def getDetectionStat(self):
'''
Returns numpy with columns:
keypointsCount;
keypointsTime;
segmentationTime;
classificationTime;
rawClsTime;
textLineTime;
wallTime;
classificationTimeTuples;
tuplesTime;
strokesTime;
gcTime;
'''
return ftext.getDetectionStat()
def getDetectionStat(self):
return ftext.getDetectionStat()
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 getDetectionStat(self):
return ftext.getDetectionStat()
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)))