OpenCV 를 활용한 명함인식 기능 구현 강좌대시보드

예제 실습에서 이미지 표시를 해 보면 다음과 같이 python gui 화면이 안 뜨고 응답없음이라고 import cv2def handle_image():imgfile = 'images/sample.png'img = cv2.imread(imgfile, cv2.IMREAD_COLOR) cv2.imshow('image', img) cv2.waitKey(0)cv2.destroyAllWindows()cv2.waitKey(1) if name == '__main__':handle_image()  실습파일 - 실습예제코드(python3.5&opencv3.2)monterey 12.6.6 / conda 23.3.1 / opencv 4.6.0 jupiter notebook 환경이 아닌 일반 python3 로 해당 코드를 실행해도 위와 똑같습니다. 자답)강의자료는 conda install -c menpo opencv 로 해서 opencv 4.6.0 이 설치되는데pip3 install opencv-python 으로 설치하면 opencv 4.7.0 이 설치되고jupiter notebook과 일반 python 환경 모두에서 정상 동작함 pip3 로 설치해도 되는것 맞겠죠?

프로젝트 oxford ai 홈페이지와 서비스가 더이상 유효하지 않습니다.->강의 및 주소 업데이트가 필요합니다. 버전 업데이트로 인한 에러?=> 웹캠을 이용한 인식에서 하기와 같이 오류가 계속 나는데, 솔루션을 알 수 있을까요? Traceback (most recent call last): File "C:\Users\lcw07\PycharmProjects\test_cv2\main.py", line 181, in <module> auto_scan_image_via_webcam() File "C:\Users\lcw07\PycharmProjects\test_cv2\main.py", line 135, in auto_scan_image_via_webcam warped = cv2.warpPerspective(frame, M, (maxWidth, maxHeight))cv2.error: OpenCV(4.6.0) :-1: error: (-5:Bad argument) in function 'warpPerspective'> Overload resolution failed:> - Can't parse 'dsize'. Sequence item with index 0 has a wrong type> - Can't parse 'dsize'. Sequence item with index 0 has a wrong type

    Traceback (most recent call last):   File "D:/001_DataAnalysisTools/pythonProject3/ex_opencv/main.py", line 790, in <module>     auto_scan_image_via_webcam()   File "D:/001_DataAnalysisTools/pythonProject3/ex_opencv/main.py", line 737, in auto_scan_image_via_webcam     if len(screenCnt) == 0: UnboundLocalError: local variable 'screenCnt' referenced before assignment STEP 1: Edge Detection [ WARN:0] global D:\a\opencv-python\opencv-python\opencv\modules\videoio\src\cap_msmf.cpp (438) `anonymous-namespace'::SourceReaderCB::~SourceReaderCB terminating async callback    

 warped = cv2.warpPerspective(orig, M, (maxWidth, maxHeight))-   에서 dsize에러나는데요

안녕하세요?  예제코드를 주피터 노트북에서 실행하면,  윈도우창과 threshold trackbar는 나오는데,  bar의 위치를 옮겨도 threshold값이 반영이 되지않고 처음 윈도우 창 열렸을 때의 흑백이미지 그대로 유지가 됩니다~ ㅠㅠ

images/scannedImage.png only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices 에러가 발생합니다. 최신 PYTHON, OPENCV에서 실행해 보았습니다. 강의내용이 이전꺼라 버전에 맞지는 않습니다.

마지막 부분에 주피터 노트북 부분이 빠르게 넘어가다 보니 미쳐 필기를 다 못해서 컴파일하지 못했습니다.

강의해주신 코드 그대로 입력했지만 ValueError가 떴습니다. 원인과 해결법에 대해서 말씀해주시면 감사하겠습니다 ㅜㅜ

images/scannedImage.png only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices코드는 아래와 같습니다.# (참고) OpenCV - 이미지에서 텍스트 영역만 찾아내기 # 출처: http://www.danvk.org/2015/01/07/finding-blocks-of-text-in-an-image-using-python-opencv-and-numpy.html import glob import os import random import sys import random import math import json from collections import defaultdict import cv2 from PIL import Image, ImageDraw import numpy as np from scipy.ndimage.filters import rank_filter def dilate(ary, N, iterations): """Dilate using an NxN '+' sign shape. ary is np.uint8.""" kernel = np.zeros((N,N), dtype=np.uint8) kernel[(N-1)/2,:] = 1 dilated_image = cv2.dilate(ary / 255, kernel, iterations=iterations) kernel = np.zeros((N,N), dtype=np.uint8) kernel[:,(N-1)/2] = 1 dilated_image = cv2.dilate(dilated_image, kernel, iterations=iterations) dilated_image = cv2.convertScaleAbs(dilated_image) return dilated_image def props_for_contours(contours, ary): """Calculate bounding box & the number of set pixels for each contour.""" c_info = [] for c in contours: x,y,w,h = cv2.boundingRect(c) c_im = np.zeros(ary.shape) cv2.drawContours(c_im, [c], 0, 255, -1) c_info.append({ 'x1': x, 'y1': y, 'x2': x + w - 1, 'y2': y + h - 1, 'sum': np.sum(ary * (c_im > 0))/255 }) return c_info def union_crops(crop1, crop2): """Union two (x1, y1, x2, y2) rects.""" x11, y11, x21, y21 = crop1 x12, y12, x22, y22 = crop2 return min(x11, x12), min(y11, y12), max(x21, x22), max(y21, y22) def intersect_crops(crop1, crop2): x11, y11, x21, y21 = crop1 x12, y12, x22, y22 = crop2 return max(x11, x12), max(y11, y12), min(x21, x22), min(y21, y22) def crop_area(crop): x1, y1, x2, y2 = crop return max(0, x2 - x1) * max(0, y2 - y1) def find_border_components(contours, ary): borders = [] area = ary.shape[0] * ary.shape[1] for i, c in enumerate(contours): x,y,w,h = cv2.boundingRect(c) if w * h > 0.5 * area: borders.append((i, x, y, x + w - 1, y + h - 1)) return borders def angle_from_right(deg): return min(deg % 90, 90 - (deg % 90)) def remove_border(contour, ary): """Remove everything outside a border contour.""" # Use a rotated rectangle (should be a good approximation of a border). # If it's far from a right angle, it's probably two sides of a border and # we should use the bounding box instead. c_im = np.zeros(ary.shape) r = cv2.minAreaRect(contour) degs = r[2] if angle_from_right(degs) <= 10: box = cv2.boxPoints(r) box = np.int0(box) cv2.drawContours(c_im, [box], 0, 255, -1) cv2.drawContours(c_im, [box], 0, 0, 4) else: x1, y1, x2, y2 = cv2.boundingRect(contour) cv2.rectangle(c_im, (x1, y1), (x2, y2), 255, -1) cv2.rectangle(c_im, (x1, y1), (x2, y2), 0, 4) return np.minimum(c_im, ary) def find_components(edges, max_components=16): """Dilate the image until there are just a few connected components. Returns contours for these components.""" # Perform increasingly aggressive dilation until there are just a few # connected components. count = 21 dilation = 5 n = 1 while count > 16: n += 1 dilated_image = dilate(edges, N=3, iterations=n) #_, contours, hierarchy = cv2.findContours(dilated_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours, hierarchy = cv2.findContours(dilated_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) count = len(contours) #print dilation #Image.fromarray(edges).show() #Image.fromarray(255 * dilated_image).show() return contours def find_optimal_components_subset(contours, edges): """Find a crop which strikes a good balance of coverage/compactness. Returns an (x1, y1, x2, y2) tuple. """ c_info = props_for_contours(contours, edges) c_info.sort(key=lambda x: -x['sum']) total = np.sum(edges) / 255 area = edges.shape[0] * edges.shape[1] c = c_info[0] del c_info[0] this_crop = c['x1'], c['y1'], c['x2'], c['y2'] crop = this_crop covered_sum = c['sum'] while covered_sum < total: changed = False recall = 1 * covered_sum / total prec = 1 - 1 * crop_area(crop) / area f1 = 2 * (prec * recall / (prec + recall)) #print '----' for i, c in enumerate(c_info): this_crop = c['x1'], c['y1'], c['x2'], c['y2'] new_crop = union_crops(crop, this_crop) new_sum = covered_sum + c['sum'] new_recall = 1 * new_sum / total new_prec = 1 - 1 * crop_area(new_crop) / area new_f1 = 2 * new_prec * new_recall / (new_prec + new_recall) # Add this crop if it improves f1 score, # _or_ it adds 25% of the remaining pixels for <15% crop expansion. # ^^^ very ad-hoc! make this smoother remaining_frac = c['sum'] / (total - covered_sum) new_area_frac = 1 * crop_area(new_crop) / crop_area(crop) - 1 if new_f1 > f1 or ( remaining_frac > 0.25 and new_area_frac < 0.15): print('%d %s -> %s / %s (%s), %s -> %s / %s (%s), %s -> %s' % ( i, covered_sum, new_sum, total, remaining_frac, crop_area(crop), crop_area(new_crop), area, new_area_frac, f1, new_f1)) crop = new_crop covered_sum = new_sum del c_info[i] changed = True break if not changed: break return crop def pad_crop(crop, contours, edges, border_contour, pad_px=15): """Slightly expand the crop to get full contours. This will expand to include any contours it currently intersects, but will not expand past a border. """ bx1, by1, bx2, by2 = 0, 0, edges.shape[0], edges.shape[1] if border_contour is not None and len(border_contour) > 0: c = props_for_contours([border_contour], edges)[0] bx1, by1, bx2, by2 = c['x1'] + 5, c['y1'] + 5, c['x2'] - 5, c['y2'] - 5 def crop_in_border(crop): x1, y1, x2, y2 = crop x1 = max(x1 - pad_px, bx1) y1 = max(y1 - pad_px, by1) x2 = min(x2 + pad_px, bx2) y2 = min(y2 + pad_px, by2) return crop crop = crop_in_border(crop) c_info = props_for_contours(contours, edges) changed = False for c in c_info: this_crop = c['x1'], c['y1'], c['x2'], c['y2'] this_area = crop_area(this_crop) int_area = crop_area(intersect_crops(crop, this_crop)) new_crop = crop_in_border(union_crops(crop, this_crop)) if 0 < int_area < this_area and crop != new_crop: print('%s -> %s' % (str(crop), str(new_crop))) changed = True crop = new_crop if changed: return pad_crop(crop, contours, edges, border_contour, pad_px) else: return crop def downscale_image(im, max_dim=2048): """Shrink im until its longest dimension is <= max_dim. Returns new_image, scale (where scale <= 1). """ a = im.shape[0] b = im.shape[1] if max(a, b) <= max_dim: return 1, im scale = 1 * max_dim / max(a, b) dim = (int(a * scale), int(b * scale)) new_im = cv2.resize(im, dim, interpolation = cv2.INTER_AREA) return scale, new_im def process_image(path, out_path): orig_im = Image.open(path) im = cv2.imread(path, cv2.IMREAD_GRAYSCALE) scale, im = downscale_image(im) edges = cv2.Canny(im, 100, 200) # TODO: dilate image _before_ finding a border. This is crazy sensitive! #_, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) borders = find_border_components(contours, edges) borders.sort(key=lambda i_x1_y1_x2_y2: (i_x1_y1_x2_y2[3] - i_x1_y1_x2_y2[1]) * (i_x1_y1_x2_y2[4] - i_x1_y1_x2_y2[2])) border_contour = None if len(borders): border_contour = contours[borders[0][0]] edges = remove_border(border_contour, edges) edges = 255 * (edges > 0).astype(np.uint8) # Remove ~1px borders using a rank filter. maxed_rows = rank_filter(edges, -5, size=(1, 20)) maxed_cols = rank_filter(edges, -5, size=(20, 1)) debordered = np.minimum(np.minimum(edges, maxed_rows), maxed_cols) edges = debordered contours = find_components(edges) if len(contours) == 0: print('%s -> (no text!)' % path) return crop = find_optimal_components_subset(contours, edges) crop = pad_crop(crop, contours, edges, border_contour) crop = [int(x / scale) for x in crop] # upscale to the original image size. # draw and show cropped rectangle area in the original image rgb_im = orig_im.convert('RGB') draw = ImageDraw.Draw(rgb_im) draw.rectangle(crop, outline='red') rgb_im.show() text_im = orig_im.crop(crop) text_im.show() text_im.save(out_path) print('%s -> %s' % (path, out_path)) if __name__ == '__main__': # path = 'images/text.jpg' path = 'images/scannedImage.png' out_path = 'croppedImage.png' try: process_image(path, out_path) except Exception as e: print('%s %s' % (path, e))

ERROR conda.core.link:_execute(507): An error occurred while uninstalling package 'defaults::conda-4.5.12-py27_0'.WindowsError(5, '')Attempting to roll back.현재 파이썬이 3.6.8버전도 함께 깔려 있어서 이런 오류가 뜨는 것 같은데 어떻게 해결해야 할까요..?

박스 클릭하고 Ctrl + return 버튼 클릭 위와 같은 결과가 나오는 것을 확인을 실행했을 때 아래와 같이 실습준비설명서와 다른 메시지가 나옵니다. File "", line 12 print "python:", sys.version ^ SyntaxError: Missing parentheses in call to 'print'. Did you mean print("python:", sys.version)?

이미지 읽고 쓰기 (1)의 문장을 제목과 같은 환경에서 사용을 시도하였습니다. 임시로 파일의 이름은 readimage1.py로 바꾼 상태로 터미널에서 사용하였습니다. File "readimage1.py", lin 14 if__name__=='__main__': ^ SyntaxError: invalid syntax</p> 라는 에러가 나와 (:)를 지우면 File "readimage1.py", lin 14 handle_image() ^ SyntaxError: invalid syntax</p> 라는 에러가 나옵니다. 또 아예 가려버리면 창이 뜨지를 않으니 굉장히 곤란한 상황입니다. 비록 강의에서 알려주신 환경은 윈도우였지만 라즈베리파이를 써야하는 상황이라 꼭 알려주시면 감사하겠습니다.

TesseractNotFoundError: tesseract is not installed or it's not in your path이미지로부터 텍스트 추출인데 이렇게 뜨네요 저번에는 잘 됐는데 안됩니다 ㅜ

실습준비 설명서 16페이지 tesseract 링크 에러납니다.마이크로 소프트 oxford 링크 에러납니다.

안녕하세요. 지금 강의를 듣고 있는데 윗부분들은 다 잘 됐는데 tesseract가 안먹어요ㅠㅠ TesseractNotFoundErrorTraceback (most recent call last) in () 13 14 if name == 'main': ---> 15 ocr_tesseract() in ocr_tesseract() 7 image_file = 'images/scannedImage.png' 8 im = Image.open(image_file) ----> 9 text = pytesseract.image_to_string(im) #이미지로부터 텍스트를 추출해 낼 수 있다. 10 im.show() 11 C:UsersLGAnaconda2libsite-packagespytesseractpytesseract.pyc in image_to_string(image, lang, config, nice, boxes, output_type) 284 return run_and_get_output(image, 'txt', lang, config, nice, True) 285 --> 286 return run_and_get_output(image, 'txt', lang, config, nice) 287 288 C:UsersLGAnaconda2libsite-packagespytesseractpytesseract.pyc in run_and_get_output(image, extension, lang, config, nice, return_bytes) 199 return output_file.read().decode('utf-8').strip() 200 except OSError: --> 201 raise TesseractNotFoundError() 202 finally: 203 cleanup(temp_name) TesseractNotFoundError: tesseract is not installed or it's not in your path 이렇게 뜨는데 무슨 문제가 있는 걸까요? 테스트코드에서도 경로 맞아서 테스트도 완료했는데 뭐가 문제인지 모르겠습니다ㅠㅠ tesseract가 안돼서 project oxford로 하려고 했고 키값도 받았는데도 그것도 안먹고요... 인강에서 말씀하신 것 처럼 키값만 넣어주면 되는거 아닌가용? 도와주세여ㅠㅠㅠㅠ

안녕하세요.소스코드를 다운받아서 압축해제하니 아무 것도 없네요.빵집을 사용하는데, 압축해제하려고 더블클릭하면 폴더명이 모두 글자가 깨진채로 구성되어 있긴 합니다.

무엇인지요? 설명에서는 없는데 if __name__ == '__main__': handel_image()