import cv2 import numpy as np import os import pandas as pd import pytesseract import re import textdistance import datetime from datetime import date from operator import itemgetter, attrgetter ROOT_PATH = os.getcwd() LINE_REC_PATH = os.path.join(ROOT_PATH, 'data/ID_CARD_KEYWORDS.csv') RELIGION_REC_PATH = os.path.join(ROOT_PATH, 'data/RELIGIONS.csv') JENIS_KELAMIN_REC_PATH = os.path.join(ROOT_PATH, 'data/JENIS_KELAMIN.csv') NEED_COLON = [3, 4, 6, 8, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21] NEXT_LINE = 9 ID_NUMBER = 3 def convertScale(img, alpha, beta): new_img = img * alpha + beta new_img[new_img < 0] = 0 new_img[new_img > 255] = 255 return new_img.astype(np.uint8) def automatic_brightness_and_contrast(image, clip_hist_percent=10): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Calculate grayscale histogram hist = cv2.calcHist([gray],[0],None,[256],[0,256]) hist_size = len(hist) # Calculate cumulative distribution from the histogram accumulator = [] accumulator.append(float(hist[0])) for index in range(1, hist_size): accumulator.append(accumulator[index -1] + float(hist[index])) # Locate points to clip maximum = accumulator[-1] clip_hist_percent *= (maximum/100.0) clip_hist_percent /= 2.0 # Locate left cut minimum_gray = 0 while accumulator[minimum_gray] < clip_hist_percent: minimum_gray += 1 # Locate right cut maximum_gray = hist_size -1 while accumulator[maximum_gray] >= (maximum - clip_hist_percent): maximum_gray -= 1 # Calculate alpha and beta values alpha = 255 / (maximum_gray - minimum_gray) beta = -minimum_gray * alpha auto_result = convertScale(image, alpha=alpha, beta=beta) # auto_result = cv2.convertScaleAbs(image, alpha=alpha, beta=beta) return auto_result # ------------------------------------------------------------------------------ def ocr_raw(image): # img_raw = cv2.imread(image_path) # image = automatic_brightness_and_contrast(image) image = cv2.resize(image, (50 * 16, 500)) # cv2.imshow("test1", image) # image = automatic_brightness_and_contrast(image) img_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) # img_gray = cv2.equalizeHist(img_gray) # img_gray = cv2.fastNlMeansDenoising(img_gray, None, 3, 7, 21) rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 7)) sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 21)) # smooth the image using a 3x3 Gaussian blur and then apply a # blackhat morpholigical operator to find dark regions on a light # background gray = cv2.GaussianBlur(img_gray, (3, 3), 0) blackhat = cv2.morphologyEx(img_gray, cv2.MORPH_BLACKHAT, rectKernel) id_number = return_id_number(image, blackhat) if id_number == "": raise Exception("KTP tidak terdeteksi") cv2.fillPoly(blackhat, pts=[np.asarray([(550, 150), (550, 499), (798, 499), (798, 150)])], color=(255, 255, 255)) th, threshed = cv2.threshold(blackhat, 130, 255, cv2.THRESH_TRUNC) pytesseract.pytesseract.tesseract_cmd = r'tesseract' result_raw = pytesseract.image_to_string(threshed, lang="ind", config='--psm 4 --oem 3') print(result_raw) return result_raw, id_number def strip_op(result_raw): result_list = result_raw.split('\n') new_result_list = [] for tmp_result in result_list: if tmp_result.strip(' '): new_result_list.append(tmp_result) return new_result_list def sort_contours(cnts, method="left-to-right"): reverse = False i = 0 if method == "right-to-left" or method == "bottom-to-top": reverse = True if method == "top-to-bottom" or method == "bottom-to-top": i = 1 boundingBoxes = [cv2.boundingRect(c) for c in cnts] (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), key=lambda b: b[1][i], reverse=reverse)) return cnts, boundingBoxes def return_id_number(image, img_gray): rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15)) tophat = cv2.morphologyEx(img_gray, cv2.MORPH_TOPHAT, rectKernel) gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1) gradX = np.absolute(gradX) (minVal, maxVal) = (np.min(gradX), np.max(gradX)) gradX = (255 * ((gradX - minVal) / (maxVal - minVal))) gradX = gradX.astype("uint8") gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) thresh = cv2.threshold(gradX, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, rectKernel) threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = threshCnts cur_img = image.copy() cv2.drawContours(cur_img, cnts, -1, (0, 0, 255), 3) copy = image.copy() locs = [] for (i, c) in enumerate(cnts): (x, y, w, h) = cv2.boundingRect(c) # ar = w / float(h) # if ar > 3: # if (w > 40 ) and (h > 10 and h < 20): if h > 10 and w > 100 and x < 300: img = cv2.rectangle(copy, (x, y), (x + w, y + h), (0, 255, 0), 2) locs.append((x, y, w, h, w * h)) locs = sorted(locs, key=itemgetter(1), reverse=False) # nik = image[locs[1][1] - 15:locs[1][1] + locs[1][3] + 15, locs[1][0] - 15:locs[1][0] + locs[1][2] + 15] # text = image[locs[2][1] - 10:locs[2][1] + locs[2][3] + 10, locs[2][0] - 10:locs[2][0] + locs[2][2] + 10] check_nik = False try: nik = image[locs[1][1] - 15:locs[1][1] + locs[1][3] + 15, locs[1][0] - 15:locs[1][0] + locs[1][2] + 15] check_nik = True except Exception as e: print(e) return "" if check_nik == True: img_mod = cv2.imread("data/module2.png") ref = cv2.cvtColor(img_mod, cv2.COLOR_BGR2GRAY) ref = cv2.threshold(ref, 66, 255, cv2.THRESH_BINARY_INV)[1] refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) refCnts = sort_contours(refCnts, method="left-to-right")[0] digits = {} for (i, c) in enumerate(refCnts): (x, y, w, h) = cv2.boundingRect(c) roi = ref[y:y + h, x:x + w] roi = cv2.resize(roi, (57, 88)) digits[i] = roi gray_nik = cv2.cvtColor(nik, cv2.COLOR_BGR2GRAY) group = cv2.threshold(gray_nik, 127, 255, cv2.THRESH_BINARY_INV)[1] digitCnts, hierarchy_nik = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) nik_r = nik.copy() cv2.drawContours(nik_r, digitCnts, -1, (0, 0, 255), 3) gX = locs[1][0] gY = locs[1][1] gW = locs[1][2] gH = locs[1][3] ctx = sort_contours(digitCnts, method="left-to-right")[0] locs_x = [] for (i, c) in enumerate(ctx): (x, y, w, h) = cv2.boundingRect(c) if h > 10 and w > 10: img = cv2.rectangle(nik_r, (x, y), (x + w, y + h), (0, 255, 0), 2) locs_x.append((x, y, w, h)) output = [] groupOutput = [] for c in locs_x: (x, y, w, h) = c roi = group[y:y + h, x:x + w] roi = cv2.resize(roi, (57, 88)) scores = [] for (digit, digitROI) in digits.items(): result = cv2.matchTemplate(roi, digitROI, cv2.TM_CCOEFF) (_, score, _, _) = cv2.minMaxLoc(result) scores.append(score) groupOutput.append(str(np.argmax(scores))) cv2.rectangle(image, (gX - 5, gY - 5), (gX + gW + 5, gY + gH + 5), (0, 0, 255), 1) cv2.putText(image, "".join(groupOutput), (gX, gY - 15), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) output.extend(groupOutput) return ''.join(output) else: return "" def main(image): raw_df = pd.read_csv(LINE_REC_PATH, header=None) religion_df = pd.read_csv(RELIGION_REC_PATH, header=None) jenis_kelamin_df = pd.read_csv(JENIS_KELAMIN_REC_PATH, header=None) result_raw, id_number = ocr_raw(image) result_list = strip_op(result_raw) provinsi = "" kabupaten = "" nik = "" nama = "" tempat_lahir = "" tgl_lahir = "" jenis_kelamin = "" alamat = "" status_perkawinan = "" agama = "" rt_rw = "" kel_desa = "" kecamatan = "" pekerjaan = "" kewarganegaraan = "" # print("NIK: " + str(id_number)) loc2index = dict() for i, tmp_line in enumerate(result_list): for j, tmp_word in enumerate(tmp_line.split(' ')): tmp_sim_list = [textdistance.damerau_levenshtein.normalized_similarity(tmp_word_, tmp_word.strip(':')) for tmp_word_ in raw_df[0].values] tmp_sim_np = np.asarray(tmp_sim_list) arg_max = np.argmax(tmp_sim_np) if tmp_sim_np[arg_max] >= 0.6: loc2index[(i, j)] = arg_max last_result_list = [] useful_info = False for i, tmp_line in enumerate(result_list): tmp_list = [] for j, tmp_word in enumerate(tmp_line.split(' ')): tmp_word = tmp_word.strip(':') if(i, j) in loc2index: useful_info = True if loc2index[(i, j)] == NEXT_LINE: last_result_list.append(tmp_list) tmp_list = [] tmp_list.append(raw_df[0].values[loc2index[(i, j)]]) if loc2index[(i, j)] in NEED_COLON: tmp_list.append(':') elif tmp_word == ':' or tmp_word =='': continue else: tmp_list.append(tmp_word) if useful_info: if len(last_result_list) > 2 and ':' not in tmp_list: last_result_list[-1].extend(tmp_list) else: last_result_list.append(tmp_list) for tmp_data in last_result_list: if '—' in tmp_data: tmp_data.remove('—') if 'PROVINSI' in tmp_data: provinsi = ' '.join(tmp_data[1:]) provinsi = re.sub('[^A-Z. ]', '', provinsi) if len(provinsi.split()) == 1: provinsi = re.sub('[^A-Z.]', '', provinsi) if 'KABUPATEN' in tmp_data or 'KOTA' in tmp_data: kabupaten = ' '.join(tmp_data[1:]) kabupaten = re.sub('[^A-Z. ]', '', kabupaten) if len(kabupaten.split()) == 1: kabupaten = re.sub('[^A-Z.]', '', kabupaten) if 'Nama' in tmp_data: nama = ' '.join(tmp_data[2:]) nama = re.sub('[^A-Z. ]', '', nama) if len(nama.split()) == 1: nama = re.sub('[^A-Z.]', '', nama) if 'NIK' in tmp_data: if len(id_number) != 16: # id_number = tmp_data[2] if "D" in id_number: id_number = id_number.replace("D", "0") if "?" in id_number: id_number = id_number.replace("?", "7") if "L" in id_number: id_number = id_number.replace("L", "1") while len(tmp_data) > 2: tmp_data.pop() tmp_data.append(id_number) else: while len(tmp_data) > 3: tmp_data.pop() if len(tmp_data) < 3: tmp_data.append(id_number) tmp_data[2] = id_number if 'Agama' in tmp_data: for tmp_index, tmp_word in enumerate(tmp_data[1:]): tmp_sim_list = [textdistance.damerau_levenshtein.normalized_similarity(tmp_word, tmp_word_) for tmp_word_ in religion_df[0].values] tmp_sim_np = np.asarray(tmp_sim_list) arg_max = np.argmax(tmp_sim_np) print(tmp_sim_np[arg_max]) if tmp_sim_np[arg_max] >= 0.6: tmp_data[tmp_index + 1] = religion_df[0].values[arg_max] agama = tmp_data[tmp_index + 1] if 'Status' in tmp_data or 'Perkawinan' in tmp_data: try: status_perkawinan = ' '.join(tmp_data[2:]) status_perkawinan = re.findall('\s+([A-Za-z]+)', status_perkawinan) status_perkawinan = ' '.join(status_perkawinan) except: status_perkawinan = "" if 'Alamat' in tmp_data: for tmp_index in range(len(tmp_data)): if "!" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("!", "I") if "1" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("1", "I") if "i" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("i", "I") alamat = ' '.join(tmp_data[1:]) alamat = re.sub('[^A-Z0-9. ]', '', alamat).strip() if len(alamat.split()) == 1: alamat = re.sub('[^A-Z0-9.]', '', alamat).strip() if 'RT/RW' in tmp_data: for tmp_index in range(len(tmp_data)): if "!" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("!", "1") if "i" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("i", "1") rt_rw = ' '.join(tmp_data[1:]) rt_rw = re.search(r'\d{3}/\d{3}', rt_rw).group() if 'Kel/Desa' in tmp_data: for tmp_index in range(len(tmp_data)): if "!" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("!", "I") if "1" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("1", "I") if "i" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("i", "I") kel_desa = ' '.join(tmp_data[1:]) kel_desa = re.sub('[^A-Z0-9. ]', '', kel_desa).strip() if len(kel_desa.split()) == 1: kel_desa = re.sub('[^A-Z0-9.]', '', kel_desa).strip() if 'Kecamatan' in tmp_data: for tmp_index in range(len(tmp_data)): if "!" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("!", "I") if "1" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("1", "I") if "i" in tmp_data[tmp_index]: tmp_data[tmp_index] = tmp_data[tmp_index].replace("i", "I") kecamatan = ' '.join(tmp_data[1:]) kecamatan = re.sub('[^A-Z0-9. ]', '', kecamatan).strip() if len(kecamatan.split()) == 1: kecamatan = re.sub('[^A-Z0-9.]', '', kecamatan).strip() if 'Jenis' in tmp_data or 'Kelamin' in tmp_data: for tmp_index, tmp_word in enumerate(tmp_data[2:]): tmp_sim_list = [textdistance.damerau_levenshtein.normalized_similarity(tmp_word, tmp_word_) for tmp_word_ in jenis_kelamin_df[0].values] tmp_sim_np = np.asarray(tmp_sim_list) arg_max = np.argmax(tmp_sim_np) if tmp_sim_np[arg_max] >= 0.6: tmp_data[tmp_index + 2] = jenis_kelamin_df[0].values[arg_max] jenis_kelamin = tmp_data[tmp_index + 2] if 'Pekerjaan' in tmp_data: pekerjaan = ' '.join(tmp_data[2:]) pekerjaan = re.sub('[^A-Za-z./ ]', '', pekerjaan) if len(pekerjaan.split()) == 1: pekerjaan = re.sub('[^A-Za-z./]', '', pekerjaan) if 'Kewarganegaraan' in tmp_data: kewarganegaraan = ' '.join(tmp_data[2:]) kewarganegaraan = re.sub('[^A-Z. ]', '', kewarganegaraan) if len(kewarganegaraan.split()) == 1: kewarganegaraan = re.sub('[^A-Z.]', '', kewarganegaraan) if 'Tempat' in tmp_data or 'Tgl' in tmp_data or 'Lahir' in tmp_data: join_tmp = ' '.join(tmp_data) match_tgl1 = re.search("([0-9]{2}—[0-9]{2}—[0-9]{4})", join_tmp) match_tgl2 = re.search("([0-9]{2}\ [0-9]{2}\ [0-9]{4})", join_tmp) match_tgl3 = re.search("([0-9]{2}\-[0-9]{2}\ [0-9]{4})", join_tmp) match_tgl4 = re.search("([0-9]{2}\ [0-9]{2}\-[0-9]{4})", join_tmp) match_tgl5 = re.search("([0-9]{2}-[0-9]{2}-[0-9]{4})", join_tmp) match_tgl6 = re.search("([0-9]{2}\-[0-9]{2}\-[0-9]{4})", join_tmp) if match_tgl1: try: tgl_lahir = datetime.datetime.strptime(match_tgl1.group(), '%d—%m—%Y').date() tgl_lahir = tgl_lahir.strftime('%d-%m-%Y') except: tgl_lahir = "" elif match_tgl2: try: tgl_lahir = datetime.datetime.strptime(match_tgl2.group(), '%d %m %Y').date() tgl_lahir = tgl_lahir.strftime('%d-%m-%Y') except: tgl_lahir = "" elif match_tgl3: try: tgl_lahir = datetime.datetime.strptime(match_tgl3.group(), '%d-%m %Y').date() tgl_lahir = tgl_lahir.strftime('%d-%m-%Y') except: tgl_lahir = "" elif match_tgl4: try: tgl_lahir = datetime.datetime.strptime(match_tgl4.group(), '%d %m-%Y').date() tgl_lahir = tgl_lahir.strftime('%d-%m-%Y') except: tgl_lahir = "" elif match_tgl5: try: tgl_lahir = datetime.datetime.strptime(match_tgl5.group(), '%d-%m-%Y').date() tgl_lahir = tgl_lahir.strftime('%d-%m-%Y') except: tgl_lahir = "" elif match_tgl6: try: tgl_lahir = datetime.datetime.strptime(match_tgl6.group(), '%d-%m-%Y').date() tgl_lahir = tgl_lahir.strftime('%d-%m-%Y') except: tgl_lahir = "" else: tgl_lahir = "" try: tempat_lahir = ' '.join(tmp_data[2:]) tempat_lahir = re.findall("[A-Z\s]", tempat_lahir) tempat_lahir = ''.join(tempat_lahir).strip() except: tempat_lahir = "" # for tmp_data in last_result_list: # print(' '.join(tmp_data)) return (id_number, nama, tempat_lahir, tgl_lahir, jenis_kelamin, agama, status_perkawinan, provinsi, kabupaten, alamat, rt_rw, kel_desa, kecamatan, pekerjaan, kewarganegaraan) if __name__ == '__main__': main(sys.argv[1])