From d5c694ac711ca3b434bf16bd920b90d1a7e758c4 Mon Sep 17 00:00:00 2001 From: Samo Penic <samo.penic@gmail.com> Date: Sat, 17 Nov 2018 09:57:31 +0000 Subject: [PATCH] Improving the robustness of all three algorithms. --- aoiOcr.py | 33 ++++++++++++---- sid_process.py | 82 ++++++++++++++++++++++++++++++++++++----- Ocr.py | 2 template-8.png | 0 4 files changed, 97 insertions(+), 20 deletions(-) diff --git a/Ocr.py b/Ocr.py index 662cb0b..f4447e5 100644 --- a/Ocr.py +++ b/Ocr.py @@ -222,7 +222,7 @@ sid_mask=self.settings.get("sid_mask", None) es,err,warn = getSID( self.img[ - int(0.045 * self.imgHeight) : int(0.085 * self.imgHeight), + int(0.04 * self.imgHeight) : int(0.095 * self.imgHeight), int(0.7 * self.imgWidth) : int(0.99 * self.imgWidth), ], self.sid_classifier, diff --git a/aoiOcr.py b/aoiOcr.py index 0d59684..bb74503 100644 --- a/aoiOcr.py +++ b/aoiOcr.py @@ -3,14 +3,24 @@ from glob import glob -settings = {"sid_mask": "61xx0xxx", "answer_treshold": 0.25} +settings = {"sid_mask": "64xx0xxx", "answer_treshold": 0.25} classifier = joblib.load("filename.joblib") -#p = Paper(filename="testpage300dpi_scan1.png") -#p=Paper(filename='sizif111.tif', sid_classifier=classifier, settings=settings) +# p = Paper(filename="testpage300dpi_scan1.png") +#p=Paper(filename='sizif111.tif', sid_classifier=classifier, settings={"sid_mask": "11xx0xxx", "answer_treshold": 0.25}) #p=Paper(filename='processed_scans/20141016095134535_0006.tif', sid_classifier=classifier, settings=settings) -#p=Paper(filename='processed_scans/20151111080408825_0001.tif', sid_classifier=classifier, settings=settings) -p=Paper(filename='processed_scans/20151028145444607_0028.tif', sid_classifier=classifier, settings=settings) +#p = Paper(filename="processed_scans/20151111080408825_0001.tif",sid_classifier=classifier,settings=settings,) +#p=Paper(filename='processed_scans/20151028145444607_0028.tif', sid_classifier=classifier, settings=settings) +pa = [ + "processed_scans/20141016095134535_0006.tif", + "processed_scans/20141016095134535_0028.tif", + "processed_scans/20141016095134535_0028.tif", + "processed_scans/20141016095134535_0037.tif", + "processed_scans/20141021095744144_0005.tif", + "processed_scans/20141021095744144_0009.tif", + "processed_scans/20141028095553745_0018.tif", +] +p=Paper(filename=pa[6], sid_classifier=classifier, settings=settings) # print(p.QRData) # print(p.errors) @@ -23,8 +33,13 @@ print(p.get_paper_ocr_data()) -exit(0) -filelist = glob("processed_scans/*.tif") -for f in filelist: - print(f,Paper(filename=f, sid_classifier=classifier, settings=settings).get_paper_ocr_data()) +filelist = glob("processed_scans/*.tif") +for f in sorted(filelist): + print("processing: {}".format(f)) + print( + f, + Paper( + filename=f, sid_classifier=classifier, settings=settings + ).get_paper_ocr_data(), + ) diff --git a/sid_process.py b/sid_process.py index 4674c0e..1f93d3c 100644 --- a/sid_process.py +++ b/sid_process.py @@ -57,6 +57,13 @@ def kernel(x, y): return np.ones((x, y), np.uint8) +def sid_compare(sid_no, sid_mask): + for s,es in zip(sid_mask,sid_no): + if s!='x' and s!=es: + return False + return True + + def segment_by_contours(image, sorted_ctrs, classifier): sid_no = "" @@ -77,7 +84,7 @@ return sid_no -def segment_by_sid_len(image, sid_mask, classifier): +def segment_by_sid_len(image, original_image, sid_mask, classifier): sid_no = "" sid_len = len(sid_mask) if sid_mask[0] == "1": @@ -86,9 +93,11 @@ move_left = 55 else: move_left = 0 + # Remove noise + image2 = cv2.morphologyEx(original_image, cv2.MORPH_OPEN, kernel(2, 2), iterations=7) # find biggest block of pixels - - image1 = cv2.morphologyEx(image, cv2.MORPH_DILATE, kernel(5, 25), iterations=4) + image1 = cv2.morphologyEx(image2, cv2.MORPH_DILATE, kernel(5, 25), iterations=4) + image1=img_as_ubyte(image1>50) cv2.imwrite("sidblock1.png", image1) im2, ctrs, hier = cv2.findContours( image1.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE @@ -97,7 +106,7 @@ ctrs, key=lambda ctr: cv2.contourArea(ctr) ) # get bigges contour x, y, w, h = cv2.boundingRect(sorted_ctrs[-1]) - image = image[y : y + h, x + 25 - move_left : x + w - 25] + image = image[y : y + h, x + 25 - move_left : x + w - 40] #+25,-25 cv2.imwrite("sidblock2.png", image) imgHeight, imgWidth = image.shape[0:2] numWidth = int(imgWidth / (sid_len)) @@ -111,14 +120,60 @@ sid_no = sid_no + str(classifier.predict(num.reshape(1, -1) / 255.0)[0]) return sid_no +def segment_by_7segments(image,original_image,sid_mask,classifier): + block_image = cv2.morphologyEx(original_image, cv2.MORPH_CLOSE, kernel(2, 2), iterations=10) + block_image =img_as_ubyte(block_image<50) + cv2.imwrite("sid_3rd1.png", block_image) + template = cv2.imread("template-8.png", 0) + w, h = template.shape[::-1] + res = cv2.matchTemplate(block_image, template, cv2.TM_CCOEFF_NORMED) + loc = np.where(res >= 0.75) + cimg = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) + loc_filtered_x=[] + loc_filtered_y=[] + for pt in zip(*loc[::-1]): + pt=(pt[0]-10,pt[1]-10) + loc_filtered_y.append(pt[1]) + loc_filtered_x.append(pt[0]) +# points.append(pt) + #filter points + if(len(loc_filtered_x)==0): + return "" + loc_filtered_x, loc_filtered_y = zip( + *sorted(zip(loc_filtered_x, loc_filtered_y)) + ) + a = np.diff(loc_filtered_x) > int(w/2) + a = np.append(a, True) + loc_filtered_x = np.array(loc_filtered_x) + loc_filtered_y = np.array(loc_filtered_y) + points = [loc_filtered_y[a], loc_filtered_x[a]] + for pt in zip(*points[::-1]): + cv2.rectangle(cimg, pt, (pt[0] + w, pt[1] + h), (0, 255, 255), 2) + cv2.imwrite("sid_3rd2.png", cimg) + + sid_no="" + for i,pt in enumerate(zip(*points[::-1])): + num=image[pt[1]:pt[1] + h, pt[0]:pt[0]+w] + #cv2.imwrite("sid_3no_{}.png".format(i), num) + num = img_as_ubyte(num < 128) + try: + num = cv2.resize(num, (32, 32)) + except: + return "" + cv2.imwrite("sid_3no_{}.png".format(i), num) + sid_no = sid_no + str(classifier.predict(num.reshape(1, -1) / 255.0)[0]) + + return sid_no def getSID(image, classifier, sid_mask): sid_warn = [] + sid_err=[] image = 255 - image + image_original=image.copy() image = img_as_ubyte(image > 100) cv2.imwrite("enSID0.png", image) # Remove noise - image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(2, 2), iterations=1) + image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(2, 2), iterations=3) # Closing. Connect non connected parts image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel(5, 3), iterations=4) # Again noise removal after closing @@ -144,14 +199,21 @@ ) sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0]) - sid_no = "" print(len(sid_mask), len(sorted_ctrs)) sid_no = segment_by_contours( image, sorted_ctrs[1:], classifier ) # we remove largest contour that surrounds whole image print(sid_no) - if len(sid_no) != len(sid_mask): - #print("Ooops have to find another way") + if len(sid_no) != len(sid_mask) or not sid_compare(sid_no,sid_mask): sid_warn.append("Trying second SID algorithm.") - sid_no = segment_by_sid_len(image, sid_mask, classifier) - return (sid_no, [], sid_warn) + sid_no = segment_by_7segments(image, image_original, sid_mask, classifier) + print(sid_no) + if(len(sid_no))!=len(sid_mask): + sid_no = segment_by_sid_len(image, image_original, sid_mask, classifier) + sid_warn.append("Trying third SID algorithm.") + + + if not sid_compare(sid_no, sid_mask): + sid_err=['Wrong SID!'] + + return (sid_no, sid_err, sid_warn) diff --git a/template-8.png b/template-8.png new file mode 100644 index 0000000..cb2063b --- /dev/null +++ b/template-8.png Binary files differ -- Gitblit v1.9.3