def raw_analysis(file_bin, analysed_parts, sign_min_size, test_dir, subdiv, manual, sleep, replacing_value=0):
# Each part to be analysed will be divided 'subdiv' times if each subdivision is higher than sign_min_size
range_list, minimal_range_set = generate_ranges(analysed_parts, subdiv, sign_min_size)
if minimal_range_set:
# each range is equal or smaller than the minimal signature size option, abort
return range_list
new_range_list = []
range_file_dict = {}
print("[i] Creating %d test files..." % len(range_list), end="")
for i, r in enumerate(range_list):
filepath = os.path.join(test_dir, "test-%s.bin" % i)
range_file_dict[filepath] = r
f = open(filepath, "wb")
f.write(file_bin[0:r[0]])
f.write(bytes([replacing_value])*(r[1]+1-r[0]))
f.write(file_bin[r[1]+1:])
f.close()
print("Done")
if not manual:
time.sleep(sleep)
else:
_ = input("Press any key to continue...")
found_sign = False
for filepath, r in range_file_dict.items():
if os.path.exists(filepath):
print("[i] Located signature between bytes %d and %d" % (r[0], r[1]))
new_range_list.append(r)
found_sign = True
try:
os.remove(filepath)
except FileNotFoundError:
pass
if len(new_range_list) == 0:
print("[i] Unable to get a more precise location of the signature, probable a payload containing multiple signatures")
return new_range_list
elif union(new_range_list) == union(analysed_parts):
print("[i] Unable to get a more precise location of the signature")
return new_range_list
else:
return raw_analysis(file_bin, new_range_list, sign_min_size, test_dir, subdiv, manual, sleep)
def crawl_web(seed): # returns index, graph of inlinks
tocrawl = [seed]
crawled = []
graph = {} # <url>, [list of pages it links to]
index = {}
while tocrawl:
page = tocrawl.pop()
if page not in crawled:
content = get_page(page)
add_page_to_index(index, page, content)
outlinks = get_all_links(content)
graph[page] = outlinks
utils.union(tocrawl, outlinks)
crawled.append(page)
return index, graph
def holes_filling(input, _):
struct_1 = np.array([
[1], [1], [1]
])
struct_2 = np.array([
[1, 1, 1]
])
structure = np.array([
[0, 1, 0],
[1, 1, 1],
[0, 1, 0]
])
mask = complementary(input)
#iter = 0
X0 = np.zeros(input.shape)
while True:
d1 = ndimage.binary_dilation(X0, struct_1, border_value=1, mask=mask)
d2 = ndimage.binary_dilation(X0, struct_2, border_value=1, mask=mask)
X1 = union(d1, d2)
if np.array_equal(X0, X1): break
X0 = X1
#iter += 1
return complementary(X0)
def line_categoric(datasets, x, y, title, path, yscale='linear', col_map=None):
print("Drawing line chart %s" % title)
if col_map is None:
col_map = make_col_map(datasets)
xdatas = []
for name in datasets:
xdata = list(map(lambda i: i[x], datasets[name]))
xdatas.append(xdata)
xdata = utils.union(*xdatas)
ypos = np.arange(len(xdata))
for i, name in enumerate(datasets):
data = datasets[name]
ydata = []
for j in range(len(xdata)):
xitem = xdata[j]
items = list(filter(lambda i: i[x] == xitem, data))
if len(items) > 0:
ydata.append(items[0][y])
else:
ydata.append(0)
plt.plot(xdata, ydata, '-o', label=name, color=col_map[name])
plt.ylabel(display.axis_case(y))
plt.yscale(yscale)
plt.xticks(ypos, xdata, rotation=90)
plt.title(title)
plt.legend(loc='upper left')
savefig(path)
def computeWordOvelap(imgc, word_gt, words, wordsOk, wordsFp):
best_match = 0
best_match2 = 0
for det_word in words:
try:
cv2.rectangle(imgc, (det_word[0], det_word[1]), (det_word[2], det_word[3]), (0, 0, 255))
for gt_box in word_gt:
rect_int = utils.intersect( det_word, gt_box )
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(det_word, gt_box))
ratio = int_area / float(union_area)
ratio2 = int_area / utils.area(gt_box)
if ratio > best_match:
best_match = ratio
w = det_word
best_match2 = ratio2
if best_match2 > 0.3:
wordsOk.append(det_word)
elif best_match == 0:
wordsFp.append(det_word)
except:
pass
return (best_match, best_match2)
def join(self, p1, p2, f, i, j):
buckets = defaultdict(set)
for ai, aj in self.get_tuples_from_function(f, i, j):
buckets[aj].add(ai)
image2 = set(frozenset(union(buckets[aj] for aj in s)) for s in p2)
image2 = make_partition(image2)
# print map(lambda x:map(str, x), image2)
# image2 = [[o for o in elems if o not in conflict] for elems in image2]
result = []
for s1 in p1:
# print " old set:", map(str, s1)
if True or not any(o in conflict for o in s1):
all = set()
for s2 in image2:
new = [o for o in s1 if o in s2]
if new:
# print " new set:", map(str, new)
result.append(new)
all.update(new)
# if len(all) < len(s1):
# print " remaining:", map(str, [o for o in s1 if o not in all])
# result.append([o for o in s1 if o not in all])
return result
def QXGen(C, Bd, B, d, l):
global genhash
if l < lmax:
if f(d) > 0:
u = utils.union(B, Bd)
if (genhash == ""):
hash = utils.getHash(u, len(modelCNF.clauses))
else:
hash = genhash
genhash = ""
if (not (hash in cache)
): #evito crear multiples hilos si ya esta en ejecución
future = pool.apply_async(callConsistencyCheck, args=([u]))
cache.update({hash: future})
#print("Genero: "+str(hash))
if f(C) == 1 and f(Bd) > 0:
QXGen(Bd, [], B + [C[0]], [C[0]], l + 1)
elif f(C) > 1:
if (len(C) > 1):
k = int(len(C) / 2)
Ca = C[0:k]
Cb = C[k:len(C)]
else:
k = int(len(C[0]) / 2)
Ca = [C[0][0:k]]
Cb = [C[0][k:len(C[0])]]
QXGen(Ca, Cb + Bd, B, Cb, l + 1)
if f(Bd) > 0 and f(d) > 0:
QXGen([Bd[0]], utils.Diff(Bd, [Bd[0]]), B, [], l + 1)
def computeWordOvelap(imgc, word_gt, words, wordsOk, wordsFp):
best_match = 0
best_match2 = 0
for det_word in words:
try:
cv2.rectangle(imgc, (det_word[0], det_word[1]),
(det_word[2], det_word[3]), (0, 0, 255))
for gt_box in word_gt:
rect_int = utils.intersect(det_word, gt_box)
int_area = utils.area(rect_int)
union_area = utils.area(utils.union(det_word, gt_box))
ratio = int_area / float(union_area)
ratio2 = int_area / utils.area(gt_box)
if ratio > best_match:
best_match = ratio
w = det_word
best_match2 = ratio2
if best_match2 > 0.3:
wordsOk.append(det_word)
elif best_match == 0:
wordsFp.append(det_word)
except:
pass
return (best_match, best_match2)
def depth_first_pebble(P, v, S):
if (P.is_source(v)):
P.pebble(v)
for u in P.get_parents(v):
if (not pebbled(u)):
depth_first_pebble(P, u, utils.union(S, P.get_parents(v)))
P.pebble(v)
P.remove_pebbles(utils.complement1(P.size(), S))
def computeSegmOverlap(gt_rects, segmentations, MIN_SEGM_OVRLAP=0.6):
segm2chars = 0
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
best_match2 = 0
for detId in range(segmentations.shape[0]):
rectn = segmentations[detId, :]
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)
if ratio > best_match:
best_match = ratio
if ratio > best_match_line and rectn[7] == 1.0:
best_match_line = ratio
gt_rect[5] = best_match
if best_match < MIN_SEGM_OVRLAP:
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)
if ratio > best_match2:
if ratio > MIN_SEGM_OVRLAP:
segm2chars += 1
best_match2 = ratio
gt_rect[5] = ratio
gt_rect2[5] = ratio
def narrow_docids(self, idx):
m0 = [ decode_array(idx[w]) for w in self.r0 if idx.has_key(w) ]
if self.r0 and not m0:
return []
m2 = [ decode_array(idx[w]) for w in self.r2 if idx.has_key(w) ]
if self.r2 and not m2:
return []
if self.r1:
try:
refs = intersect( decode_array(idx[w]) for w in self.r1 )
except KeyError:
return []
refs = union(refs, [ m for m in (m0,m2) if m ])
elif not self.r2:
refs = merge(m0)
else:
refs = union(merge(m0), [m2])
# Now: refs = [ docid1,sentid1, docid2,sentid2, ... ]
locs = [ (refs[i], refs[i+1]) for i in xrange(0, len(refs), 2) ]
return locs
def narrow_docids(self, idx):
m0 = [decode_array(idx[w]) for w in self.r0 if idx.has_key(w)]
if self.r0 and not m0:
return []
m2 = [decode_array(idx[w]) for w in self.r2 if idx.has_key(w)]
if self.r2 and not m2:
return []
if self.r1:
try:
refs = intersect(decode_array(idx[w]) for w in self.r1)
except KeyError:
return []
refs = union(refs, [m for m in (m0, m2) if m])
elif not self.r2:
refs = merge(m0)
else:
refs = union(merge(m0), [m2])
# Now: refs = [ docid1,sentid1, docid2,sentid2, ... ]
locs = [(refs[i], refs[i + 1]) for i in xrange(0, len(refs), 2)]
return locs
def computeSegmOverlap(gt_rects, segmentations, MIN_SEGM_OVRLAP = 0.6):
segm2chars = 0
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
best_match2 = 0
for detId in range(segmentations.shape[0]):
rectn = segmentations[detId, :]
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)
if ratio > best_match:
best_match = ratio
if ratio > best_match_line and rectn[7] == 1.0 :
best_match_line = ratio
gt_rect[5] = best_match
if best_match < MIN_SEGM_OVRLAP:
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)
if ratio > best_match2:
if ratio > MIN_SEGM_OVRLAP:
segm2chars += 1
best_match2 = ratio
gt_rect[5] = ratio
gt_rect2[5] = ratio
def create_optimistic_partitions(self):
self.best_function_partitions = {}
def get_partition(t):
if t not in self.best_partitions:
assert isinstance(t, pddl.types.CompositeType)
pnew = sum((get_partition(subt) for subt in t.types), [])
self.best_partitions[t] = pnew
return self.best_partitions[t]
logger.debug("Functions: %s", map(str, self.functions))
for f in self.functions:
logger.debug("Function: %s, args: %s, type: %s", f,
map(str, f.args), f.type)
self.best_function_partitions[f] = {}
logger.debug("best function Partiiton: %s",
map(str, self.best_function_partitions))
for i, t in enumerate(chain((a.type for a in f.args), [f.type])):
if t == pddl.t_boolean:
continue
p1 = [set(self.problem.get_all_objects(t))]
original = set(p1[0])
logger.debug("")
logger.debug("start %s %s %s", f, i, t)
for j, t2 in enumerate(
chain((a.type for a in f.args), [f.type])):
if i == j or t2 == pddl.t_boolean:
continue
logger.debug("inner %s %s", j, t2)
p2 = get_partition(t2)
logger.debug("%s %s %s", t2, map(lambda x: map(str, x),
p1),
map(lambda x: map(str, x), p2))
p1 = self.join(p1, p2, f, i, j)
used = union(p1)
if used < original:
p1.append(list(original - used))
self.best_function_partitions[f][i] = p1
logger.debug("%s %s", f, i)
for p in p1:
logger.debug(map(str, p))
def bar_categoric(datasets, x, y, path, yscale='linear'):
title = display.header_case(y)
print("Drawing bar chart %s" % title)
xdatas = []
for name in datasets:
xdata = list(map(lambda i: i[x], datasets[name]))
xdatas.append(xdata)
xdata = utils.union(*xdatas)
ypos = np.arange(len(xdata))
cols = len(datasets)
width = (1 - 2 * bar_padding) / cols
for i, name in enumerate(datasets):
data = datasets[name]
ydata = []
for j in range(len(xdata)):
xitem = xdata[j]
items = list(filter(lambda i: i[x] == xitem, data))
if len(items) > 0:
ydata.append(items[0][y])
else:
ydata.append(0)
offset = i - (cols - 1) / 2
plt.bar(ypos + offset * width,
ydata,
width=width,
align='center',
label=name)
plt.ylabel(display.axis_case(y))
plt.yscale(yscale)
plt.xticks(ypos, xdata, rotation=90)
plt.title(title)
plt.legend(loc='upper left')
savefig(path)
def stripPunc(word, w_type="any"):
n_spaces = word.count(" ")
if word.isalpha() and len(word) <= 2:
return False
chars = set(word)
for p in chars.intersection(PUNC):
if p != "/":
word = word.replace(p, "")
else:
splt = p.split(-1)
if splt in union(uniq, gqa_answers) and splt.isalpha():
return splt
else:
word = word.replace(p, "")
if all(map(lambda x: x.isdigit() or x == " ",
word)) and w_type is not "number":
return False
if not word:
return False
if not chars.intersection(VOWELS):
return False
return word
def complete_partition(t):
if t not in partitions:
if isinstance(t, pddl.types.CompositeType):
pnew = sum((complete_partition(subt) for subt in t.types),
[])
partitions[t] = pnew
else:
# print "Composite"
# print "subtypes: ", map(str, get_direct_subypes(t))
pnew = sum((complete_partition(t2)
for t2 in get_direct_subypes(t)), [])
# print t, map(lambda x:map(str, x), pnew)
all = set(self.problem.get_all_objects(t))
used = union(pnew)
assert used <= all
if used < all:
pnew.append(list(all - used))
# print (pnew)
# tmp = []
# for p in pnew:
# print type(p)
# if len(p)>1:
#
# for el in p:
# print el
# tmp.append(el)
# else:
# #el = p.pop()
# print "else ", p
# pass
# #tmp.append(el)
# print "tmp ", (tmp), "\n"
# print "pnew: ",t, map(lambda x:map(str, x), pnew)
partitions[t] = pnew
return partitions[t]
def run_batches(model,
opt,
lr_scheduler,
loader,
args,
timer,
training,
epoch=None,
epoch_fraction=None,
logger=None,
writer=None):
if not training and epoch_fraction != 1:
raise ValueError("Must do full epochs for val")
if epoch_fraction > 1 or epoch_fraction <= 0:
msg = "Invalid epoch_fraction {}.".format(epoch_fraction)
msg += " Should satisfy 0 < epoch_fraction <= 1"
raise ValueError(msg)
model.train(training)
client_download = torch.zeros(loader.dataset.num_clients)
client_upload = torch.zeros(loader.dataset.num_clients)
spe = steps_per_epoch(args.local_batch_size, loader.dataset,
args.num_workers)
if training:
epoch_idxs = epoch * spe
losses = []
for batch_idx, batch in enumerate(loader):
if batch_idx > 2 and args.do_test and batch_idx < spe - 10:
print("skipping ", batch_idx)
continue
# only carry out an epoch_fraction portion of the epoch
if batch_idx > spe * epoch_fraction:
break
lr_scheduler.step()
if lr_scheduler.get_lr() == 0:
# hack to get the starting LR right for fedavg
opt.step()
if args.local_batch_size == -1:
expected_num_clients = args.num_workers
if torch.unique(batch[0]).numel() < expected_num_clients:
# skip if there weren't enough clients left
print("SKIPPING BATCH: NOT ENOUGH CLIENTS")
continue
else:
expected_numel = args.num_workers * args.local_batch_size
if batch[0].numel() < expected_numel:
# skip incomplete batches
print("SKIPPING BATCH: NOT ENOUGH DATA")
continue
loss, download, upload = model(batch)
client_download += download
client_upload += upload
opt.step()
loss = np.mean(loss)
losses.append(loss)
train_time = timer()
download_mb = download.sum().item() / (1024 * 1024)
upload_mb = upload.sum().item() / (1024 * 1024)
batch_stats = {
'train_time': train_time,
'train_loss': loss,
'total_time': timer.total_time,
'down (MiB)': round(download_mb),
'up (MiB)': round(upload_mb),
}
lr = lr_scheduler.get_lr()[0]
writer.add_scalar('training/loss', loss, batch_idx + epoch_idxs)
writer.add_scalar('Lr', lr, batch_idx + epoch_idxs)
writer.add_scalar('Time/train', train_time, batch_idx + epoch_idxs)
summary = union({
'batch_idx': batch_idx + 1 + epoch_idxs,
'lr': lr
}, batch_stats)
logger.append(summary)
return np.mean(losses), client_download, client_upload
else:
nlls, accs, ppls = [], [], []
for batch_idx, batch in enumerate(loader):
if batch_idx > 5 and args.do_test and batch_idx < spe - 5:
print("skipping ", batch_idx)
continue
nll, acc = model(batch)
nll = np.mean(nll)
acc = np.mean(acc)
nlls.append(nll)
accs.append(acc)
return np.mean(nlls), np.mean(accs), np.exp(np.mean(nlls))
qid2keep = {}
#data to write
vqa_number_subset_qids = []
vqa_train_qid2ans = []
vqa_val_qid2ans = []
vqa_trashed_qids = set()
vqa_gqa_ans_overlap = set()
uniq_ans = set()
#data to load
uniq_attributes = loadTxt("phase_1/uniq_attrs")
uniq_relations = loadTxt("phase_1/uniq_rels")
uniq_objects = loadTxt("phase_1/uniq_objs")
uniq = union(uniq_attributes, uniq_relations, uniq_objects)
gqa_answers = loadTxt("phase_1/gqa_answers")
#funcs
def stripPunc(word, w_type="any"):
n_spaces = word.count(" ")
if word.isalpha() and len(word) <= 2:
return False
chars = set(word)
for p in chars.intersection(PUNC):
if p != "/":
word = word.replace(p, "")
else:
splt = p.split(-1)
def process_batch(nets, optim, optim2, image_size, args):
global it, mean_loss, mean_rec
it += 1 # 迭代次数加一
net, net_ctc = nets
net = net.net
net_ctc = net_ctc.net
net.blobs['data'].reshape(args.batch_size, 1, image_size[1],
image_size[0]) # 把一个batch的输入图片reshape
net.reshape()
optim2.step(1)
im = net.blobs['data'].data[...] # shape [batch_size,1,416,416]
draw = np.swapaxes(im, 2, 3)
draw = np.swapaxes(draw, 1, 3)
im_ctc = np.copy(draw)
draw += 1
draw *= 128
draw = np.array(draw, dtype="uint8").copy()
if args.debug:
grid_step = 16
line = 0
while line < image_size[0]:
cv2.line(draw[0], (0, line), (image_size[1], line),
(128, 128, 128))
line += grid_step
boxes = net.blobs['boxes'].data[...] # shape (4, 1, 500, 15)
word_gtob = net.blobs['gt_boxes'].data[...] # shape (4, 6, 1, 6)
word_txt = net.blobs['gt_labels'].data[...] # shape (4, 6, 1, 14)
lines_gtob = net.blobs['line_boxes'].data[...] # shape (4, 1, 1, 5)
lines_txt = net.blobs['line_labels'].data[...] # shape (4, 1, 1, 7)
#nms = boxeso[:, 0, 0, 8] == 0
#boxes = boxes[:, :, nms, :]
boxes[:, 0, :, 0] *= image_size[0]
boxes[:, 0, :, 1] *= image_size[1]
normFactor = math.sqrt(image_size[1] * image_size[1] +
image_size[0] * image_size[0])
boxes[:, 0, :, 2] *= normFactor
boxes[:, 0, :, 3] *= normFactor
sum_cost = 0
count = 0
labels_gt = []
labels_det = []
gt_to_detection = {}
net_ctc.clear_param_diffs()
batch_buckets = []
dummy = {}
matched_detections = 0
for bid in range(im.shape[0]): # 遍历batchsize下的每一个样本
o_image = net.layers[0].get_image_file_name(bid)
o_image = cv2.imread(o_image, cv2.IMREAD_GRAYSCALE)
cx = net.layers[0].get_crop(bid, 0)
cy = net.layers[0].get_crop(bid, 1)
cmx = net.layers[0].get_crop(bid, 2)
cmy = net.layers[0].get_crop(bid, 3)
o_image = o_image[cy:cmy, cx:cmx]
boxes_count = 0
for i in range(0, boxes.shape[2]):
det_word = boxes[bid, 0, i]
if (det_word[0] == 0 and det_word[1] == 0) or det_word[5] < 0.01:
break
boxes_count += 1
x = [i for i in range(boxes_count)]
#random.shuffle(x)
bucket_images = {}
batch_buckets.append(bucket_images)
word_gto = word_gtob[bid]
word_gto_txt = word_txt[bid]
gt_count = 0
for gt_no in range(word_gto.shape[0]):
gt = word_gto[gt_no, :]
gt = gt.reshape(6)
gtnum = 1000 * bid + gt_no
if gt[5] == -1:
#print("ignore gt!")
continue
gt_count += 1
txt = word_gto_txt[gt_no, :]
gtbox = ((gt[0] * image_size[0], gt[1] * image_size[1]),
(gt[2] * normFactor,
gt[3] * normFactor), gt[4] * 180 / 3.14)
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
if rect_gt[0] == 0 or rect_gt[
1] == 0 or rect_gt[0] + rect_gt[2] >= image_size[
0] or rect_gt[1] + rect_gt[3] >= image_size[1]:
continue
if gt[3] * normFactor < 3:
if args.debug:
pass
print('too small gt!')
continue
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0]
rect_gt[3] += rect_gt[1]
for i in range(0, min(100, boxes_count)):
if math.fabs(gt[4] - det_word[4]) > math.pi / 16:
continue
det_word = boxes[bid, 0, x[i], :]
if (det_word[0] == 0
and det_word[1] == 0) or det_word[5] < 0.01:
break
box = ((det_word[0], det_word[1]), (det_word[2], det_word[3]),
det_word[4] * 180 / 3.14)
box = cv2.boxPoints(box)
if args.debug:
boxp = np.array(box, dtype="int")
vis.draw_box_points(draw[bid], boxp, color=(0, 255, 0))
box = np.array(box, dtype="int")
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0]
bbox[3] += bbox[1]
#rectangle intersection ...
inter = intersect(bbox, rect_gt)
uni = union(bbox, rect_gt)
ratio = area(inter) / float(area(uni))
ratio_gt = area(inter) / float(area(rect_gt))
if ratio_gt < 0.95:
continue
if ratio < 0.5:
continue
if not gt_to_detection.has_key(gtnum):
gt_to_detection[gtnum] = [0, 0, 0]
tupl = gt_to_detection[gtnum]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = x[i]
tupl[2] = ratio_gt
det_word = boxes[bid, 0, x[i], :]
box = ([det_word[0],
det_word[1]], [det_word[2],
det_word[3]], det_word[4] * 180 / 3.14)
boxO = get_obox(im_ctc[bid], o_image, box)
boxO = ((boxO[0][0], boxO[0][1]), (boxO[1][0], boxO[1][1]),
boxO[2])
norm2, rot_mat = get_normalized_image(o_image, boxO)
#norm3, rot_mat = get_normalized_image(im_ctc[bid], ([det_word[0], det_word[1]], [det_word[2] * 1.2, det_word[3] * 1.1], det_word[4] * 180 / 3.14))
if norm2 is None:
continue
#if norm3 is None:
# continue
#continue
#cv2.imshow('ts', norm2)
#cv2.imshow('ts3', norm3)
#cv2.waitKey(1)
width_scale = 32.0 / norm2.shape[0]
width = norm2.shape[1] * width_scale
best_diff = width
bestb = 0
for b in range(0, len(buckets)):
if best_diff > abs(width * 1.3 - buckets[b]):
best_diff = abs(width * 1.3 - buckets[b])
bestb = b
scaled = cv2.resize(norm2, (buckets[bestb], 32))
scaled = np.asarray(scaled, dtype=np.float)
delta = scaled.max() - scaled.min()
scaled = (scaled) / (delta / 2)
scaled -= scaled.mean()
if not bucket_images.has_key(bestb):
bucket_images[bestb] = {}
bucket_images[bestb]['img'] = []
bucket_images[bestb]['sizes'] = []
bucket_images[bestb]['txt'] = []
bucket_images[bestb]['gt_enc'] = []
dummy[bestb] = 1
else:
if args.debug and len(bucket_images[bestb]) > 4:
continue
elif len(bucket_images[bestb]) > 32:
continue
gt_labels = []
txt_enc = ''
for k in range(txt.shape[1]):
if txt[0, k] > 0:
if codec_rev.has_key(txt[0, k]):
gt_labels.append(codec_rev[txt[0, k]])
else:
gt_labels.append(3)
txt_enc += unichr(txt[0, k])
else:
gt_labels.append(0)
if scaled.ndim == 3:
scaled = cv2.cvtColor(scaled, cv2.COLOR_BGR2GRAY)
if args.debug:
cv2.imshow('scaled', scaled)
bucket_images[bestb]['sizes'].append(len(gt_labels))
bucket_images[bestb]['gt_enc'].append(gt_labels)
bucket_images[bestb]['txt'].append(txt_enc)
bucket_images[bestb]['img'].append(scaled)
matched_detections += 1
#and learn OCR
for bucket in bucket_images.keys():
imtf = np.asarray(bucket_images[bucket]['img'], dtype=np.float)
imtf = np.reshape(imtf,
(imtf.shape[0], -1, imtf.shape[1], imtf.shape[2]))
#imtf = imtf.reshape((imtf.shape[0], imtf.shape[1], imtf.shape[2], 1))
#imtf = np.swapaxes(imtf,1,3)
net_ctc.blobs['data'].reshape(imtf.shape[0], imtf.shape[1],
imtf.shape[2], imtf.shape[3])
net_ctc.blobs['data'].data[...] = imtf
labels = bucket_images[bucket]['gt_enc']
txt = bucket_images[bucket]['txt']
max_len = 0
for l in range(0, len(labels)):
max_len = max(max_len, len(labels[l]))
for l in range(0, len(labels)):
while len(labels[l]) < max_len:
labels[l].append(0)
labels = np.asarray(labels, np.float)
net_ctc.blobs['label'].reshape(labels.shape[0], labels.shape[1])
net_ctc.blobs['label'].data[...] = labels
if args.debug:
vis.vis_square(imtf[0])
cv2.imshow('draw', draw[0])
cv2.waitKey(5)
#optim.step(1)
sum_cost += net_ctc.blobs['loss'].data[...]
if net_ctc.blobs['loss'].data[...] > 10:
#vis.vis_square(imtf[0])
#cv2.imshow('draw', draw[0])
sf = net_ctc.blobs['transpose'].data[...]
labels2 = sf.argmax(3)
out = utils.print_seq(labels2[:, 0, :])
print(u'{0} --- {1}'.format(out, txt[0]))
#cv2.waitKey(5)
count += imtf.shape[0]
correct_cout = 0
for i in range(len(labels_gt)):
det_text = labels_det[i]
gt_text = labels_gt[i]
if it % 100 == 0:
pass
#print( u"{0} -- {1}".format(det_text, gt_text).encode('utf8') )
if det_text == gt_text:
correct_cout += 1
count = max(count, 1)
mean_loss = 0.99 * mean_loss + 0.01 * sum_cost / count
mean_rec = mean_rec * 0.99 + 0.01 * correct_cout / float(
max(1, len(labels_gt)))
#count detection ratio
tp = 0
for bid in range(im.shape[0]):
word_gto = word_gtob[bid]
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
gtnum = 1000 * bid + gt_no
if gt_to_detection.has_key(gtnum):
tupl = gt_to_detection[gtnum]
if tupl[0] > 0.5:
tp += 1
loc_recall = tp / float(max(1, gt_count))
if it % 10 == 0:
print(
'{0} - lr:{1:.3e} ctc:{2:.4f}/{3:.4f} wr:{4:.2f}/{5:.2f}, loc:{6:.2f} {7}'
.format(it, 0.0001, sum_cost / count, mean_loss,
correct_cout / float(max(1, len(labels_gt))), mean_rec,
loc_recall, matched_detections))
if it % snapshot_interval == 0:
#optim.snapshot()
optim2.snapshot()
def process_batch(nets, optim, optim2, image_size, args):
global it, mean_loss, mean_rec
net, net_ctc = nets
net = net.net
net_ctc = net_ctc.net
net.blobs['data'].reshape(args.batch_size,1,image_size[1],image_size[0])
net.reshape()
it += 1
optim2.step(1)
im = net.blobs['data'].data[...]
draw = np.swapaxes(im,2,3)
draw = np.swapaxes(draw,1,3)
im_ctc = np.copy(draw)
draw += 1
draw *= 128
draw = np.array(draw, dtype="uint8").copy()
if args.debug:
grid_step = 16
line = 0
while line < image_size[0]:
cv2.line(draw[0], (0, line), (image_size[1], line), (128, 128, 128))
line += grid_step
boxes = net.blobs['boxes'].data[...]
word_gtob = net.blobs['gt_boxes'].data[...]
word_txt = net.blobs['gt_labels'].data[...]
lines_gtob = net.blobs['line_boxes'].data[...]
lines_txt = net.blobs['line_labels'].data[...]
#nms = boxeso[:, 0, 0, 8] == 0
#boxes = boxes[:, :, nms, :]
boxes[:, 0, :, 0] *= image_size[0]
boxes[:, 0, :, 1] *= image_size[1]
normFactor = math.sqrt(image_size[1] * image_size[1] + image_size[0] * image_size[0])
boxes[:, 0, :, 2] *= normFactor
boxes[:, 0, :, 3] *= normFactor
sum_cost = 0
count = 0
labels_gt = []
labels_det = []
gt_to_detection = {}
net_ctc.clear_param_diffs()
batch_buckets = []
dummy = {}
matched_detections = 0
for bid in range(im.shape[0]):
o_image = net.layers[0].get_image_file_name(bid)
o_image = cv2.imread(o_image, cv2.IMREAD_GRAYSCALE)
cx = net.layers[0].get_crop(bid, 0)
cy = net.layers[0].get_crop(bid, 1)
cmx = net.layers[0].get_crop(bid, 2)
cmy = net.layers[0].get_crop(bid, 3)
o_image = o_image[cy:cmy, cx:cmx]
boxes_count = 0
for i in range(0, boxes.shape[2]):
det_word = boxes[bid, 0, i]
if (det_word[0] == 0 and det_word[1] == 0) or det_word[5] < 0.01:
break
boxes_count += 1
x = [i for i in range(boxes_count)]
#random.shuffle(x)
bucket_images = {}
batch_buckets.append(bucket_images)
word_gto = word_gtob[bid]
word_gto_txt = word_txt[bid]
gt_count = 0
for gt_no in range(word_gto.shape[0]):
gt = word_gto[gt_no, :]
gt = gt.reshape(6)
gtnum = 1000 * bid + gt_no
if gt[5] == -1:
#print("ignore gt!")
continue
gt_count += 1
txt = word_gto_txt[gt_no, :]
gtbox = ((gt[0] * image_size[0], gt[1] * image_size[1]), (gt[2] * normFactor, gt[3] * normFactor), gt[4] * 180 / 3.14)
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
if rect_gt[0] == 0 or rect_gt[1] == 0 or rect_gt[0] + rect_gt[2] >= image_size[0] or rect_gt[1] + rect_gt[3] >= image_size[1]:
continue
if gt[3] * normFactor < 3:
if args.debug:
#print('too small gt!')
vis.draw_box_points(draw[bid], gtbox, color = (255, 255, 0))
cv2.imshow('draw', draw[bid])
continue
if args.debug:
vis.draw_box_points(draw[bid], gtbox, color = (0, 0, 0), thickness=2)
#vis.draw_box_points(draw[bid], gtbox, color = (255, 255, 255))
#cv2.imshow('draw', draw[bid])
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0]
rect_gt[3] += rect_gt[1]
for i in range(0, min(100, boxes_count)):
if math.fabs(gt[4] - det_word[4]) > math.pi / 16:
continue
det_word = boxes[bid, 0, x[i], :]
if (det_word[0] == 0 and det_word[1] == 0) or det_word[5] < 0.01:
break
box = ((det_word[0], det_word[1]), (det_word[2], det_word[3]), det_word[4] * 180 / 3.14)
box = cv2.boxPoints(box)
if args.debug:
boxp = np.array(box, dtype="int")
vis.draw_box_points(draw[bid], boxp, color = (0, 255, 0))
box = np.array(box, dtype="int")
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0]
bbox[3] += bbox[1]
#rectangle intersection ...
inter = intersect(bbox, rect_gt)
uni = union(bbox, rect_gt)
ratio = area(inter) / float(area(uni))
ratio_gt = area(inter) / float(area(rect_gt))
if ratio_gt < 0.95:
continue
if ratio < 0.5:
continue
if not gt_to_detection.has_key(gtnum):
gt_to_detection[gtnum] = [0, 0, 0]
tupl = gt_to_detection[gtnum]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = x[i]
tupl[2] = ratio_gt
det_word = boxes[bid, 0, x[i], :]
box = ([det_word[0], det_word[1]], [det_word[2], det_word[3]], det_word[4] * 180 / 3.14)
boxO = get_obox(im_ctc[bid], o_image, box)
boxO = ((boxO[0][0], boxO[0][1]), (boxO[1][0], boxO[1][1]), boxO[2])
norm2, rot_mat = get_normalized_image(o_image, boxO)
#norm3, rot_mat = get_normalized_image(im_ctc[bid], ([det_word[0], det_word[1]], [det_word[2] * 1.2, det_word[3] * 1.1], det_word[4] * 180 / 3.14))
if norm2 is None:
continue
#if norm3 is None:
# continue
#continue
#cv2.imshow('ts', norm2)
#cv2.imshow('ts3', norm3)
#cv2.waitKey(1)
width_scale = 32.0 / norm2.shape[0]
width = norm2.shape[1] * width_scale
best_diff = width
bestb = 0
for b in range(0, len(buckets)):
if best_diff > abs(width * 1.3 - buckets[b]):
best_diff = abs(width * 1.3 - buckets[b])
bestb = b
scaled = cv2.resize(norm2, (buckets[bestb], 32))
scaled = np.asarray(scaled, dtype=np.float)
delta = scaled.max() - scaled.min()
scaled = (scaled) / (delta / 2)
scaled -= scaled.mean()
if not bucket_images.has_key(bestb):
bucket_images[bestb] = {}
bucket_images[bestb]['img'] = []
bucket_images[bestb]['sizes'] = []
bucket_images[bestb]['txt'] = []
bucket_images[bestb]['gt_enc'] = []
dummy[bestb] = 1
else:
if args.debug and len(bucket_images[bestb]) > 4:
continue
elif len(bucket_images[bestb]) > 32:
continue
gt_labels = []
txt_enc = ''
for k in range(txt.shape[1]):
if txt[0, k] > 0:
if codec_rev.has_key(txt[0, k]):
gt_labels.append( codec_rev[txt[0, k]] )
else:
gt_labels.append( 3 )
txt_enc += unichr(txt[0, k])
else:
gt_labels.append( 0 )
if scaled.ndim == 3:
scaled = cv2.cvtColor(scaled, cv2.COLOR_BGR2GRAY)
if args.debug:
cv2.imshow('scaled', scaled)
bucket_images[bestb]['sizes'].append(len(gt_labels))
bucket_images[bestb]['gt_enc'].append(gt_labels)
bucket_images[bestb]['txt'].append(txt_enc)
bucket_images[bestb]['img'].append(scaled)
matched_detections += 1
#and learn OCR
for bucket in bucket_images.keys():
imtf = np.asarray(bucket_images[bucket]['img'], dtype=np.float)
imtf = np.reshape(imtf, (imtf.shape[0], -1, imtf.shape[1], imtf.shape[2]))
#imtf = imtf.reshape((imtf.shape[0], imtf.shape[1], imtf.shape[2], 1))
#imtf = np.swapaxes(imtf,1,3)
net_ctc.blobs['data'].reshape(imtf.shape[0],imtf.shape[1],imtf.shape[2], imtf.shape[3])
net_ctc.blobs['data'].data[...] = imtf
labels = bucket_images[bucket]['gt_enc']
txt = bucket_images[bucket]['txt']
max_len = 0
for l in range(0, len(labels)):
max_len = max(max_len, len(labels[l]))
for l in range(0, len(labels)):
while len(labels[l]) < max_len:
labels[l].append(0)
labels = np.asarray(labels, np.float)
net_ctc.blobs['label'].reshape(labels.shape[0], labels.shape[1])
net_ctc.blobs['label'].data[...] = labels
if args.debug:
vis.vis_square(imtf[0])
cv2.imshow('draw', draw[0])
cv2.waitKey(5)
optim.step(1)
sum_cost += net_ctc.blobs['loss'].data[...]
if net_ctc.blobs['loss'].data[...] > 10:
vis.vis_square(imtf[0])
cv2.imshow('draw', draw[0])
sf = net_ctc.blobs['transpose'].data[...]
labels2 = sf.argmax(3)
out = utils.print_seq(labels2[:, 0, :])
print(u'{0} - {1}'.format(out, txt[0]) )
cv2.waitKey(5)
count += imtf.shape[0]
correct_cout = 0
for i in range(len(labels_gt)):
det_text = labels_det[i]
gt_text = labels_gt[i]
if it % 100 == 0:
print( u"{0} - {1}".format(det_text, gt_text).encode('utf8') )
if det_text == gt_text:
correct_cout += 1
count = max(count, 1)
mean_loss = 0.99 * mean_loss + 0.01 * sum_cost / count
mean_rec = mean_rec * 0.99 + 0.01 * correct_cout / float(max(1, len(labels_gt)))
#count detection ratio
tp = 0
for bid in range(im.shape[0]):
word_gto = word_gtob[bid]
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
gtnum = 1000 * bid + gt_no
if gt_to_detection.has_key(gtnum):
tupl = gt_to_detection[gtnum]
if tupl[0] > 0.5:
tp += 1
loc_recall = tp / float(max(1, gt_count))
if args.debug:
cv2.imshow('draw', draw[0])
if im.shape[0] > 1:
cv2.imshow('draw2', draw[1])
cv2.waitKey(10)
if it % 10 == 0:
print('{0} - lr:{1:.3e} ctc:{2:.4f}/{3:.4f} wr:{4:.2f}/{5:.2f}, loc:{6:.2f} {7}'.format(it, 0.0001, sum_cost / count, mean_loss, correct_cout / float(max(1, len(labels_gt))), mean_rec, loc_recall, matched_detections))
if it % 1000 == 0:
optim.snapshot()
optim2.snapshot()
def draw_missed_letters_tile(
input_dir='/datagrid/personal/TextSpotter/FastTextEval/ICDAR-Train',
color=0,
edgeThreshold=13,
inter=True,
scalingFactor=1.6,
segmList=[]):
ft = FASTex(process_color=color, edgeThreshold=edgeThreshold)
d = input_dir
subdirs = [
os.path.join(d, o) for o in os.listdir(d)
if os.path.isdir(os.path.join(d, o))
]
subdirs = np.sort(subdirs)
lastDir = ''
for dir_name in subdirs:
file_name = '{0}/evaluation.npz'.format(dir_name)
if not os.path.exists(file_name):
continue
vars_dict = np.load(file_name)
inputDir = vars_dict['inputDir']
lastDir = dir_name
if 'letterKeypointHistogram' in vars_dict.keys():
letterKeypointHistogram = vars_dict['letterKeypointHistogram']
letterKeypointHistogram = dict(letterKeypointHistogram.tolist())
print(lastDir)
missing_letters = vars_dict['missing_letters']
missing_letters = dict(missing_letters.tolist())
segmDir = '{0}/segmentations'.format(inputDir)
segmDir = '/datagrid/personal/TextSpotter/evaluation-sets/icdar2013-Test/segmentations'
keys = []
ticks = []
values = []
values.append([])
values.append([])
values.append([])
values.append([])
ticks.append([])
ticks.append([])
ticks.append([])
ticks.append([])
listlen = 0
for letter in letterKeypointHistogram.keys():
keys.append(letter)
values[0].append(0)
ticks[0].append(listlen)
values[1].append(0)
ticks[1].append(listlen + 0.2)
values[2].append(0)
ticks[2].append(listlen + 0.4)
values[3].append(0)
ticks[3].append(listlen + 0.6)
for num in letterKeypointHistogram[letter].keys():
values[num][listlen] = letterKeypointHistogram[letter][num]
listlen += 1
indices = sorted(range(len(values[0])), key=lambda x: values[0][x])
indices.reverse()
border = 15
missLetter = []
imagesMiss = {}
for letter in np.asarray(keys)[np.asarray(indices)]:
if not missing_letters.has_key(letter):
continue
arr = missing_letters[letter]
for i in range(len(arr)):
miss = arr[i]
if len(segmList) > 0:
base = os.path.basename(miss[0])
if not base in segmList:
continue
missLetter.append(miss)
if imagesMiss.has_key(miss[0]):
imagesMiss[miss[0]].append(miss[1])
else:
imagesMiss[miss[0]] = []
imagesMiss[miss[0]].append(miss[1])
rowSize = len(imagesMiss.keys())
f, axes = plt.subplots(2, len(imagesMiss.keys()))
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=None,
hspace=None)
figNo = 0
for image in imagesMiss.keys():
if len(imagesMiss.keys()) > 1:
ax0 = axes[0][figNo]
ax = axes[1][figNo]
else:
ax0 = axes[figNo]
ax = axes[figNo]
figNo += 1
if color == 1:
img = cv2.imread(image)
else:
img = cv2.imread(image, 0)
baseName = os.path.basename(image)
baseName = baseName[:-4]
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)
segmentations = ft.getCharSegmentations(img)
keypoints = ft.getLastDetectionKeypoints()
if color == 1:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i in range(len(imagesMiss[image])):
if i == 0:
orBox = imagesMiss[image][0]
else:
orBox = utils.union(orBox, imagesMiss[image][i])
gt0 = orBox
gt = [
gt0[0] - border, gt0[1] - border, gt0[2] + border, gt0[3] + border
]
gt[0] = max(0, gt[0])
gt[1] = max(0, gt[1])
gt[2] = min(img.shape[1], gt[2])
gt[3] = min(img.shape[0], gt[3])
zoom = img[gt[1]:gt[3], gt[0]:gt[2]]
ax.imshow(zoom, cmap=pylab.gray(), interpolation='nearest')
ax0.imshow(zoom, cmap=pylab.gray(), interpolation='nearest')
centers = segmImg[keypoints[:, 1].astype(int),
keypoints[:, 0].astype(int)]
keypointsInsideMask = centers == (255, 255, 255)
keypointsInsideMask = np.invert(
np.bitwise_and(
np.bitwise_and(keypointsInsideMask[:, 0],
keypointsInsideMask[:, 1]),
keypointsInsideMask[:, 2]))
keypointsInside = keypoints[keypointsInsideMask, :]
mask = (keypoints[:, 0] > gt[0]) * (keypoints[:, 0] < gt[2]) * (
keypoints[:, 1] > gt[1]) * (keypoints[:, 1] < gt[3])
kpMask = keypoints[mask]
kpMask[:, 0] = kpMask[:, 0] - gt[0]
kpMask[:, 1] = kpMask[:, 1] - gt[1]
kpMask[:, 7] = kpMask[:, 7] - gt[0]
kpMask[:, 8] = kpMask[:, 8] - gt[1]
ax.plot(kpMask[:, 0], kpMask[:, 1], 'ro')
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax0.xaxis.set_ticklabels([])
ax0.yaxis.set_ticklabels([])
for k in range(kpMask.shape[0]):
ax.plot([kpMask[k, 0], kpMask[k, 7]], [kpMask[k, 1], kpMask[k, 8]],
'r-')
style = 'rx'
if kpMask.shape[1] > 9:
for k in range(3):
maski = kpMask[:, 9] == k + 1
if k == 1:
style = "rv"
if k == 2:
style = "rs"
if k == 4:
style = "bo"
if k == 5:
style = "yo"
ax.plot([kpMask[maski, 7]], [kpMask[maski, 8]], style)
for i in range(len(imagesMiss[image])):
gt0 = imagesMiss[image][i]
mask = (keypointsInside[:, 0] >
gt[0]) * (keypointsInside[:, 0] < gt[2]) * (
keypointsInside[:, 1] > gt[1]) * (keypointsInside[:, 1]
< gt[3])
kpMask = keypointsInside[mask]
keypointsInside[:, 0] = keypointsInside[:, 0] - gt[0]
keypointsInside[:, 1] = keypointsInside[:, 1] - gt[1]
keypointsInside[:, 7] = keypointsInside[:, 7] - gt[0]
keypointsInside[:, 8] = keypointsInside[:, 8] - gt[1]
ax.plot(keypointsInside[:, 0], keypointsInside[:, 1], 'go')
for k in range(keypointsInside.shape[0]):
ax.plot([keypointsInside[k, 0], keypointsInside[k, 7]],
[keypointsInside[k, 1], keypointsInside[k, 8]], 'g-')
ax.set_xlim(0, gt[2] - max(0, gt[0]))
ax.set_ylim((gt[3] - max(0, gt[1]), 0))
line = mlines.Line2D(np.array([
gt0[0] - gt[0], gt0[2] - gt[0], gt0[2] - gt[0], gt0[0] - gt[0],
gt0[0] - gt[0]
]),
np.array([
gt0[1] - gt[1], gt0[1] - gt[1],
gt0[3] - gt[1], gt0[3] - gt[1],
gt0[1] - gt[1]
]),
lw=5.,
alpha=0.6,
color='r')
ax0.add_line(line)
plt.show()
def compare_missed_segm(
input_dir='/datagrid/personal/TextSpotter/FastTextEval/experiments/segmentation',
input_dir2='/datagrid/personal/TextSpotter/FastTextEval/experiments/segmentationg',
showPictures=False):
ft = FASTex()
(ms, dirs) = read_segm_data(input_dir)
(ms2, dirs2) = read_segm_data(input_dir2, 'g')
ms.extend(ms2)
dirs.extend(dirs2)
sumHash = {}
for j in np.arange(0, len(ms)):
missing_segm = ms[j]
for image in missing_segm.keys():
arr = missing_segm[image]
if not sumHash.has_key(image):
sumHash[image] = arr
continue
for i in range(len(arr)):
miss_gt = arr[i]
check = sumHash[image]
hasGt = False
for k in range(len(check)):
miss_gt2 = check[k]
if miss_gt == miss_gt2:
hasGt = True
if not hasGt:
sumHash[image].append(miss_gt)
missing_segm = ms[0]
data = []
dataf = []
gt_id = 0
columns = ['Img', 'GT Id']
for image in sumHash.keys():
arr = sumHash[image]
f = None
for i in range(len(arr)):
orValue = False
miss_gt = arr[i]
row = []
row.append(os.path.basename(image))
row.append(gt_id)
gt_id += 1
rowf = []
for j in np.arange(0, len(ms)):
if gt_id == 1:
columns.append(dirs[j])
msj = ms[j]
hasSegmj = True
val = 1
if msj.has_key(image):
arrj = msj[image]
for k in range(len(arrj)):
miss_gtj = arrj[k]
if miss_gtj == miss_gt:
hasSegmj = False
val = 0
break
row.append(hasSegmj)
rowf.append(val)
orValue = orValue or hasSegmj
if orValue:
rowf.append(1)
else:
rowf.append(0)
if showPictures:
img = cv2.imread(image)
imgg = cv2.imread(image, cv2.IMREAD_GRAYSCALE)
if f == None:
f, axes = plt.subplots(1, 2, figsize=(16, 3))
f.suptitle('Missing segmentation: {0}'.format(image))
ax = axes[0]
ax.imshow(img,
cmap=pylab.gray(),
interpolation='nearest')
ax = axes[1]
ax.imshow(imgg,
cmap=pylab.gray(),
interpolation='nearest')
orBox = miss_gt
segmentations = ft.getCharSegmentations(imgg)
keypoints = ft.getLastDetectionKeypoints()
style = 'rx'
for k in range(5):
maski = keypoints[:, 9] == k + 1
if k == 1:
style = "rv"
if k == 2:
style = "ro"
if k == 4:
style = "bo"
ax.plot(keypoints[maski, 0], keypoints[maski, 1],
style)
for k in range(keypoints.shape[0]):
ax.plot([keypoints[k, 0], keypoints[k, 7]],
[keypoints[k, 1], keypoints[k, 8]], 'r-')
ax = axes[0]
else:
orBox = utils.union(orBox, miss_gt)
line = mlines.Line2D(np.array([
miss_gt[0], miss_gt[2], miss_gt[2], miss_gt[0],
miss_gt[0]
]),
np.array([
miss_gt[1], miss_gt[1],
miss_gt[3], miss_gt[3], miss_gt[1]
]),
lw=5.,
alpha=0.6,
color='r')
ax.add_line(line)
row.append(orValue)
data.append(row)
dataf.append(rowf)
if f != None:
ax = axes[0]
ax.set_xlim(orBox[0] - 20, orBox[2] + 20)
ax.set_ylim(orBox[3] + 20, orBox[1] - 20)
ax = axes[1]
ax.set_xlim(orBox[0] - 20, orBox[2] + 20)
ax.set_ylim(orBox[3] + 20, orBox[1] - 20)
plt.show()
columns.append("OR")
data = np.array(data)
dataf = np.array(dataf)
df = pandas.DataFrame(data=data, columns=columns)
#print(df)
sumCols = dataf.sum(0)
sumCols = dataf.shape[0] - sumCols
print("Missing Segmentations:")
print(sumCols)
indices = np.argsort(sumCols)
bestFactor = indices[1]
missing_segm = ms[bestFactor]
print("Best factor: {0}".format(dirs[bestFactor]))
maskBest = dataf[:, bestFactor] == 0
datafSec = dataf[maskBest, :]
sumCols = datafSec.sum(0)
sumCols = datafSec.shape[0] - sumCols
print("Missing Segmentations 2 best:")
print(sumCols)
indices = np.argsort(sumCols)
bestFactor2 = indices[1]
print("Best factor 2: {0}, missing segmentations: {1} -> {2}".format(
dirs[bestFactor2], datafSec.shape[0], sumCols[indices[1]]))
maskBest = datafSec[:, bestFactor2] == 0
dataf3 = datafSec[maskBest, :]
sumCols = dataf3.sum(0)
sumCols = dataf3.shape[0] - sumCols
indices = np.argsort(sumCols)
bestFactor2 = indices[1]
print("Best factor 3: {0}, missing segmentations: {1} -> {2}".format(
dirs[bestFactor2], dataf3.shape[0], sumCols[indices[1]]))
def compare_missed_segm(input_dir='/datagrid/personal/TextSpotter/FastTextEval/experiments/segmentation', input_dir2='/datagrid/personal/TextSpotter/FastTextEval/experiments/segmentationg', showPictures = False):
ft = FASTex()
(ms, dirs) = read_segm_data(input_dir)
(ms2, dirs2) = read_segm_data(input_dir2, 'g')
ms.extend(ms2)
dirs.extend(dirs2)
sumHash = {}
for j in np.arange(0, len(ms)):
missing_segm = ms[j]
for image in missing_segm.keys():
arr = missing_segm[image]
if not sumHash.has_key(image):
sumHash[image] = arr
continue
for i in range(len(arr)):
miss_gt = arr[i]
check = sumHash[image]
hasGt = False
for k in range(len(check)):
miss_gt2 = check[k]
if miss_gt == miss_gt2:
hasGt = True
if not hasGt:
sumHash[image].append(miss_gt)
missing_segm = ms[0]
data = []
dataf = []
gt_id = 0
columns = ['Img', 'GT Id']
for image in sumHash.keys():
arr = sumHash[image]
f = None
for i in range(len(arr)):
orValue = False
miss_gt = arr[i]
row = []
row.append(os.path.basename(image))
row.append(gt_id)
gt_id += 1
rowf = []
for j in np.arange(0, len(ms)):
if gt_id == 1:
columns.append(dirs[j])
msj = ms[j]
hasSegmj = True
val = 1
if msj.has_key(image):
arrj = msj[image]
for k in range(len(arrj)):
miss_gtj = arrj[k]
if miss_gtj == miss_gt:
hasSegmj = False
val = 0
break
row.append(hasSegmj)
rowf.append(val)
orValue = orValue or hasSegmj
if orValue:
rowf.append(1)
else:
rowf.append(0)
if showPictures:
img = cv2.imread(image)
imgg = cv2.imread(image, cv2.IMREAD_GRAYSCALE)
if f == None:
f, axes = plt.subplots(1, 2, figsize=(16, 3))
f.suptitle('Missing segmentation: {0}'.format(image))
ax = axes[0]
ax.imshow(img, cmap=pylab.gray(), interpolation='nearest')
ax = axes[1]
ax.imshow(imgg, cmap=pylab.gray(), interpolation='nearest')
orBox = miss_gt
segmentations = ft.getCharSegmentations(imgg)
keypoints = ft.getLastDetectionKeypoints()
style = 'rx'
for k in range(5):
maski = keypoints[:, 9] == k + 1
if k == 1:
style = "rv"
if k == 2:
style = "ro"
if k == 4:
style = "bo"
ax.plot(keypoints[maski, 0], keypoints[maski, 1], style)
for k in range(keypoints.shape[0]):
ax.plot([keypoints[k,0], keypoints[k,7]], [keypoints[k,1], keypoints[k,8]], 'r-')
ax = axes[0]
else:
orBox = utils.union(orBox, miss_gt)
line = mlines.Line2D(np.array([miss_gt[0], miss_gt[2], miss_gt[2], miss_gt[0], miss_gt[0]]), np.array([miss_gt[1], miss_gt[1], miss_gt[3], miss_gt[3], miss_gt[1]]), lw=5., alpha=0.6, color='r')
ax.add_line(line)
row.append(orValue)
data.append(row)
dataf.append(rowf)
if f != None:
ax = axes[0]
ax.set_xlim(orBox[0] - 20, orBox[2] + 20)
ax.set_ylim(orBox[3] + 20, orBox[1] - 20)
ax = axes[1]
ax.set_xlim(orBox[0] - 20, orBox[2] + 20)
ax.set_ylim(orBox[3] + 20, orBox[1] - 20)
plt.show()
columns.append("OR")
data = np.array(data)
dataf = np.array(dataf)
df = pandas.DataFrame(data = data, columns=columns)
#print(df)
sumCols = dataf.sum(0)
sumCols = dataf.shape[0] - sumCols
print("Missing Segmentations:")
print(sumCols)
indices = np.argsort(sumCols)
bestFactor = indices[1]
missing_segm = ms[bestFactor]
print( "Best factor: {0}".format(dirs[bestFactor]) )
maskBest = dataf[:, bestFactor] == 0
datafSec = dataf[maskBest, :]
sumCols = datafSec.sum(0)
sumCols = datafSec.shape[0] - sumCols
print("Missing Segmentations 2 best:")
print(sumCols)
indices = np.argsort(sumCols)
bestFactor2 = indices[1]
print( "Best factor 2: {0}, missing segmentations: {1} -> {2}".format(dirs[bestFactor2], datafSec.shape[0], sumCols[indices[1]]) )
maskBest = datafSec[:, bestFactor2] == 0
dataf3 = datafSec[maskBest, :]
sumCols = dataf3.sum(0)
sumCols = dataf3.shape[0] - sumCols
indices = np.argsort(sumCols)
bestFactor2 = indices[1]
print( "Best factor 3: {0}, missing segmentations: {1} -> {2}".format(dirs[bestFactor2], dataf3.shape[0], sumCols[indices[1]]) )
def preprocess(self, data):
print('preprocessing data...')
# modify date format
data['Date'] = data['Date'].apply(lambda x : datetime.datetime.strptime(x, '%d/%m/%y').strftime('%Y-%m-%d'))
# average out betting odds
data['Hodds'] = np.mean(data[['B365H','BWH','GBH','IWH','LBH','SBH','WHH','SJH','VCH','BSH']],axis=1)
data['Dodds'] = np.mean(data[['B365D','BWD','GBD','IWD','LBD','SBD','WHD','SJD','VCD','BSD']],axis=1)
data['Aodds'] = np.mean(data[['B365A','BWA','GBA','IWA','LBA','SBA','WHA','SJA','VCA','BSA']],axis=1)
# filter columns - meta data @ http://www.football-data.co.uk/notes.txt
use_col = ['Date','HomeTeam','AwayTeam','FTHG','FTAG','FTR','HTHG','HTAG','HTR','Referee','HS','AS','HST','AST',
'HC','AC','HF','AF','HY','AY','HR','AR','Hodds','Dodds','Aodds']
data = data[use_col]
# accumulate histories
# : referenced http://andrew.carterlunn.co.uk/programming/2018/02/20/beating-the-bookmakers-with-tensorflow.html
acc_hist = {'home_wins' : [], 'home_draws' : [], 'home_losses' : [], 'home_goals' : [], 'home_oppos_goals' : [],
'home_shots' : [], 'home_oppos_shots' : [], 'home_shotontarget' : [], 'home_oppos_shotontarget' : [],
'away_wins' : [], 'away_draws' : [], 'away_losses' : [], 'away_goals' : [], 'away_oppos_goals' : [],
'away_shots' : [], 'away_oppos_shots' : [], 'away_shotontarget' : [], 'away_oppos_shotontarget' : [],
'home_oppos_wins' : [], 'home_oppos_draws' : [], 'home_oppos_losses' : [],
'home_fouls' : [], 'home_yellowcards' : [], 'home_redcards' : [], 'home_cornerkicks' : [],
'home_oppos_cornerkicks' : [], 'home_oppos_fouls' : [], 'home_oppos_yellowcards' : [], 'home_oppos_redcards' : [],
'away_fouls' : [], 'away_yellowcards' : [], 'away_redcards' : [], 'away_cornerkicks' : [],
'away_oppos_cornerkicks' : [],'away_oppos_fouls' : [], 'away_oppos_yellowcards' : [], 'away_oppos_redcards' : []}
d = 0
for row in data.iterrows() :
hometeam = row[1]['HomeTeam']
awayteam = row[1]['AwayTeam']
date = row[1]['Date']
# filter matches with same playing teams
temp1 = data[conjunction(data['HomeTeam']==hometeam, data['AwayTeam']==awayteam)]
temp2 = data[conjunction(data['HomeTeam']==awayteam, data['AwayTeam']==hometeam)]
temp = pd.concat([temp1, temp2], axis=0)
history = temp[temp['Date']<date].sort_values(by='Date').tail(self.config.lookback_opp_matches)
# if opponent history is too short, continue
if len(history) < self.config.lookback_opp_matches :
for key in list(acc_hist.keys()) :
acc_hist[key].append(np.nan)
continue
# compute average number of goals scored against opponent in the past N matches with the opponent
home = history[history['HomeTeam'] == hometeam]
away = history[history['AwayTeam'] == hometeam]
home_sum = np.sum(home[['FTHG','FTAG','HS','AS','HST','AST','HC','AC','HF','AF','HY','AY','HR','AR']])
away_sum = np.sum(away[['FTHG','FTAG','HS','AS','HST','AST','HC','AC','HF','AF','HY','AY','HR','AR']])
# filter recent N matches of both home and away
home = data[union(data['HomeTeam']==hometeam, data['AwayTeam']==hometeam)]
home = home[home['Date']<date].sort_values(by='Date').tail(self.config.lookback_matches)
away = data[union(data['HomeTeam']==awayteam, data['AwayTeam']==awayteam)]
away = away[away['Date']<date].sort_values(by='Date').tail(self.config.lookback_matches)
# if match history is too short, continue
if len(home) < self.config.lookback_matches or len(away) < self.config.lookback_matches :
for key in list(acc_hist.keys()) :
acc_hist[key].append(np.nan)
continue
home_home_sum = np.sum(home[home['HomeTeam']==hometeam][['FTHG','HS','HST','HC','HF','HY','HR']])
home_away_sum = np.sum(home[home['AwayTeam']==hometeam][['FTAG','AS','AST','AC','AF','AY','AR']])
away_home_sum = np.sum(away[away['HomeTeam']==awayteam][['FTHG','HS','HST','HC','HF','HY','HR']])
away_away_sum = np.sum(away[away['AwayTeam']==awayteam][['FTAG','AS','AST','AC','AF','AY','AR']])
# append computation results to dictionary
acc_hist['home_oppos_goals'].append((home_sum['FTHG'] + away_sum['FTAG']) / self.config.lookback_opp_matches)
acc_hist['away_oppos_goals'].append((home_sum['FTAG'] + away_sum['FTHG']) / self.config.lookback_opp_matches)
acc_hist['home_oppos_shots'].append((home_sum['HS'] + away_sum['AS']) / self.config.lookback_opp_matches)
acc_hist['away_oppos_shots'].append((home_sum['AS'] + away_sum['HS']) / self.config.lookback_opp_matches)
acc_hist['home_oppos_shotontarget'].append((home_sum['HST'] + away_sum['AST']) / self.config.lookback_opp_matches)
acc_hist['away_oppos_shotontarget'].append((home_sum['AST'] + away_sum['HST']) / self.config.lookback_opp_matches)
acc_hist['home_oppos_cornerkicks'].append((home_sum['HC'] + away_sum['AC']) / self.config.lookback_opp_matches)
acc_hist['away_oppos_cornerkicks'].append((home_sum['AC'] + away_sum['HC']) / self.config.lookback_opp_matches)
acc_hist['home_oppos_fouls'].append((home_sum['HF'] + away_sum['AF']) / self.config.lookback_opp_matches)
acc_hist['away_oppos_fouls'].append((home_sum['AF'] + away_sum['HF']) / self.config.lookback_opp_matches)
acc_hist['home_oppos_yellowcards'].append((home_sum['HY'] + away_sum['AY']) / self.config.lookback_opp_matches)
acc_hist['away_oppos_yellowcards'].append((home_sum['AY'] + away_sum['HY']) / self.config.lookback_opp_matches)
acc_hist['home_oppos_redcards'].append((home_sum['HR'] + away_sum['AR']) / self.config.lookback_opp_matches)
acc_hist['away_oppos_redcards'].append((home_sum['AR'] + away_sum['HR']) / self.config.lookback_opp_matches)
acc_hist['home_goals'].append((home_home_sum['FTHG'] + home_away_sum['FTAG']) / self.config.lookback_matches)
acc_hist['away_goals'].append((away_home_sum['FTHG'] + away_away_sum['FTAG']) / self.config.lookback_matches)
acc_hist['home_shots'].append((home_home_sum['HS'] + home_away_sum['AS']) / self.config.lookback_matches)
acc_hist['away_shots'].append((away_home_sum['HS'] + away_away_sum['AS']) / self.config.lookback_matches)
acc_hist['home_shotontarget'].append((home_home_sum['HST'] + home_away_sum['AST']) / self.config.lookback_matches)
acc_hist['away_shotontarget'].append((away_home_sum['HST'] + away_away_sum['AST']) / self.config.lookback_matches)
acc_hist['home_cornerkicks'].append((home_home_sum['HC'] + home_away_sum['AC']) / self.config.lookback_matches)
acc_hist['away_cornerkicks'].append((away_home_sum['HC'] + away_away_sum['AC']) / self.config.lookback_matches)
acc_hist['home_fouls'].append((home_home_sum['HF'] + home_away_sum['AF']) / self.config.lookback_matches)
acc_hist['away_fouls'].append((away_home_sum['HF'] + away_away_sum['AF']) / self.config.lookback_matches)
acc_hist['home_yellowcards'].append((home_home_sum['HY'] + home_away_sum['AY']) / self.config.lookback_matches)
acc_hist['away_yellowcards'].append((away_home_sum['HY'] + away_away_sum['AY']) / self.config.lookback_matches)
acc_hist['home_redcards'].append((home_home_sum['HR'] + home_away_sum['AR']) / self.config.lookback_matches)
acc_hist['away_redcards'].append((away_home_sum['HR'] + away_away_sum['AR']) / self.config.lookback_matches)
# count ratio of wins / draws / losses in the past N matches of Home vs Away
res = []
for r in history.iterrows() :
if r[1]['HomeTeam'] == hometeam :
res.append(r[1]['FTR'])
else :
if r[1]['FTR'] == 'A' :
res.append('H')
elif r[1]['FTR'] == 'H' :
res.append('A')
else :
res.append('D')
acc_hist['home_oppos_wins'].append(res.count('H') / self.config.lookback_opp_matches)
acc_hist['home_oppos_draws'].append(res.count('D') / self.config.lookback_opp_matches)
acc_hist['home_oppos_losses'].append(res.count('A') / self.config.lookback_opp_matches)
# count ratio of wins / draws / losses in the past N matches
res = []
for r in home.iterrows() :
if r[1]['HomeTeam'] == hometeam :
res.append(r[1]['FTR'])
else :
if r[1]['FTR'] == 'A' :
res.append('H')
elif r[1]['FTR'] == 'H' :
res.append('A')
else :
res.append('D')
acc_hist['home_wins'].append(res.count('H') / self.config.lookback_matches)
acc_hist['home_draws'].append(res.count('D') / self.config.lookback_matches)
acc_hist['home_losses'].append(res.count('A') / self.config.lookback_matches)
res = []
for r in away.iterrows() :
if r[1]['HomeTeam'] == awayteam :
res.append(r[1]['FTR'])
else :
if r[1]['FTR'] == 'A' :
res.append('H')
elif r[1]['FTR'] == 'H' :
res.append('A')
else :
res.append('D')
acc_hist['away_wins'].append(res.count('H') / self.config.lookback_matches)
acc_hist['away_draws'].append(res.count('D') / self.config.lookback_matches)
acc_hist['away_losses'].append(res.count('A') / self.config.lookback_matches)
acc_hist = pd.DataFrame(acc_hist)
data = pd.concat([data, acc_hist], axis=1)
data = data.dropna()
return data
def evaluate_image(batch,
detections,
word_gto,
iou_th=0.3,
iou_th_vis=0.5,
iou_th_eval=0.4):
'''
Summary : Returns end-to-end true-positives, detection true-positives, number of GT to be considered for eval (len > 2).
Description : For each predicted bounding-box, comparision is made with each GT entry. Values of number of end-to-end true
positives, number of detection true positives, number of GT entries to be considered for evaluation are computed.
Parameters
----------
iou_th_eval : float
Threshold value of intersection-over-union used for evaluation of predicted bounding-boxes
iou_th_vis : float
Threshold value of intersection-over-union used for visualization when transciption is true but IoU is lesser.
iou_th : float
Threshold value of intersection-over-union between GT and prediction.
word_gto : list of lists
List of ground-truth bounding boxes along with transcription.
batch : list of lists
List containing data (input image, image file name, ground truth).
detections : tuple of tuples
Tuple of predicted bounding boxes along with transcriptions and text/no-text score.
Returns
-------
tp : int
Number of predicted bounding-boxes having IoU with GT greater than iou_th_eval.
tp_e2e : int
Number of predicted bounding-boxes having same transciption as GT and len > 2.
gt_e2e : int
Number of GT entries for which transcription len > 2.
'''
gt_to_detection = {}
tp = 0
tp_e2e = 0
gt_e2e = 0
draw = batch[4][0]
normFactor = math.sqrt(
draw.shape[1] * draw.shape[1] +
draw.shape[0] * draw.shape[0]) # Normalization factor
for i in range(0, len(detections)):
det = detections[i]
boxr = det[0]
box = cv2.boxPoints(boxr) # Predicted bounding-box parameters
box = np.array(
box, dtype="int") # Convert predicted bounding-box to numpy array
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0] # Convert width to right-coordinate
bbox[3] += bbox[1] # Convert height to bottom-coordinate
vis.draw_box_points(draw, box, color=(255, 0, 0))
det_text = det[1][0] # Predicted transcription for bounding-box
#print(det_text)
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
txt = gt[5] # GT transcription for given GT bounding-box
gtbox = ((gt[0] * draw.shape[1], gt[1] * draw.shape[0]),
(gt[2] * normFactor, gt[3] * normFactor),
gt[4] * 180 / 3.14) # Re-scaling GT values
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0] # Convert GT width to right-coordinate
rect_gt[3] += rect_gt[1] # Convert GT height to bottom-coordinate
inter = intersect(
bbox,
rect_gt) # Intersection of predicted and GT bounding-boxes
uni = union(bbox,
rect_gt) # Union of predicted and GT bounding-boxes
ratio = area(inter) / float(area(
uni)) # IoU measure between predicted and GT bounding-boxes
# 1). Visualize the predicted-bounding box if IoU with GT is higher than IoU threshold (iou_th) (Always required)
# 2). Visualize the predicted-bounding box if transcription matches the GT and condition 1. holds
# 3). Visualize the predicted-bounding box if transcription matches and IoU with GT is less than iou_th_vis and 1. and 2. hold
if ratio > iou_th:
vis.draw_box_points(draw, box, color=(0, 128, 0))
if not gt_to_detection.has_key(gt_no):
gt_to_detection[gt_no] = [0, 0]
if txt.lower() == det_text.lower():
to_cls_x.append(
[len(det_text), det[1][1], det[1][2], det[1][3]])
to_cls_y.append(1)
vis.draw_box_points(draw,
box,
color=(0, 255, 0),
thickness=2)
gt[7] = 1 # Change this parameter to 1 when predicted transcription is correct.
if ratio < iou_th_vis:
vis.draw_box_points(draw,
box,
color=(255, 255, 255),
thickness=2)
cv2.imshow('draw', draw)
#cv2.waitKey(0)
else:
to_cls_x.append(
[len(det_text), det[1][1], det[1][2], det[1][3]])
to_cls_y.append(0)
tupl = gt_to_detection[gt_no]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = i
# Count the number of end-to-end and detection true-positives
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
txt = gt[5]
if len(txt) > 2:
gt_e2e += 1
if gt[7] == 1:
tp_e2e += 1
if gt_to_detection.has_key(gt_no):
tupl = gt_to_detection[gt_no]
if tupl[0] > iou_th_eval: # Increment detection true-positive, if IoU is greater than iou_th_eval
tp += 1
cv2.imshow('draw', draw)
return tp, tp_e2e, gt_e2e
def draw_missed_letters_tile(input_dir='/datagrid/personal/TextSpotter/FastTextEval/ICDAR-Train', color = 0, edgeThreshold = 13, inter = True, scalingFactor=1.6, segmList=[]):
ft = FASTex(process_color = color, edgeThreshold = edgeThreshold)
d=input_dir
subdirs = [os.path.join(d,o) for o in os.listdir(d) if os.path.isdir(os.path.join(d,o))]
subdirs = np.sort(subdirs)
lastDir = ''
for dir_name in subdirs:
file_name = '{0}/evaluation.npz'.format(dir_name)
if not os.path.exists(file_name):
continue
vars_dict = np.load(file_name)
inputDir = vars_dict['inputDir']
lastDir = dir_name
if 'letterKeypointHistogram' in vars_dict.keys():
letterKeypointHistogram = vars_dict['letterKeypointHistogram']
letterKeypointHistogram = dict(letterKeypointHistogram.tolist())
print(lastDir)
missing_letters = vars_dict['missing_letters']
missing_letters = dict(missing_letters.tolist())
segmDir = '{0}/segmentations'.format(inputDir)
segmDir = '/datagrid/personal/TextSpotter/evaluation-sets/icdar2013-Test/segmentations'
keys = []
ticks = []
values = []
values.append([])
values.append([])
values.append([])
values.append([])
ticks.append([])
ticks.append([])
ticks.append([])
ticks.append([])
listlen = 0
for letter in letterKeypointHistogram.keys():
keys.append(letter)
values[0].append(0)
ticks[0].append(listlen)
values[1].append(0)
ticks[1].append(listlen + 0.2)
values[2].append(0)
ticks[2].append(listlen + 0.4)
values[3].append(0)
ticks[3].append(listlen + 0.6)
for num in letterKeypointHistogram[letter].keys():
values[num][listlen] = letterKeypointHistogram[letter][num]
listlen += 1
indices = sorted(range(len(values[0])),key=lambda x:values[0][x])
indices.reverse()
border = 15
missLetter = []
imagesMiss = {}
for letter in np.asarray(keys)[np.asarray(indices)]:
if not missing_letters.has_key(letter):
continue
arr = missing_letters[letter]
for i in range(len(arr)):
miss = arr[i]
if len(segmList) > 0:
base = os.path.basename(miss[0])
if not base in segmList:
continue
missLetter.append(miss)
if imagesMiss.has_key(miss[0]):
imagesMiss[miss[0]].append( miss[1] )
else:
imagesMiss[miss[0]] = []
imagesMiss[miss[0]].append( miss[1] )
rowSize = len(imagesMiss.keys())
f, axes = plt.subplots(2, len(imagesMiss.keys()))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
figNo = 0
for image in imagesMiss.keys():
if len(imagesMiss.keys()) > 1:
ax0 = axes[0][figNo]
ax = axes[1][figNo]
else:
ax0 = axes[figNo]
ax = axes[figNo]
figNo += 1
if color == 1:
img = cv2.imread(image)
else:
img = cv2.imread(image, 0)
baseName = os.path.basename(image)
baseName = baseName[:-4]
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)
segmentations = ft.getCharSegmentations(img)
keypoints = ft.getLastDetectionKeypoints()
if color == 1:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i in range(len(imagesMiss[image])):
if i == 0:
orBox = imagesMiss[image][0]
else:
orBox = utils.union(orBox, imagesMiss[image][i])
gt0 = orBox
gt = [gt0[0] - border, gt0[1] - border, gt0[2] + border, gt0[3] + border ]
gt[0] = max(0, gt[0])
gt[1] = max(0, gt[1])
gt[2] = min(img.shape[1], gt[2])
gt[3] = min(img.shape[0], gt[3])
zoom = img[gt[1]:gt[3], gt[0]:gt[2]]
ax.imshow(zoom, cmap=pylab.gray(), interpolation='nearest')
ax0.imshow(zoom, cmap=pylab.gray(), interpolation='nearest')
centers = segmImg[keypoints[:, 1].astype(int), keypoints[:, 0].astype(int)]
keypointsInsideMask = centers == (255, 255, 255)
keypointsInsideMask = np.invert(np.bitwise_and(np.bitwise_and(keypointsInsideMask[:, 0], keypointsInsideMask[:, 1]), keypointsInsideMask[:, 2]))
keypointsInside = keypoints[keypointsInsideMask, :]
mask = (keypoints[:, 0] > gt[0]) * (keypoints[:, 0] < gt[2]) * (keypoints[:, 1] > gt[1]) * (keypoints[:, 1] < gt[3])
kpMask = keypoints[mask]
kpMask[:, 0] = kpMask[:, 0] - gt[0]
kpMask[:, 1] = kpMask[:, 1] - gt[1]
kpMask[:, 7] = kpMask[:, 7] - gt[0]
kpMask[:, 8] = kpMask[:, 8] - gt[1]
ax.plot(kpMask[:, 0], kpMask[:, 1], 'ro')
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax0.xaxis.set_ticklabels([])
ax0.yaxis.set_ticklabels([])
for k in range(kpMask.shape[0]):
ax.plot([kpMask[k,0], kpMask[k,7]], [kpMask[k,1], kpMask[k,8]], 'r-')
style = 'rx'
if kpMask.shape[1] > 9:
for k in range(3):
maski = kpMask[:, 9] == k + 1
if k == 1:
style = "rv"
if k == 2:
style = "rs"
if k == 4:
style = "bo"
if k == 5:
style = "yo"
ax.plot([kpMask[maski,7]], [kpMask[maski,8]], style)
for i in range(len(imagesMiss[image])):
gt0 = imagesMiss[image][i]
mask = (keypointsInside[:, 0] > gt[0]) * (keypointsInside[:, 0] < gt[2]) * (keypointsInside[:, 1] > gt[1]) * (keypointsInside[:, 1] < gt[3])
kpMask = keypointsInside[mask]
keypointsInside[:, 0] = keypointsInside[:, 0] - gt[0]
keypointsInside[:, 1] = keypointsInside[:, 1] - gt[1]
keypointsInside[:, 7] = keypointsInside[:, 7] - gt[0]
keypointsInside[:, 8] = keypointsInside[:, 8] - gt[1]
ax.plot(keypointsInside[:, 0], keypointsInside[:, 1], 'go')
for k in range(keypointsInside.shape[0]):
ax.plot([keypointsInside[k,0], keypointsInside[k,7]], [keypointsInside[k,1], keypointsInside[k,8]], 'g-')
ax.set_xlim(0, gt[2] - max(0, gt[0]))
ax.set_ylim((gt[3] - max(0, gt[1]), 0))
line = mlines.Line2D(np.array([gt0[0] - gt[0], gt0[2] - gt[0], gt0[2] - gt[0], gt0[0] - gt[0], gt0[0] - gt[0]]), np.array([gt0[1] - gt[1], gt0[1] - gt[1], gt0[3] - gt[1], gt0[3] - gt[1], gt0[1] - gt[1]]), lw=5., alpha=0.6, color='r')
ax0.add_line(line)
plt.show()
try:
graph.add_edge((names[i], names[j], dist))
except ValueError:
graph.update_edge(names[i], names[j], dist)
print(names[i], names[j])
graph.create_adjacency_matrix()
return graph
imdb, afi, rt, mc, hw = scrape_movies.get_movie_lists()
movie_lists = [imdb, afi, rt, mc, hw]
union = utils.union(movie_lists)
intersection = utils.intersect(movie_lists)
ranks = {}
counts = {}
for movie in union:
rank, count = average_rank(movie, movie_lists)
ranks[movie], counts[movie] = rank, count
top_movies = sorted(ranks, key=ranks.get)
with open("movie_lists/aggregate_top_100.txt", "w") as file:
for rank, movie in enumerate(top_movies[:100]):
file.write(str(rank + 1) + ". " + movie + "\n")
union = sorted(union)
imdb_ranks = np.zeros((len(union), 1))
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)))
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 evaluate_image(batch, detections, word_gto, iou_th=0.3, iou_th_vis=0.5, iou_th_eval=0.4):
'''
Summary : Returns end-to-end true-positives, detection true-positives, number of GT to be considered for eval (len > 2).
Description : For each predicted bounding-box, comparision is made with each GT entry. Values of number of end-to-end true
positives, number of detection true positives, number of GT entries to be considered for evaluation are computed.
Parameters
----------
iou_th_eval : float
Threshold value of intersection-over-union used for evaluation of predicted bounding-boxes
iou_th_vis : float
Threshold value of intersection-over-union used for visualization when transciption is true but IoU is lesser.
iou_th : float
Threshold value of intersection-over-union between GT and prediction.
word_gto : list of lists
List of ground-truth bounding boxes along with transcription.
batch : list of lists
List containing data (input image, image file name, ground truth).
detections : tuple of tuples
Tuple of predicted bounding boxes along with transcriptions and text/no-text score.
Returns
-------
tp : int
Number of predicted bounding-boxes having IoU with GT greater than iou_th_eval.
tp_e2e : int
Number of predicted bounding-boxes having same transciption as GT and len > 2.
gt_e2e : int
Number of GT entries for which transcription len > 2.
'''
gt_to_detection = {}
tp = 0
tp_e2e = 0
gt_e2e = 0
draw = batch[4][0]
normFactor = math.sqrt(draw.shape[1] * draw.shape[1] + draw.shape[0] * draw.shape[0]) # Normalization factor
for i in range(0, len(detections)):
det = detections[i]
boxr = det[0]
box = cv2.boxPoints(boxr) # Predicted bounding-box parameters
box = np.array(box, dtype="int") # Convert predicted bounding-box to numpy array
bbox = cv2.boundingRect(box)
bbox = [bbox[0], bbox[1], bbox[2], bbox[3]]
bbox[2] += bbox[0] # Convert width to right-coordinate
bbox[3] += bbox[1] # Convert height to bottom-coordinate
vis.draw_box_points(draw, box, color = (255, 0, 0))
det_text = det[1][0] # Predicted transcription for bounding-box
#print(det_text)
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
txt = gt[5] # GT transcription for given GT bounding-box
gtbox = ((gt[0] * draw.shape[1], gt[1] * draw.shape[0]), (gt[2] * normFactor, gt[3] * normFactor), gt[4] * 180 / 3.14) # Re-scaling GT values
gtbox = cv2.boxPoints(gtbox)
gtbox = np.array(gtbox, dtype="int")
rect_gt = cv2.boundingRect(gtbox)
rect_gt = [rect_gt[0], rect_gt[1], rect_gt[2], rect_gt[3]]
rect_gt[2] += rect_gt[0] # Convert GT width to right-coordinate
rect_gt[3] += rect_gt[1] # Convert GT height to bottom-coordinate
inter = intersect(bbox, rect_gt) # Intersection of predicted and GT bounding-boxes
uni = union(bbox, rect_gt) # Union of predicted and GT bounding-boxes
ratio = area(inter) / float(area(uni)) # IoU measure between predicted and GT bounding-boxes
# 1). Visualize the predicted-bounding box if IoU with GT is higher than IoU threshold (iou_th) (Always required)
# 2). Visualize the predicted-bounding box if transcription matches the GT and condition 1. holds
# 3). Visualize the predicted-bounding box if transcription matches and IoU with GT is less than iou_th_vis and 1. and 2. hold
if ratio > iou_th:
vis.draw_box_points(draw, box, color = (0, 128, 0))
if not gt_to_detection.has_key(gt_no):
gt_to_detection[gt_no] = [0, 0]
if txt.lower() == det_text.lower():
to_cls_x.append([len(det_text), det[1][1], det[1][2], det[1][3]])
to_cls_y.append(1)
vis.draw_box_points(draw, box, color = (0, 255, 0), thickness=2)
gt[7] = 1 # Change this parameter to 1 when predicted transcription is correct.
if ratio < iou_th_vis:
vis.draw_box_points(draw, box, color = (255, 255, 255), thickness=2)
cv2.imshow('draw', draw)
#cv2.waitKey(0)
else:
to_cls_x.append([len(det_text), det[1][1], det[1][2], det[1][3]])
to_cls_y.append(0)
tupl = gt_to_detection[gt_no]
if tupl[0] < ratio:
tupl[0] = ratio
tupl[1] = i
# Count the number of end-to-end and detection true-positives
for gt_no in range(len(word_gto)):
gt = word_gto[gt_no]
txt = gt[5]
if len(txt) > 2:
gt_e2e += 1
if gt[7] == 1:
tp_e2e += 1
if gt_to_detection.has_key(gt_no):
tupl = gt_to_detection[gt_no]
if tupl[0] > iou_th_eval: # Increment detection true-positive, if IoU is greater than iou_th_eval
tp += 1
cv2.imshow('draw', draw)
return tp, tp_e2e, gt_e2e
def train(model,
opt,
lr_scheduler,
train_loader,
test_loader,
args,
writer,
loggers=(),
timer=None):
timer = timer or Timer()
total_download = 0
total_upload = 0
if args.eval_before_start:
# val
test_loss, test_acc, _, _ = run_batches(model, None, None, test_loader,
False, args)
test_time = timer()
print("Test acc at epoch 0: {:0.4f}".format(test_acc))
# ceil in case num_epochs in case we want to do a
# fractional number of epochs
for epoch in range(math.ceil(args.num_epochs)):
if epoch == math.ceil(args.num_epochs) - 1:
epoch_fraction = args.num_epochs - epoch
else:
epoch_fraction = 1
# train
train_loss, train_acc, download, upload = run_batches(
model, opt, lr_scheduler, train_loader, True, epoch_fraction, args)
if train_loss is np.nan:
print("TERMINATING TRAINING DUE TO NAN LOSS")
return
train_time = timer()
download_mb = download.sum().item() / (1024 * 1024)
upload_mb = upload.sum().item() / (1024 * 1024)
total_download += download_mb
total_upload += upload_mb
# val
test_loss, test_acc, _, _ = run_batches(model, None, None, test_loader,
False, 1, args)
test_time = timer()
# report epoch results
try:
rounded_down = round(download_mb)
except:
rounded_down = np.nan
try:
rounded_up = round(upload_mb)
except:
rounded_up = np.nan
epoch_stats = {
'train_time': train_time,
'train_loss': train_loss,
'train_acc': train_acc,
'test_loss': test_loss,
'test_acc': test_acc,
'down (MiB)': rounded_down,
'up (MiB)': rounded_up,
'total_time': timer.total_time,
}
lr = lr_scheduler.get_last_lr()[0]
summary = union({'epoch': epoch + 1, 'lr': lr}, epoch_stats)
for logger in loggers:
logger.append(summary)
if args.use_tensorboard:
writer.add_scalar('Loss/train', train_loss, epoch)
writer.add_scalar('Loss/test', test_loss, epoch)
writer.add_scalar('Acc/train', train_acc, epoch)
writer.add_scalar('Acc/test', test_acc, epoch)
writer.add_scalar('Time/train', train_time, epoch)
writer.add_scalar('Time/test', test_time, epoch)
writer.add_scalar('Time/total', timer.total_time, epoch)
writer.add_scalar('Lr', lr, epoch)
print("Total Download (MiB): {:0.2f}".format(total_download))
print("Total Upload (MiB): {:0.2f}".format(total_upload))
print("Avg Download Per Client: {:0.2f}".format(
total_download / train_loader.dataset.num_clients))
print("Avg Upload Per Client: {:0.2f}".format(
total_upload / train_loader.dataset.num_clients))
return summary
def run_evaluation(inputDir, outputDir, process_color = 0, processTest = 0):
if not os.path.exists(outputDir):
os.mkdir(outputDir)
edgeThreshold = 14
fastex = FASTex(edgeThreshold = edgeThreshold)
modelFile = '/home/busta/outModel.boost'
model = cv2.Boost()
model.load(modelFile)
images = glob.glob('{0}/*.jpg'.format(inputDir))
segmDir = '{0}/segmentations'.format(inputDir)
precision = 0;
precisionDen = 0
recall = 0
recall05 = 0
recallNonMax = 0
recallDen = 0
wordRecall = 0
wordRecallDen = 0
segm2chars = 0
regionsCount = 0
regionsCountNonMax = 0
missing_segmNonMaxCount = 0
letterKeypointHistogram = defaultdict(lambda : defaultdict(float))
octaveLetterKeypointHistogram = defaultdict(lambda : defaultdict(float))
missing_letters = {}
letterHistogram = defaultdict(int)
missing_segm = {}
missing_segm2 = {}
missing_segmNonMax = {}
diffMaxOctavesMap = {}
diffScoreOctavesMap = {}
segmHistogram = []
segmWordHistogram = []
results = []
hist = None
histFp = None
histDist = None
histDistFp = None
histDistMax = None
histDistMaxWhite = None
histDistMaxFp = None
hist2dDist =None
hist2dDistFp = None
hist2dDistScore = None
hist2dDistScoreFp = None
histDistMaxWhiteFp = None
histSegm = np.zeros((256), dtype = np.float)
histSegmCount = np.zeros((256), dtype = np.int)
stat = np.asarray([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=np.float)
times = []
gtSegmCount = 0
wordsOk = []
wordsFp = []
keypointsTotal = 0
keypointsTotalInside = 0
orbTime = 0
lineNo = 0
perfectWords = 0;
perfectWordsNS = 0;
hasSegm = False
for image in images:
print('Processing {0}'.format(image))
img = cv2.imread(image, 0)
imgc = cv2.imread(image)
imgcO = cv2.imread(image)
if process_color == 1:
imgproc = imgc
else:
imgproc = img
baseName = os.path.basename(image)
baseName = baseName[:-4]
workPoint = 0.3
segmentations = fastex.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, :]
if segmentations.shape[0] > 0:
print( 'Dupl ratio: {0} - {1}/ {2} - {3}'.format(segmentationsDuplicates.shape[0] / float(segmentations.shape[0]), segmentationsDuplicates.shape[0], segmentations.shape[0], segmentationsNoNei.shape[0] ) )
keypoints = fastex.getLastDetectionKeypoints()
keypointsTotal += keypoints.shape[0]
statc = fastex.getDetectionStat()
times.append([ statc[1], statc[2], statc[3], statc[4], statc[5], statc[6], statc[7], statc[8], statc[9], statc[10]])
stat += statc
values = img[ keypoints[:, 1].astype(int), keypoints[:, 0].astype(int) ]
valuesMax = img[keypoints[:, 6].astype(int), keypoints[:, 5].astype(int)]
diffValMax = np.abs(values - valuesMax)
regionsCount += segmentations.shape[0]
regionsCountNonMax += segmentationsNoNei.shape[0]
segmentations[:, 2] += segmentations[:, 0]
segmentations[:, 3] += segmentations[:, 1]
keypointsOrb = fastex.getLastDetectionOrbKeypoints()
orbTime += keypointsOrb[0][9]
segmGt = '{0}/{1}_GT.txt'.format(segmDir, baseName)
pden = 0
rden = 0
if os.path.exists(segmGt):
hasSegm = True
(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)
try:
(hist, histFp, histDist, histDistMax, histDistMaxWhite, hist2dDist, hist2dDistScore, histDistFp, histDistMaxFp, histDistMaxWhiteFp, hist2dDistFp, hist2dDistScoreFp, keypointsInside) = collect_histograms(img, segmImg, keypoints, values, diffValMax, keypointsTotalInside, diffMaxOctavesMap, diffScoreOctavesMap, hist, histFp, histDist, histDistMax, histDistMaxWhite, hist2dDist, hist2dDistScore, histDistFp, histDistMaxFp, histDistMaxWhiteFp, hist2dDistFp, hist2dDistScoreFp)
except:
pass
rcurrent = 0
rcurrent05 = 0
rcurrentNonMax = 0
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
gtSegmCount += 1
rectMask = np.bitwise_and(np.bitwise_and( keypointsInside[:, 0] >= gt_rect[0], keypointsInside[:, 0] <= gt_rect[2]), np.bitwise_and(keypointsInside[:, 1] >= gt_rect[1], keypointsInside[:, 1] <= gt_rect[3]))
letterInside = keypointsInside[rectMask, :]
#make keypoints histogram
if letterInside.shape[0] > 0:
octaves = np.unique( letterInside[:, 2])
maxOctave = np.max(octaves)
maxOctavePoints = 0
for i in range(int(maxOctave) + 1):
octavePoints = letterInside[letterInside[:, 2] == i, :]
maxOctavePoints = max(maxOctavePoints, octavePoints.shape[0])
if maxOctavePoints > 0:
octaveLetterKeypointHistogram[gt_rect[4]][0] += 1
if maxOctavePoints > 1:
octaveLetterKeypointHistogram[gt_rect[4]][1] += 1
if maxOctavePoints > 2:
octaveLetterKeypointHistogram[gt_rect[4]][2] += 1
if maxOctavePoints > 3:
octaveLetterKeypointHistogram[gt_rect[4]][3] += 1
if letterInside.shape[0] == 0:
if not missing_letters.has_key(gt_rect[4]):
missing_letters[gt_rect[4]] = []
missing_letters[gt_rect[4]].append( (image, gt_rect) )
if letterInside.shape[0] > 0:
letterKeypointHistogram[gt_rect[4]][0] += 1
if letterInside.shape[0] > 1:
letterKeypointHistogram[gt_rect[4]][1] += 1
if letterInside.shape[0] > 2:
letterKeypointHistogram[gt_rect[4]][2] += 1
if letterInside.shape[0] > 3:
letterKeypointHistogram[gt_rect[4]][3] += 1
letterHistogram[gt_rect[4]] += 1
best_match2 = 0
minSingleOverlap = MIN_SEGM_OVRLAP
if gt_rect[4] == 'i' or gt_rect[4] == '!':
minSingleOverlap = 0.5
for detId in range(segmentations.shape[0]):
rectn = segmentations[detId, :]
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
best_segm = segmentations[detId, :]
if ratio > best_match_line and rectn[7] == 1.0 :
best_match_line = ratio
if best_match < 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 > best_match2:
if ratio > MIN_SEGM_OVRLAP:
segm2chars += 1
best_match2 = ratio
gt_rect[5] = ratio
gt_rect2[5] = ratio
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)
recall += best_match
recallNonMax += gt_rect[6]
if best_match >= minSingleOverlap:
recall05 += best_match
rcurrent05 += best_match
else:
if not missing_segm.has_key(image):
missing_segm[image] = []
missing_segm[image].append(gt_rect)
if gt_rect[5] < MIN_SEGM_OVRLAP:
if not missing_segm2.has_key(image):
missing_segm2[image] = []
missing_segm2[image].append(gt_rect)
segm2chars += 1
if gt_rect[6] < minSingleOverlap:
if not missing_segmNonMax.has_key(image):
missing_segmNonMax[image] = []
missing_segmNonMax[image].append(gt_rect)
missing_segmNonMaxCount += 1
rcurrent += best_match
rcurrentNonMax += gt_rect[6]
recallDen += 1
rden += 1
if best_match > 0 and process_color != 1:
val = img[best_segm[5], best_segm[4]]
histSegm[val] += best_match
histSegmCount[val] += 1
pcurrent = 0
for detId in range(segmentations.shape[0]):
best_match = 0
rectn = segmentations[detId, :]
for gt_rect in gt_rects:
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)
if ratio > best_match:
best_match = ratio
precision += best_match
pcurrent += best_match
precisionDen += 1
pden += 1
if pden == 0:
pcurrent = 0
else:
pcurrent = pcurrent / pden
if rden == 0:
rcurrent = 0
rcurrent05 = 0
rcurrentNonMax = 0
else:
rcurrent = rcurrent / rden
rcurrent05 = rcurrent05 / rden
rcurrentNonMax = rcurrentNonMax / rden
segmHistogram.append([ segmentations.shape[0], segmentations[segmentations[:, 10] > 0.4].shape[0], segmentations[segmentations[:, 10] > 0.5].shape[0], segmentations[segmentations[:, 10] > 0.6].shape[0], segmentations[segmentations[:, 10] > 0.7].shape[0] ])
segmWordHistogram.append([segmentations.shape[0], segmentations[np.bitwise_or(segmentations[:, 10] > 0.5, segmentations[:, 11] > 0.5 )].shape[0]])
results.append((baseName, rcurrent, pcurrent, rcurrent05))
if precisionDen == 0:
pcurrent = 0
else:
precision = precision / precisionDen
if recallDen == 0:
rcurrent = 0
else:
recall = recall / recallDen
recall05 = recall05 / recallDen
recallNonMax = recallNonMax / recallDen
wordRecall = wordRecall / max(1, wordRecallDen)
try:
histSegm = histSegm / max(1, histSegmCount)
except ValueError:
pass
print('Evalation Results:')
print( 'recall: {0}, precision: {1}, recall 0.5: {2}, recall NonMax: {3}'.format(recall, precision, recall05, recallNonMax) )
kpTimes = np.histogram(np.asarray(times)[:, 0], bins=20)
print('Keypoint Time Histogram: {0}'.format(kpTimes))
print('Detection statistics:')
print(stat)
for letter in letterKeypointHistogram.keys():
for num in letterKeypointHistogram[letter].keys():
letterKeypointHistogram[letter][num] = letterKeypointHistogram[letter][num] / float(letterHistogram[letter])
for num in octaveLetterKeypointHistogram[letter].keys():
octaveLetterKeypointHistogram[letter][num] = octaveLetterKeypointHistogram[letter][num] / float(letterHistogram[letter])
letterKeypointHistogram[letter] = dict(letterKeypointHistogram[letter])
octaveLetterKeypointHistogram[letter] = dict(octaveLetterKeypointHistogram[letter])
print('Perfect words: {0}'.format(perfectWords))
eval_date = datetime.date.today()
np.savez('{0}/evaluation'.format(outputDir), recall=recall, recall05 = recall05, recallNonMax=recallNonMax, precision=precision, eval_date=eval_date, regionsCount=regionsCount, inputDir = inputDir, hist = hist, histSegm = histSegm, stat=stat, letterKeypointHistogram = dict(letterKeypointHistogram), missing_letters=missing_letters, octaveLetterKeypointHistogram=dict(octaveLetterKeypointHistogram), missing_segm=missing_segm,
times=np.asarray(times), histFp = histFp, gtSegmCount = gtSegmCount, wordRecall=wordRecall, histDist=histDist, histDistFp = histDistFp, histDistMax=histDistMax, histDistMaxFp=histDistMaxFp, hist2dDist=hist2dDist, hist2dDistFp=hist2dDistFp, hist2dDistScore=hist2dDistScore, hist2dDistScoreFp=hist2dDistScoreFp, histDistMaxWhite=histDistMaxWhite, histDistMaxWhiteFp=histDistMaxWhiteFp, wordsOk=wordsOk, wordsFp=wordsFp, diffMaxOctavesMap = diffMaxOctavesMap, diffScoreOctavesMap = diffScoreOctavesMap,
missing_segm2=missing_segm2, segmHistogram=segmHistogram, segmWordHistogram=segmWordHistogram, regionsCountNonMax=regionsCountNonMax, missing_segmNonMax=missing_segmNonMax)
print( "GT segmentations count {0}".format(gtSegmCount) )
print('FasTex Inside {0}/{1} ({2})'.format(keypointsTotalInside, keypointsTotal, keypointsTotalInside / float(keypointsTotal) ))
print('FasText time: {0}, Orb time: {1} '.format( np.sum(times, 0)[0], orbTime))
print('2 Chars Segmentation: {0}'.format(segm2chars) )
print('NonMax Regions Count: {0}/{1}'.format(regionsCountNonMax, missing_segmNonMaxCount))