def getLastDetectionKeypoints(self): return ftext.getLastDetectionKeypoints()
def run_evaluation(inputDir, outputDir, invert=False, isFp=False): if not os.path.exists(outputDir): os.mkdir(outputDir) images = glob.glob('{0}/*.jpg'.format(inputDir)) images.extend(glob.glob('{0}/*.JPG'.format(inputDir))) images.extend(glob.glob('{0}/*.png'.format(inputDir))) segmDir = '{0}/segmentations'.format(inputDir) for image in images: print('Processing {0}'.format(image)) img = cv2.imread(image, 0) imgc = cv2.imread(image) imgproc = img imgKp = np.copy(img) imgKp.fill(0) baseName = os.path.basename(image) baseName = baseName[:-4] workPoint = 0.3 segmentations = ftext.getCharSegmentations( imgproc) #, outputDir, baseName) segmentations = segmentations[:, 0:10] segmentations = np.column_stack([ segmentations, np.zeros((segmentations.shape[0], 2), dtype=np.float) ]) maskDuplicates = segmentations[:, 8] == -1 segmentationsDuplicates = segmentations[maskDuplicates, :] maskNoNei = segmentationsDuplicates[:, 9] > workPoint segmentationsNoNei = segmentationsDuplicates[maskNoNei, :] keypoints = ftext.getLastDetectionKeypoints() imgKp[keypoints[:, 1].astype(int), keypoints[:, 0].astype(int)] = 255 scales = ftext.getImageScales() statc = ftext.getDetectionStat() words = ftext.findTextLines() segmLine = segmentations[segmentations[:, 7] == 1.0, :] segmentations[:, 2] += segmentations[:, 0] segmentations[:, 3] += segmentations[:, 1] if isFp: for detId in range(0, segmentations.shape[0]): ftext.acummulateCharFeatures(0, detId) continue lineGt = '{0}/gt_{1}.txt'.format(inputDir, baseName) if not os.path.exists(lineGt): lineGt = '{0}/{1}.txt'.format(inputDir, baseName) lineGt = '{0}/gt_{1}.txt'.format(inputDir, baseName) if os.path.exists(lineGt): try: word_gt = utls.read_icdar2013_txt_gt(lineGt) except ValueError: try: word_gt = utls.read_icdar2013_txt_gt(lineGt, separator=',') except ValueError: word_gt = utls.read_icdar2015_txt_gt(lineGt, separator=',') else: lineGt = '{0}/{1}.txt'.format(inputDir, baseName) word_gt = utls.read_mrrc_txt_gt(lineGt, separator=',') rWcurrent = 0.0 for gt_box in word_gt: if len(gt_box[4]) == 1: continue best_match = 0 cv2.rectangle(imgc, (gt_box[0], gt_box[1]), (gt_box[2], gt_box[3]), (0, 255, 0)) for det_word in words: rect_int = utils.intersect(det_word, gt_box) int_area = utils.area(rect_int) union_area = utils.area(utils.union(det_word, gt_box)) if union_area == 0: continue ratio = int_area / float(union_area) det_word[11] = max(det_word[11], ratio) if ratio > best_match: best_match = ratio rWcurrent += best_match best_match = 0 for detId in range(segmentations.shape[0]): rectn = segmentations[detId, :] rect_int = utils.intersect(rectn, gt_box) int_area = utils.area(rect_int) union_area = utils.area(utils.union(rectn, gt_box)) ratio = int_area / float(union_area) rectn[11] = max(ratio, rectn[11]) if ratio > best_match: best_match = ratio if ratio > 0.7: #print( "Word Match!" ) #tmp = ftext.getSegmentationMask(detId) #cv2.imshow("ts", tmp) #cv2.waitKey(0) ftext.acummulateCharFeatures(2, detId) segmImg = '{0}/{1}_GT.bmp'.format(segmDir, baseName) if not os.path.exists(segmImg): segmImg = '{0}/gt_{1}.png'.format(segmDir, baseName) if not os.path.exists(segmImg): segmImg = '{0}/{1}.png'.format(segmDir, baseName) segmImg = cv2.imread(segmImg, 0) if invert and segmImg is not None: segmImg = ~segmImg gt_rects = [] miss_rects = [] segmGt = '{0}/{1}_GT.txt'.format(segmDir, baseName) if os.path.exists(segmGt) and False: (gt_rects, groups) = utls.read_icdar2013_segm_gt(segmGt) segmImg = '{0}/{1}_GT.bmp'.format(segmDir, baseName) if not os.path.exists(segmImg): segmImg = '{0}/gt_{1}.png'.format(segmDir, baseName) segmImg = cv2.imread(segmImg) else: contours = cv2.findContours(np.copy(segmImg), mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)[1] for cont in contours: rect = cv2.boundingRect(cont) rect = [ rect[0], rect[1], rect[0] + rect[2], rect[1] + rect[3], '?', 0, 0 ] gt_rects.append(rect) for detId in range(segmentations.shape[0]): rectn = segmentations[detId, :] for k in range(len(gt_rects)): gt_rect = gt_rects[k] best_match = 0 best_match_line = 0 if (gt_rect[4] == ',' or gt_rect[4] == '.' or gt_rect[4] == '\'' or gt_rect[4] == ':' or gt_rect[4] == '-') and not evalPunctuation: continue minSingleOverlap = MIN_SEGM_OVRLAP if gt_rect[4] == 'i' or gt_rect[4] == '!': minSingleOverlap = 0.5 rect_int = utils.intersect(rectn, gt_rect) int_area = utils.area(rect_int) union_area = utils.area(utils.union(rectn, gt_rect)) ratio = int_area / float(union_area) rectn[10] = max(ratio, rectn[10]) if rectn[9] > workPoint: gt_rect[6] = max(ratio, gt_rect[6]) if ratio > best_match: best_match = ratio if ratio > best_match_line and rectn[7] == 1.0: best_match_line = ratio if ratio > minSingleOverlap: ftext.acummulateCharFeatures(1, detId) if ratio < minSingleOverlap: if k < len(gt_rects) - 1: gt_rect2 = gt_rects[k + 1] chars2Rect = utils.union(gt_rect2, gt_rect) rect_int = utils.intersect(rectn, chars2Rect) int_area = utils.area(rect_int) union_area = utils.area(utils.union(rectn, chars2Rect)) ratio = int_area / float(union_area) rectn[10] = max(ratio, rectn[10]) if ratio > 0.8: best_match2 = ratio gt_rect[5] = ratio gt_rect2[5] = ratio ftext.acummulateCharFeatures(2, detId) thickness = 1 color = (255, 0, 255) if best_match >= minSingleOverlap: color = (0, 255, 0) if best_match > 0.7: thickness = 2 cv2.rectangle(imgc, (gt_rect[0], gt_rect[1]), (gt_rect[2], gt_rect[3]), color, thickness) if rectn[10] == 0 and rectn[11] == 0: ftext.acummulateCharFeatures(0, detId) '''
def getLastDetectionKeypoints(self): return ftext.getLastDetectionKeypoints()
def run_words(inputDir, outputDir, invert=False): if not os.path.exists(outputDir): os.mkdir(outputDir) #images = glob.glob('{0}/*.png'.format('/datagrid/personal/TextSpotter/evaluation-sets/MS-text_database')) #images = glob.glob('{0}/*.jpg'.format('/datagrid/personal/TextSpotter/evaluation-sets/neocr_dataset')) images = glob.glob('{0}/*.jpg'.format(inputDir)) images.extend(glob.glob('{0}/*.JPG'.format(inputDir))) images.extend(glob.glob('{0}/*.png'.format(inputDir))) matched_words = 0 word_count = 0 for image in sorted(images): print('Processing {0}'.format(image)) img = cv2.imread(image, 0) imgc = cv2.imread(image) imgproc = img imgKp = np.copy(img) imgKp.fill(0) baseName = os.path.basename(image) baseName = baseName[:-4] workPoint = 0.3 segmentations = ftext.getCharSegmentations( imgproc) #, outputDir, baseName) segmentations = segmentations[:, 0:10] segmentations = np.column_stack([ segmentations, np.zeros((segmentations.shape[0], 2), dtype=np.float) ]) maskDuplicates = segmentations[:, 8] == -1 segmentationsDuplicates = segmentations[maskDuplicates, :] maskNoNei = segmentationsDuplicates[:, 9] > workPoint keypoints = ftext.getLastDetectionKeypoints() imgKp[keypoints[:, 1].astype(int), keypoints[:, 0].astype(int)] = 255 scales = ftext.getImageScales() statc = ftext.getDetectionStat() words = ftext.findTextLines() segmentations[:, 2] += segmentations[:, 0] segmentations[:, 3] += segmentations[:, 1] lineGt = '{0}/gt_{1}.txt'.format(inputDir, baseName) if not os.path.exists(lineGt): lineGt = '{0}/{1}.txt'.format(inputDir, baseName) lineGt = '{0}/gt_{1}.txt'.format(inputDir, baseName) if os.path.exists(lineGt): try: word_gt = utls.read_icdar2013_txt_gt(lineGt) except ValueError: try: word_gt = utls.read_icdar2013_txt_gt(lineGt, separator=',') except ValueError: word_gt = utls.read_icdar2015_txt_gt(lineGt, separator=',') else: lineGt = '{0}/{1}.txt'.format(inputDir, baseName) word_gt = utls.read_mrrc_txt_gt(lineGt, separator=',') cw = 0 for detId in range(segmentations.shape[0]): best_match = 0 for gt_box in word_gt: if len(gt_box[4]) == 1: continue if gt_box[4][0] == "#": continue cw += 1 rectn = segmentations[detId, :] rect_int = utils.intersect(rectn, gt_box) int_area = utils.area(rect_int) union_area = utils.area(utils.union(rectn, gt_box)) ratio = int_area / float(union_area) rectn[11] = max(ratio, rectn[11]) if ratio > best_match: best_match = ratio if ratio > 0.7: #print( "Word Match!" ) #cv2.rectangle(imgc, (rectn[0], rectn[1]), (rectn[2], rectn[3]), (0, 255, 0)) #cv2.imshow("ts", imgc) #cv2.waitKey(0) ftext.acummulateCharFeatures(2, detId) if gt_box[5] != -1: matched_words += 1 gt_box[5] = -1 if best_match == 0: ftext.acummulateCharFeatures(0, detId) word_count += cw print("word recall: {0}".format(matched_words / float(word_count)))