) # _, binary = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) # # コントラスト強調 # image = cv2.equalizeHist(image) # # ガウシアンブラーでノイズ除去 # image = cv2.GaussianBlur(image, (3, 3), 0) # horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1)) # detected_lines = cv2.morphologyEx(binary, cv2.MORPH_OPEN, horizontal_kernel, iterations=2) # binary = cv2.subtract(binary, detected_lines) # vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 40)) # detected_lines = cv2.morphologyEx(binary, cv2.MORPH_OPEN, vertical_kernel, iterations=2) # binary = cv2.subtract(binary, detected_lines) # temp_path = "/tmp/processed_image.png" # cv2.imwrite(temp_path, binary) # return temp_path # def correct_six_misrecognition(text): # corrected_text = re.sub(r'(?<=\d)e(?=\d)', '6', text) # corrected_text = re.sub(r'-e', '-6', corrected_text) # corrected_text = corrected_text.replace('ー', '-') # OCRの'ー'を'-'に変換 # corrected_text = re.sub(r'(?<=\d)z(?=\d)', '2', corrected_text) # 数値に挟まれた'z'を'2'に変換 # return corrected_text # def clean_numeric(cell): # return re.sub(r'[^0-9\.-]', '', cell) # def merge_split_numbers(cells): # """ # -69022. 285 のように、数値が分割されている場合に結合する。 # """ # merged_cells = [] # i = 0 # while i < len(cells): # if i < len(cells) - 1 and re.match(r'-?\d+\.$', cells[i]) and re.match(r'\d+', cells[i + 1]): # merged_cells.append(cells[i] + cells[i + 1]) # i += 2 # else: # merged_cells.append(cells[i]) # i += 1 # return merged_cells # def extract_text_from_image(image_path): # processed_image = preprocess_image(image_path) # if processed_image is None: # return {"success": False, "error": "Failed to process image"} # ocr_result = pytesseract.image_to_string(processed_image, config="--oem 1 --psm 6 --dpi 1000", lang="jpn") # ocr_text = correct_six_misrecognition(ocr_result.strip().replace('|', '')) # lines = [line for line in ocr_text.split('\n') if line] # structured_data = [] # chiban_data = "" # if lines: # first_line_words = lines[0].split() # if first_line_words: # chiban_data = first_line_words[-1] # 最後の単語を抽出 # for line in lines: # cells = line.split() # cleaned_cells = [clean_numeric(cell) for cell in cells if clean_numeric(cell)] # merged_cells = merge_split_numbers(cleaned_cells) # col1 = next((cell for cell in cells if not re.match(r'-?\d+\.\d+', cell)), '9') # col2, col3 = None, None # for cell in merged_cells: # if len(cell) >= 5 and re.match(r'-?\d+\.\d+', cell): # 5桁以上の数値、小数点を考慮 # if col2 is None: # col2 = cell # elif col3 is None: # col3 = cell # else: # break # if col2 and col3: # 両方セットできた行のみを出力 # structured_data.append([ # col1, # col2, # col3 # ]) # return {"success": True, "raw_output": ocr_text, "structured_data": structured_data, "chiban_data": chiban_data} # if __name__ == "__main__": # if len(sys.argv) < 2: # print(json.dumps({"success": False, "error": "Missing image path"})) # sys.exit(1) # image_path = sys.argv[1] # result = extract_text_from_image(image_path) # print(json.dumps(result, ensure_ascii=False, indent=2))