def draw(self, ctx, area, fill_or_stroke=True):
"""Draw the complete drawing by drawing each object in turn."""
self._painter.background(ctx, area, color=(1,1,1), clip=True) # paint white background
self._painter.setup(ctx, transform={"translate":self.get_offset(), "scale": self.get_ratio()})
# paishysime tik tuos tashkus kurie pakliuna i vartotojo langa
# reikia device_area konvertuoti i user koordinates
x, y = self.device_to_user(Point(area.x, area.y))
x2, y2 = self.device_to_user(Point(area.x + area.width, area.y + area.height))
radius = 0 #self.pixel_radius + self.line_width # ne cia reikia prideti radiusa, o ten kur dedame i cavas'a shape'us
# geriau cia, nes paprasciau yra iskviesti nupaisyti didesni gabala nei paskaiciuoti tikslu pvz linijo simboliu dydi...
elements = self._index_rtree.intersection((x-radius, y-radius, x2+radius, y2+radius))
elements_zindex = self._styler.create_zindex(elements) # jis yra pilnas visu galimu zindex'u sarasas - pradzioje dalis ju gali buti ir tusti []
timer.start("draw")
for zindex in sorted(elements_zindex.keys()):
for element in elements_zindex[zindex]:
#print "element: ", element[0][0]
self._painter.draw(element, update=elements_zindex, fill_or_stroke=fill_or_stroke) # kazka visada nupaiso - bet dar papildomai gali iterpti elementu su didesniu zindex'u
# tie elementai bus nupaisomi veliau.
timer.end("draw")
def update_surface_buffer(self, area, offset=Point(0, 0)):
"""update self._surface_buffer"""
#self._surface_buffer = None
if not self._surface_buffer:
self._surface_buffer = cairo.ImageSurface(cairo.FORMAT_ARGB32, area.width, area.height)
#self._surface_buffer = cairo.RecordingSurface(cairo.CONTENT_COLOR_ALPHA, (0,0, area.width, area.height))
context = cairo.Context(self._surface_buffer)
timer.start("expose full redraw")
self._canvas.draw(context, area)
timer.end("expose full redraw")
else: # reikia paslinkti arba tiesiog perpaisyti is buferio
#print "expose buffer"
# nupaisome buferi (jeigu reikia su postumiu)
merged_surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, area.width, area.height)
#merged_surface = cairo.RecordingSurface(cairo.CONTENT_COLOR_ALPHA, (0,0, area.width, area.height))
merged_ctx = cairo.Context(merged_surface)
merged_ctx.set_source_surface(self._surface_buffer, offset.x, offset.y)
merged_ctx.paint()
# pridejome sena padraginta gabala
# dabar pridesime papildomus gabalus, jeigu atliktas dragas
self.invalidate_drag_regions(area, offset) # paskaiciuojame ka reikia perpaisyti po draginimo virsuje/apacioje/sonuose
self.draw_invalid_regions(merged_surface) # perpaisome viska, kas buvo irasyra i self._redraw_regions = []
self._surface_buffer = merged_surface
def test(obj, tool):
if obj[0] == 4:
#data = Shape.decompress(obj).get_data()
#data1 = geometry.curve2ocad(data)
#data2 = geometry.triplets2bezier(data)
geometry.bezier_points = []
timer.start("BezierLength") # praleidziama pirma ir paskutines koordinates
length = geometry.bezier_length(obj[3:], maxdepth=4, distance_tolerance=0.25)
# bent dvigubai greiciau nei, kai distance_tolerance=None, panasu kai maxdepth=3
# reikia apsispresti kas geriau (maxdepth=4, distance_tolerance=0.25), ar (maxdepth=3, distance_tolerance=None)
# panasu, kad maxdepth=3 - duoda daugiau tasku
# maxdepth=3 - rezultatas mane tenkina, ar galima pagreitinti pridejus distance_tolerance?
timer.end("BezierLength")
print "bezier length: ", length
timer.start("CairoLength")
length2 = geometry.cairo_curve_length(obj[3:])
timer.end("CairoLength")
print "cairo length: ", length2
if tool._curve_id:
screen.replace(tool._curve_id, ScreenLine(geometry.bezier_points))
else:
tool._curve_id = screen.add(ScreenLine(geometry.bezier_points))
#timer.start("BezierLength22")
#print "lnegth: ", geometry.bezier_length(data2, maxdepth=4, distance_tolerance=None)
#timer.end("BezierLength22")
return "Length: " + str(length)
else:
return str(obj[1]) + " - z-index: " + str(screen.get_canvas().get_styler().get_style(obj).get("z-index", "?")) #obj[1]
def paint_buffer(self, zoom=0):
"""zoom:0, tai atliekam iprasta perpaisyma (zoom nebuvo paspaustas)
zoom:1 - pradinis perpaisymas po zoom paspaudimo, nupaisome tik vartotojui matoma dali
zoom:2 - perpaisymas po expose ivykdymo, dapaisome likusia buferio dali
"""
#print "paint_buffer :", zoom
self.ui_set_cursor("wait_arrow")
area = self._device_area
self._canvas.drag2(Point(area.width, area.height)) #pastumiam canvas, kuri paskui atstumsime atgal
if not zoom:
#timer.start("update 3x3")
#self._surface_buffer = None - pilnai perpiesti draginimo metu apsimoka tik kartais, kai zoomlevelis pats maziausias - nes tada
# ta pati figura patenka tiek i kaire tiek ir i desine drago puse ir yra perpaisoma du kartus
self.update_surface_buffer(Area(0, 0, area.width*3, area.height*3), self._buffer_offset)
#timer.end("update 3x3")
elif zoom == 1: # perpaisome po zoom paspaudimo virsutine kaire dali
#self._surface_buffer = None
timer.start("update 2x2")
self.update_surface_buffer(Area(0, 0, area.width*2, area.height*2))
timer.end("update 2x2")
self._zoom = True # po expose
elif zoom == 2: # perpaisysime likusia dali
timer.start("update invalid")
self.invalidate(Area(area.width*2, 0, area.width, area.height*2)) # invaliduojame buferio desini sona
self.invalidate(Area(0, area.height*2, area.width*3, area.height)) # invaliduojame buferio apacia
self.update_surface_buffer(Area(0, 0, area.width*3, area.height*3))
timer.end("update invalid")
self._zoom = False
self._canvas.drag2(Point(-area.width, -area.height))
self.ui_reset_cursor()
def load_fcad_file(self, file_path):
timer.start("loading shapes.fcad")
self._shapes = ShapeFile.read_fcad_file(file_path)
if len(self._shapes):
def generator_function(points):
for i, obj in enumerate(points):
if obj == None: continue
yield (i, self._styler.get_bbox(fshape.Shape.decompress(obj)), obj)
self._index_rtree = Rtree(generator_function(self._shapes))
timer.end("loading shapes.fcad")
def press(self, screen, point):
timer.start("press")
if self._object_index != None:
id = self._selected_objects[self._object_index]
found = list(reversed(screen.get_canvas().find_objects_at_position(point))) # apverciame kad pirmi eitu tie kuriuos nupaiseme veliausiai
if id in found:
self._drag_object_id = id # start dragging object
self._drag_object_point = point # start of the dragging
else:
self._drag_object_id = None
timer.end("press")
def load_pickle(self, file_path):
timer.start("loading shapes.p")
if os.path.exists(file_path):
self._shapes = pickle.load(open(file_path, "rb"))
if len(self._shapes):
def generator_function(points):
for i, obj in enumerate(points):
if obj == None: continue
yield (i, self._styler.get_bbox(fshape.Shape.decompress(obj)), obj)
self._index_rtree = Rtree(generator_function(self._shapes))
timer.end("loading shapes.p")
def draw_object(self, ctx, id, fill_or_stroke=True):
#self.apply_transforms(ctx) # drag and scale
self._painter.setup(ctx, transform={"translate":self.get_offset(), "scale": self.get_ratio()})
elements_zindex = self._styler.create_zindex([id])
timer.start("draw single object")
for zindex in sorted(elements_zindex.keys()):
for element in elements_zindex[zindex]:
self._painter.draw(element, update=elements_zindex, fill_or_stroke=fill_or_stroke) # kazka visada nupaiso - bet dar papildomai gali iterpti elementu su didesniu zindex'u
timer.end("draw single object")
def louvainCommunityDetection(f, ft, gnx):
start = timer.start(ft, 'Louvain')
bp = community.best_partition(gnx)
comSizeBp = getCommunitySize(gnx, bp)
timer.stop(ft, start)
writeTofile(comSizeBp, f)
return comSizeBp
def k_core(f,ft,gnx):
start = timer.start(ft, 'K-Core')
result = nx.core_number(gnx)
timer.stop(ft, start)
for k in result:
f.writelines(str(k) + ',' + str(result[k]) + '\n');
return result
def page_rank(gnx, f, ft):
start = timer.start(ft, 'Page Rank')
page_rank_values = nx.pagerank(gnx, alpha=0.9)
timer.stop(ft, start)
for k in page_rank_values.keys():
f.writelines(str(k) + ',' + str(page_rank_values[k]) + '\n')
return page_rank_values
def attractor_basin(gnx, f, ft):
if(not gnx.is_directed()):
return
start = timer.start(ft, 'Attractor Basin')
attractor_dict = calc_attractor_basin(gnx)
timer.stop(ft, start)
for k in attractor_dict.keys():
f.writelines(str(k) + ',' + str(attractor_dict[k]) + '\n')
return attractor_dict
def hierarchy_energy(gnx, f, ft):
start = timer.start(ft, 'hierarchyEnergy')
hierarchyEnergy_list, vet_index = calculate_hierarchyEnergy_index(gnx)
timer.stop(ft, start)
#writing the results in to file
for n in range(0, len(vet_index)):
f.writelines(
str(vet_index[n]) + ',' + str(hierarchyEnergy_list[n][0]) + '\n')
return hierarchyEnergy_list
def mask_iou(mask1, mask2, iscrowd=False):
"""
Inputs inputs are matricies of size _ x N. Output is size _1 x _2.
Note: if iscrowd is True, then mask2 should be the crowd.
"""
timer.start('Mask IoU')
intersection = torch.matmul(mask1, mask2.t())
area1 = torch.sum(mask1, dim=1).view(1, -1)
area2 = torch.sum(mask2, dim=1).view(1, -1)
union = (area1.t() + area2) - intersection
if iscrowd:
# Make sure to brodcast to the right dimension
ret = intersection / area1.t()
else:
ret = intersection / union
timer.stop('Mask IoU')
return ret.cpu()
def release(self, screen, point):
timer.start("release")
if self._drag_object_id != None: # object drag end
offset = (point.x - self._drag_object_point.x, point.y - self._drag_object_point.y)
if offset != (0,0):
Invoker.execute(MoveShapeCommand(self, screen, self._drag_object_id, offset))
text = "Object %i was moved." % self._drag_object_id
self._drag_object_id = None
return text
shape_id = self.get_next(screen, point)
if shape_id != None:
self.show_handlers(screen, ScreenSelectedObject(shape_id))
timer.end("release")
return "%i:%i of %i" % (self._object_index, shape_id, len(self._selected_objects))
else:
timer.end("release")
return "No objects found"
def general_information_undirected(gnx, f, ft):
degrees = []
start = timer.start(ft, 'Genral information')
nodes = gnx.nodes()
[degrees.append([n, gnx.degree(n)]) for n in nodes]
timer.stop(ft, start)
[f.writelines(str(degree[0]) + ',' + str(degree[1]) + '\n') for degree in degrees]
map_degree = {}
for degree in degrees:
map_degree[degree[0]] = [degree[1]]
return map_degree
def find_all_motifs(f, ft, ggt, motifs_number):
motifs_veriations = get_motif_veriation_list(motifs_number)
start = timer.start(ft, 'Find Motifs ' + str(motifs_number) + ' ')
result = gt.clustering.motifs(ggt,
motif_list=motifs_veriations,
k=motifs_number,
return_maps=True)
timer.stop(ft, start)
return parse_motif_result(f, ft, ggt, motifs_number, result,
motifs_veriations)
def flow_mesure(f, ft, gnx):
start = timer.start(ft, 'Flow Mesure')
flow_map = calculate_flow_index(gnx)
timer.stop(ft, start)
for n in flow_map:
f.writelines(str(n)+','+str(flow_map[n]) + '\n')
return flow_map
def bfs_distance_distribution(f, ft, gnx):
start = timer.start(ft, 'BFS distance distribution')
bfs_dist = calc_bfs_dist(gnx)
dist_moments = {}
for key in bfs_dist.keys():
lst = []
lst.append(float(np.mean(bfs_dist[key])))
lst.append(float(np.std(bfs_dist[key])))
dist_moments[key] = lst
timer.stop(ft, start)
write_bfs_moments_to_file(dist_moments, f)
return dist_moments
def general_information_directed(gnx, f, ft):
out_deg = []
in_deg = []
start = timer.start(ft,'Genral information')
nodes = gnx.nodes()
[out_deg.append([n, gnx.out_degree(n)]) for n in nodes]
[in_deg.append([n, gnx.in_degree(n)]) for n in nodes]
timer.stop(ft,start)
[f.writelines(str(i) + ',' + str(in_deg[i][1]) +',' + str(out_deg[i][1]) + '\n') for i in nodes]
map_degree ={}
for n in nodes:
map_degree[n] = [in_deg[n][1], out_deg[n][1]]
return map_degree
def draw_100(self, ctx, area):
"""100% draw for printing, no scale, area in user coordinates"""
page_area = Area(0, 0, area.width, area.height)
#ctx.rectangle(0, 0, area.width, area.height)
#ctx.clip() # tam kad nepaisytu uzh sito staciakampio ribu (tada gaunasi dubliuotos linijos)
#self.background(ctx, page_area) # paint white background
self._painter.background(ctx, page_area, color=(1,1,1), clip=True) # paint white background
self._painter.setup(ctx, transform={"translate":(-area.x, -area.y)})
radius = 0 #self.pixel_radius + self.line_width # ne cia reikia prideti radiusa, o ten kur dedame i cavas'a shape'us
elements = self._index_rtree.intersection((area.x-radius, area.y-radius, area.x+area.width+radius, area.y+area.height+radius))
elements_zindex = self._styler.create_zindex(elements) # jis yra pilnas visu galimu zindex'u sarasas - pradzioje dalis ju gali buti ir tusti []
timer.start("draw100")
for zindex in sorted(elements_zindex.keys()):
for element in elements_zindex[zindex]:
self._painter.draw(element, update=elements_zindex) # kazka visada nupaiso - bet dar papildomai gali iterpti elementu su didesniu zindex'u
# tie elementai bus nupaisomi veliau.
timer.end("draw100")
def find_all_circuits(f, ft, ggt):
start = timer.start(ft, 'Find Cycles')
circuits = graph_tool.topology.all_circuits(ggt)
timer.stop(ft, start)
for c in circuits:
first = True
for v in c:
if (first):
f.writelines('[' + str(ggt.vp.id[v]))
first = False
else:
f.writelines(',' + str(ggt.vp.id[v]))
f.writelines(']\n')
def betweenness_centrality(ggt, f, ft, normalized=False):
b_prop = ggt.new_vertex_property('float')
ggt.vp.bc = b_prop
start = timer.start(ft, 'Betweenness Centrality')
graph_tool.centrality.betweenness(ggt, vprop=b_prop, norm=normalized)
timer.stop(ft, start)
for v in ggt.vertices():
f.writelines(ggt.vp.id[v] + ',' + str(ggt.vp.bc[v]) + '\n')
graph_tool.centrality.betweenness(ggt, norm=False)
nx.betweenness_centrality(gnx, normalized=False)
def add_random_points(self, number, area, generator=True):
"""Kai generator=False - sunaudoja maziau atminties ikrovimo metu, bet trunka gerokai leciau
"""
self._shapes = []
timer.start("Adding random data")
from random import randint
for x in range(0, number):
color = 65536 * randint(0,255) + 256 * randint(0,255) + randint(0,255) # RGBint
x, y = randint(2, area.width), randint(2, area.height)
if not generator: # darysime rtree.add kiekvienam taskui atskirai
self.add(fshape.Shape(1, Point(x, y), color=color))
else:
self._shapes.append((1, color, x, y))
if generator:
def generator_function(points):
for i, obj in enumerate(points):
yield (i, (obj[2], obj[3], obj[2], obj[3]), obj)
self._index_rtree = Rtree(generator_function(self._shapes))
timer.end("Adding random data")
def load_ocad_file(self, file_path, generator=True):
#import ocadfile
#self.add(Shape(1, Point(0, 0), symbol="graphics")) # koordinaciu centras
self._prj = GisProjection()
self._zoom_level = self._ratio_index.index(4)
of = OcadFile(file_path, self._prj)
#self._styler.load_ocad_symbols(of, prj)
timer.start("Adding ocad symbols")
self._styler.set_symbols_style(of.get_symbols_style())
timer.end("Adding ocad symbols")
timer.start("Adding ocad elements")
shapes = of.get_shapes()
print "Shapes: ", len(shapes)
for shape in shapes:
self.add(shape)
timer.end("Adding ocad elements")
self.center()
def load_shape_file(self, file_path, generator=True):
import shapefile
from random import randint
sf = shapefile.Reader(file_path)
print "Number of shapes: ", sf.numRecords
self.add(fshape.Shape(1, Point(0, 0), style={"z-index":99})) # koordinaciu centras
if self.prj.scale == 1: # pirmas kartas
prj = GisProjection(self, sf.bbox)
#po sito ciklo jau turesime zemelapio ribas
center = prj.map_to_user(prj.get_center())
self.center(center)
self.prj = prj
timer.start("Adding shapes")
symbol = sf.shapeName.split("/")[-1]
self._styler.set_symbol_style(symbol, {"color": (randint(0,255),randint(0,255),randint(0,255))})
for shape in sf.ogis_shapes(self.prj):
self.add(fshape.Shape(shape.shapeType, shape.points, symbol=symbol))
timer.end("Adding shapes")
def find_all_motifs(f, ft, gnx, motif_path, motifs_number=3):
gnx_copy = gnx.copy()
start = timer.start(ft, 'Find Motifs ' + str(motifs_number) + ' ')
if motifs_number == 3:
motifsHist = find_motifs_3(gnx_copy, motif_path)
if motifs_number == 4:
motifsHist = find_motifs_4(gnx_copy, motif_path)
timer.stop(ft, start)
print 'start write to file: ' + str(datetime.now())
for i in motifsHist:
line = str(i)
for h in motifsHist[i]:
line = line + ',' + str(h)
f.writelines(line + '\n')
print 'finish write to file: ' + str(datetime.now())
return motifsHist
def prep_metrics(ap_data, nms_outs, gt, gt_masks, h, w, num_crowd, image_id, make_json, cocoapi):
""" Returns a list of APs for this image, with each element being for a class """
with timer.env('After NMS'):
class_ids, classes, boxes, masks = after_nms(nms_outs, h, w)
if class_ids.size(0) == 0:
return
class_ids = list(class_ids.cpu().numpy().astype(int))
classes = list(classes.cpu().numpy().astype(float))
masks = masks.view(-1, h * w).cuda() if cuda else masks.view(-1, h * w)
boxes = boxes.cuda() if cuda else boxes
if cocoapi:
with timer.env('Output json'):
boxes = boxes.cpu().numpy()
masks = masks.view(-1, h, w).cpu().numpy()
for i in range(masks.shape[0]):
# Make sure that the bounding box actually makes sense and a mask was produced
if (boxes[i, 3] - boxes[i, 1]) * (boxes[i, 2] - boxes[i, 0]) > 0:
make_json.add_bbox(image_id, class_ids[i], boxes[i, :], classes[i])
make_json.add_mask(image_id, class_ids[i], masks[i, :, :], classes[i])
return
with timer.env('Prepare gt'):
gt_boxes = torch.Tensor(gt[:, :4])
gt_boxes[:, [0, 2]] *= w
gt_boxes[:, [1, 3]] *= h
gt_classes = list(gt[:, 4].astype(int))
gt_masks = torch.Tensor(gt_masks).view(-1, h * w)
if num_crowd > 0:
split = lambda x: (x[-num_crowd:], x[:-num_crowd])
crowd_boxes, gt_boxes = split(gt_boxes)
crowd_masks, gt_masks = split(gt_masks)
crowd_classes, gt_classes = split(gt_classes)
with timer.env('Eval Setup'):
num_pred = len(class_ids)
num_gt = len(gt_classes)
mask_iou_cache = mask_iou(masks, gt_masks)
bbox_iou_cache = bbox_iou(boxes.float(), gt_boxes.float())
if num_crowd > 0:
crowd_mask_iou_cache = mask_iou(masks, crowd_masks, iscrowd=True)
crowd_bbox_iou_cache = bbox_iou(boxes.float(), crowd_boxes.float(), iscrowd=True)
else:
crowd_mask_iou_cache = None
crowd_bbox_iou_cache = None
iou_types = [('box', lambda i, j: bbox_iou_cache[i, j].item(), lambda i, j: crowd_bbox_iou_cache[i, j].item()),
('mask', lambda i, j: mask_iou_cache[i, j].item(), lambda i, j: crowd_mask_iou_cache[i, j].item())]
timer.start('Main loop')
for _class in set(class_ids + gt_classes):
num_gt_for_class = sum([1 for x in gt_classes if x == _class])
for iouIdx in range(len(iou_thresholds)):
iou_threshold = iou_thresholds[iouIdx]
for iou_type, iou_func, crowd_func in iou_types:
gt_used = [False] * len(gt_classes)
ap_obj = ap_data[iou_type][iouIdx][_class]
ap_obj.add_gt_positives(num_gt_for_class)
for i in range(num_pred):
if class_ids[i] != _class:
continue
max_iou_found = iou_threshold
max_match_idx = -1
for j in range(num_gt):
if gt_used[j] or gt_classes[j] != _class:
continue
iou = iou_func(i, j)
if iou > max_iou_found:
max_iou_found = iou
max_match_idx = j
if max_match_idx >= 0:
gt_used[max_match_idx] = True
ap_obj.push(classes[i], True)
else:
# If the detection matches a crowd, we can just ignore it
matched_crowd = False
if num_crowd > 0:
for j in range(len(crowd_classes)):
if crowd_classes[j] != _class:
continue
iou = crowd_func(i, j)
if iou > iou_threshold:
matched_crowd = True
break
# All this crowd code so that we can make sure that our eval code gives the
# same result as COCOEval. There aren't even that many crowd annotations to
# begin with, but accuracy is of the utmost importance.
if not matched_crowd:
ap_obj.push(classes[i], False)
timer.stop('Main loop')
def evaluate(net, cfg, step=None):
dataset = COCODetection(cfg, mode='val')
data_loader = data.DataLoader(dataset,
1,
num_workers=4,
shuffle=False,
pin_memory=True,
collate_fn=val_collate)
ds = len(data_loader)
progress_bar = ProgressBar(40, ds)
timer.reset()
ap_data = {
'box': [[APDataObject() for _ in cfg.class_names] for _ in iou_thres],
'mask': [[APDataObject() for _ in cfg.class_names] for _ in iou_thres]
}
with torch.no_grad():
for i, (img, gt, gt_masks, img_h, img_w) in enumerate(data_loader):
if i == 1:
timer.start()
if cfg.cuda:
img, gt, gt_masks = img.cuda(), gt.cuda(), gt_masks.cuda()
with timer.counter('forward'):
class_p, box_p, coef_p, proto_p, anchors = net(img)
with timer.counter('nms'):
ids_p, class_p, box_p, coef_p, proto_p = nms(
class_p, box_p, coef_p, proto_p, anchors, cfg)
with timer.counter('after_nms'):
ids_p, class_p, boxes_p, masks_p = after_nms(
ids_p, class_p, box_p, coef_p, proto_p, img_h, img_w)
if ids_p is None:
continue
with timer.counter('metric'):
ids_p = list(ids_p.cpu().numpy().astype(int))
class_p = list(class_p.cpu().numpy().astype(float))
if cfg.coco_api:
boxes_p = boxes_p.cpu().numpy()
masks_p = masks_p.cpu().numpy()
for j in range(masks_p.shape[0]):
if (boxes_p[j, 3] - boxes_p[j, 1]) * (
boxes_p[j, 2] - boxes_p[j, 0]) > 0:
make_json.add_bbox(dataset.ids[i], ids_p[j],
boxes_p[j, :], class_p[j])
make_json.add_mask(dataset.ids[i], ids_p[j],
masks_p[j, :, :], class_p[j])
else:
prep_metrics(ap_data, ids_p, class_p, boxes_p, masks_p, gt,
gt_masks, img_h, img_w, iou_thres)
aa = time.perf_counter()
if i > 0:
batch_time = aa - temp
timer.add_batch_time(batch_time)
temp = aa
if i > 0:
t_t, t_d, t_f, t_nms, t_an, t_me = timer.get_times(
['batch', 'data', 'forward', 'nms', 'after_nms', 'metric'])
fps, t_fps = 1 / (t_d + t_f + t_nms + t_an), 1 / t_t
bar_str = progress_bar.get_bar(i + 1)
print(
f'\rTesting: {bar_str} {i + 1}/{ds}, fps: {fps:.2f} | total fps: {t_fps:.2f} | '
f't_t: {t_t:.3f} | t_d: {t_d:.3f} | t_f: {t_f:.3f} | t_nms: {t_nms:.3f} | '
f't_after_nms: {t_an:.3f} | t_metric: {t_me:.3f}',
end='')
if cfg.coco_api:
make_json.dump()
print(
f'\nJson files dumped, saved in: \'results/\', start evaluating.'
)
gt_annotations = COCO(cfg.val_ann)
bbox_dets = gt_annotations.loadRes(f'results/bbox_detections.json')
mask_dets = gt_annotations.loadRes(f'results/mask_detections.json')
print('\nEvaluating BBoxes:')
bbox_eval = COCOeval(gt_annotations, bbox_dets, 'bbox')
bbox_eval.evaluate()
bbox_eval.accumulate()
bbox_eval.summarize()
print('\nEvaluating Masks:')
bbox_eval = COCOeval(gt_annotations, mask_dets, 'segm')
bbox_eval.evaluate()
bbox_eval.accumulate()
bbox_eval.summarize()
else:
table, box_row, mask_row = calc_map(ap_data,
iou_thres,
len(cfg.class_names),
step=step)
print(table)
return table, box_row, mask_row
def main():
parser = argparse.ArgumentParser(description='YOLACT Detection.')
parser.add_argument('--weight', default='weights/best_30.5_res101_coco_392000.pth', type=str)
parser.add_argument('--image', default=None, type=str, help='The folder of images for detecting.')
parser.add_argument('--video', default=None, type=str, help='The path of the video to evaluate.')
parser.add_argument('--img_size', type=int, default=544, help='The image size for validation.')
parser.add_argument('--traditional_nms', default=False, action='store_true', help='Whether to use traditional nms.')
parser.add_argument('--hide_mask', default=False, action='store_true', help='Hide masks in results.')
parser.add_argument('--hide_bbox', default=False, action='store_true', help='Hide boxes in results.')
parser.add_argument('--hide_score', default=False, action='store_true', help='Hide scores in results.')
parser.add_argument('--cutout', default=False, action='store_true', help='Cut out each object and save.')
parser.add_argument('--save_lincomb', default=False, action='store_true', help='Show the generating process of masks.')
parser.add_argument('--no_crop', default=False, action='store_true',
help='Do not crop the output masks with the predicted bounding box.')
parser.add_argument('--real_time', default=False, action='store_true', help='Show the detection results real-timely.')
parser.add_argument('--visual_thre', default=0.3, type=float,
help='Detections with a score under this threshold will be removed.')
args = parser.parse_args()
prefix = re.findall(r'best_\d+\.\d+_', args.weight)[0]
suffix = re.findall(r'_\d+\.pth', args.weight)[0]
args.cfg = args.weight.split(prefix)[-1].split(suffix)[0]
cfg = get_config(args, mode='detect')
net = Yolact(cfg)
net.load_weights(cfg.weight, cfg.cuda)
net.eval()
if cfg.cuda:
cudnn.benchmark = True
cudnn.fastest = True
net = net.cuda()
# detect images
if cfg.image is not None:
dataset = COCODetection(cfg, mode='detect')
data_loader = data.DataLoader(dataset, 1, num_workers=2, shuffle=False, pin_memory=True, collate_fn=detect_collate)
ds = len(data_loader)
assert ds > 0, 'No .jpg images found.'
progress_bar = ProgressBar(40, ds)
timer.reset()
for i, (img, img_origin, img_name) in enumerate(data_loader):
if i == 1:
timer.start()
if cfg.cuda:
img = img.cuda()
img_h, img_w = img_origin.shape[0:2]
with torch.no_grad(), timer.counter('forward'):
class_p, box_p, coef_p, proto_p = net(img)
with timer.counter('nms'):
ids_p, class_p, box_p, coef_p, proto_p = nms(class_p, box_p, coef_p, proto_p, net.anchors, cfg)
with timer.counter('after_nms'):
ids_p, class_p, boxes_p, masks_p = after_nms(ids_p, class_p, box_p, coef_p, proto_p,
img_h, img_w, cfg, img_name=img_name)
with timer.counter('save_img'):
img_numpy = draw_img(ids_p, class_p, boxes_p, masks_p, img_origin, cfg, img_name=img_name)
cv2.imwrite(f'results/images/{img_name}', img_numpy)
aa = time.perf_counter()
if i > 0:
batch_time = aa - temp
timer.add_batch_time(batch_time)
temp = aa
if i > 0:
t_t, t_d, t_f, t_nms, t_an, t_si = timer.get_times(['batch', 'data', 'forward',
'nms', 'after_nms', 'save_img'])
fps, t_fps = 1 / (t_d + t_f + t_nms + t_an), 1 / t_t
bar_str = progress_bar.get_bar(i + 1)
print(f'\rTesting: {bar_str} {i + 1}/{ds}, fps: {fps:.2f} | total fps: {t_fps:.2f} | '
f't_t: {t_t:.3f} | t_d: {t_d:.3f} | t_f: {t_f:.3f} | t_nms: {t_nms:.3f} | '
f't_after_nms: {t_an:.3f} | t_save_img: {t_si:.3f}', end='')
print('\nFinished, saved in: results/images.')
# detect videos
elif cfg.video is not None:
vid = cv2.VideoCapture(cfg.video)
target_fps = round(vid.get(cv2.CAP_PROP_FPS))
frame_width = round(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = round(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
num_frames = round(vid.get(cv2.CAP_PROP_FRAME_COUNT))
name = cfg.video.split('/')[-1]
video_writer = cv2.VideoWriter(f'results/videos/{name}', cv2.VideoWriter_fourcc(*"mp4v"), target_fps,
(frame_width, frame_height))
progress_bar = ProgressBar(40, num_frames)
timer.reset()
t_fps = 0
for i in range(num_frames):
if i == 1:
timer.start()
frame_origin = vid.read()[1]
img_h, img_w = frame_origin.shape[0:2]
frame_trans = val_aug(frame_origin, cfg.img_size)
frame_tensor = torch.tensor(frame_trans).float()
if cfg.cuda:
frame_tensor = frame_tensor.cuda()
with torch.no_grad(), timer.counter('forward'):
class_p, box_p, coef_p, proto_p = net(frame_tensor.unsqueeze(0))
with timer.counter('nms'):
ids_p, class_p, box_p, coef_p, proto_p = nms(class_p, box_p, coef_p, proto_p, net.anchors, cfg)
with timer.counter('after_nms'):
ids_p, class_p, boxes_p, masks_p = after_nms(ids_p, class_p, box_p, coef_p, proto_p, img_h, img_w, cfg)
with timer.counter('save_img'):
frame_numpy = draw_img(ids_p, class_p, boxes_p, masks_p, frame_origin, cfg, fps=t_fps)
if cfg.real_time:
cv2.imshow('Detection', frame_numpy)
cv2.waitKey(1)
else:
video_writer.write(frame_numpy)
aa = time.perf_counter()
if i > 0:
batch_time = aa - temp
timer.add_batch_time(batch_time)
temp = aa
if i > 0:
t_t, t_d, t_f, t_nms, t_an, t_si = timer.get_times(['batch', 'data', 'forward',
'nms', 'after_nms', 'save_img'])
fps, t_fps = 1 / (t_d + t_f + t_nms + t_an), 1 / t_t
bar_str = progress_bar.get_bar(i + 1)
print(f'\rDetecting: {bar_str} {i + 1}/{num_frames}, fps: {fps:.2f} | total fps: {t_fps:.2f} | '
f't_t: {t_t:.3f} | t_d: {t_d:.3f} | t_f: {t_f:.3f} | t_nms: {t_nms:.3f} | '
f't_after_nms: {t_an:.3f} | t_save_img: {t_si:.3f}', end='')
if not cfg.real_time:
print(f'\n\nFinished, saved in: results/videos/{name}')
vid.release()
video_writer.release()
args = parser.parse_args()
cfg = Config(args=args.__dict__, mode='Detect')
cfg.show_config()
test_dataset = Seg_dataset(cfg)
model = DLASeg(cfg).cuda()
model.load_state_dict(torch.load(cfg.trained_model), strict=True)
model.eval()
timer.reset()
with torch.no_grad():
for i, (data_tuple, img_name) in enumerate(test_dataset):
if i == 1:
timer.start() # timer does not timing for the first image.
img_name = img_name.replace('tif', 'png')
image = data_tuple[0].unsqueeze(0).cuda().detach()
with timer.counter('forward'):
output = model(image)
with timer.counter('save result'):
pred = torch.max(output, 1)[1].squeeze(0).cpu().numpy()
if cfg.colorful:
pred = PALLETE[pred].astype('uint8')
cv2.imwrite(f'results/{img_name}', pred)
if cfg.overlay:
pred = PALLETE[pred].astype('uint8')
sess = ort.InferenceSession(cfg.weight)
input_name = sess.get_inputs()[0].name
# detect images
if cfg.image is not None:
dataset = COCODetection(cfg, mode='detect')
data_loader = data.DataLoader(dataset, 1, num_workers=4, shuffle=False,
pin_memory=True, collate_fn=detect_onnx_collate)
ds = len(data_loader)
assert ds > 0, 'No .jpg images found.'
progress_bar = ProgressBar(40, ds)
timer.reset()
for i, (img, img_origin, img_name) in enumerate(data_loader):
if i == 1:
timer.start()
img_h, img_w = img_origin.shape[0:2]
with timer.counter('forward'):
class_p, box_p, coef_p, proto_p, anchors = sess.run(None, {input_name: img})
with timer.counter('nms'):
ids_p, class_p, box_p, coef_p, proto_p = nms_numpy(class_p, box_p, coef_p, proto_p, anchors, cfg)
with timer.counter('after_nms'):
ids_p, class_p, boxes_p, masks_p = after_nms_numpy(ids_p, class_p, box_p, coef_p,
proto_p, img_h, img_w, cfg)
with timer.counter('save_img'):
img_numpy = draw_img(ids_p, class_p, boxes_p, masks_p, img_origin, cfg, img_name=img_name)
# if cfg.MODEL.USE_SYNCBN: # TODO: figure this out
# model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optim = Optimizer(model, cfg)
checkpointer = Checkpointer(cfg, model.module, optim.optimizer)
start_iter = int(cfg.resume.split('_')[-1].split('.')[0]) if cfg.resume else 0
data_loader = make_data_loader(cfg, start_iter=start_iter)
max_iter = len(data_loader) - 1
timer.reset()
main_gpu = dist.get_rank() == 0
num_gpu = dist.get_world_size()
for i, (img_list_batch, box_list_batch) in enumerate(data_loader, start_iter):
if main_gpu and i == start_iter + 1:
timer.start()
optim.update_lr(step=i)
img_tensor_batch = torch.stack([aa.img for aa in img_list_batch], dim=0).cuda()
for box_list in box_list_batch:
box_list.to_cuda()
with timer.counter('for+loss'):
category_loss, box_loss, iou_loss = model(img_tensor_batch, box_list_batch)
all_loss = torch.stack([category_loss, box_loss, iou_loss], dim=0)
dist.reduce(all_loss, dst=0)
if main_gpu: # get the mean loss across all GPUS
l_c = all_loss[0].item() / num_gpu # seems when printing, need to call .item(), not sure
l_b = all_loss[1].item() / num_gpu
def inference(model, cfg, during_training=False):
model.eval()
predictions, coco_results = {}, []
val_loader = make_data_loader(cfg, during_training=during_training)
dataset = val_loader.dataset
dl = len(val_loader)
bar = ProgressBar(length=40, max_val=dl)
timer.reset()
with torch.no_grad():
for i, (img_list_batch, _) in enumerate(val_loader):
if i == 1:
timer.start()
with timer.counter('forward'):
img_tensor_batch = torch.stack(
[aa.img for aa in img_list_batch], dim=0).cuda()
c_pred, box_pred, iou_pred, anchors = model(img_tensor_batch)
with timer.counter('post_process'):
resized_size = [aa.resized_size for aa in img_list_batch]
pred_batch = post_process(cfg, c_pred, box_pred, iou_pred,
anchors, resized_size)
with timer.counter('accumulate'):
for pred in pred_batch:
pred.to_cpu()
for img_list, pred in zip(img_list_batch, pred_batch):
if pred.box.shape[0] == 0:
continue
original_id = dataset.id_img_map[img_list.id]
pred.resize(img_list.ori_size)
pred.convert_mode("x1y1wh")
boxes = pred.box.tolist()
score = pred.score.tolist()
label = pred.label.tolist()
mapped_labels = [dataset.to_category_id[i] for i in label]
coco_results.extend([{
"image_id": original_id,
"category_id": mapped_labels[k],
"bbox": box,
"score": score[k]
} for k, box in enumerate(boxes)])
aa = time.perf_counter()
if i > 0:
batch_time = aa - temp
timer.add_batch_time(batch_time)
time_name = [
'batch', 'data', 'forward', 'post_process', 'accumulate'
]
t_t, t_d, t_f, t_pp, t_acc = timer.get_times(time_name)
fps, t_fps = 1 / (t_d + t_f + t_pp), 1 / t_t
bar_str = bar.get_bar(i + 1)
print(
f'\rTesting: {bar_str} {i + 1}/{dl}, fps: {fps:.2f} | total fps: {t_fps:.2f} | t_t: {t_t:.3f} | '
f't_d: {t_d:.3f} | t_f: {t_f:.3f} | t_pp: {t_pp:.3f} | t_acc: {t_acc:.3f}',
end='')
temp = aa
print('\n\nTest ended, doing evaluation...')
json_name = cfg.weight.split('/')[-1].split('.')[0]
file_path = f'results/{json_name}.json'
with open(file_path, "w") as f:
json.dump(coco_results, f)
coco_dt = dataset.coco.loadRes(file_path)
if cfg.val_api == 'Improved COCO':
from my_cocoeval.cocoeval import SelfEval
bbox_eval = SelfEval(dataset.coco, coco_dt, all_points=True)
else:
from pycocotools.cocoeval import COCOeval
bbox_eval = COCOeval(dataset.coco, coco_dt, iouType='bbox')
bbox_eval.evaluate()
bbox_eval.accumulate()
bbox_eval.summarize()
if not during_training:
if cfg.val_api == 'Improved COCO':
bbox_eval.draw_curve()
else:
compute_thre_per_class(bbox_eval)
def prep_metrics(ap_data,
dets,
img,
gt,
gt_masks,
h,
w,
num_crowd,
image_id,
detections: Detections = None):
""" Returns a list of APs for this image, with each element being for a class """
if not args.output_coco_json:
with timer.env('Prepare gt'):
gt_boxes = jt.array(gt[:, :4])
gt_boxes[:, [0, 2]] *= w
gt_boxes[:, [1, 3]] *= h
gt_classes = list(gt[:, 4].astype(int))
gt_masks = jt.array(gt_masks).view(-1, h * w)
if num_crowd > 0:
split = lambda x: (x[-num_crowd:], x[:-num_crowd])
crowd_boxes, gt_boxes = split(gt_boxes)
crowd_masks, gt_masks = split(gt_masks)
crowd_classes, gt_classes = split(gt_classes)
with timer.env('Postprocess'):
classes, scores, boxes, masks = postprocess(
dets,
w,
h,
crop_masks=args.crop,
score_threshold=args.score_threshold)
if classes.size(0) == 0:
return
classes = list(classes.numpy().astype(int))
if isinstance(scores, list):
box_scores = list(scores[0].numpy().astype(float))
mask_scores = list(scores[1].numpy().astype(float))
else:
scores = list(scores.numpy().astype(float))
box_scores = scores
mask_scores = scores
masks = masks.view(-1, h * w)
boxes = boxes
#print('GG')
if args.output_coco_json:
with timer.env('JSON Output'):
boxes = boxes.numpy()
masks = masks.view(-1, h, w).numpy()
for i in range(masks.shape[0]):
# Make sure that the bounding box actually makes sense and a mask was produced
if (boxes[i, 3] - boxes[i, 1]) * (boxes[i, 2] -
boxes[i, 0]) > 0:
detections.add_bbox(image_id, classes[i], boxes[i, :],
box_scores[i])
detections.add_mask(image_id, classes[i], masks[i, :, :],
mask_scores[i])
return
#print('GG')
with timer.env('Eval Setup'):
num_pred = len(classes)
num_gt = len(gt_classes)
mask_iou_cache = _mask_iou(masks, gt_masks).numpy()
bbox_iou_cache = _bbox_iou(boxes.float(), gt_boxes.float()).numpy()
if num_crowd > 0:
crowd_mask_iou_cache = _mask_iou(masks, crowd_masks,
iscrowd=True).numpy()
crowd_bbox_iou_cache = _bbox_iou(boxes.float(),
crowd_boxes.float(),
iscrowd=True).numpy()
else:
crowd_mask_iou_cache = None
crowd_bbox_iou_cache = None
box_indices = sorted(range(num_pred), key=lambda i: -box_scores[i])
mask_indices = sorted(box_indices, key=lambda i: -mask_scores[i])
iou_types = [('box', lambda i, j: bbox_iou_cache[i, j].item(),
lambda i, j: crowd_bbox_iou_cache[i, j].item(),
lambda i: box_scores[i], box_indices),
('mask', lambda i, j: mask_iou_cache[i, j].item(),
lambda i, j: crowd_mask_iou_cache[i, j].item(),
lambda i: mask_scores[i], mask_indices)]
#print('GG')
#print(bbox_iou_cache)
timer.start('Main loop')
for _class in set(classes + gt_classes):
ap_per_iou = []
num_gt_for_class = sum([1 for x in gt_classes if x == _class])
for iouIdx in range(len(iou_thresholds)):
iou_threshold = iou_thresholds[iouIdx]
for iou_type, iou_func, crowd_func, score_func, indices in iou_types:
gt_used = [False] * len(gt_classes)
ap_obj = ap_data[iou_type][iouIdx][_class]
ap_obj.add_gt_positives(num_gt_for_class)
for i in indices:
if classes[i] != _class:
continue
max_iou_found = iou_threshold
max_match_idx = -1
for j in range(num_gt):
if gt_used[j] or gt_classes[j] != _class:
continue
iou = iou_func(i, j)
if iou > max_iou_found:
max_iou_found = iou
max_match_idx = j
if max_match_idx >= 0:
gt_used[max_match_idx] = True
ap_obj.push(score_func(i), True)
else:
# If the detection matches a crowd, we can just ignore it
matched_crowd = False
if num_crowd > 0:
for j in range(len(crowd_classes)):
if crowd_classes[j] != _class:
continue
iou = crowd_func(i, j)
if iou > iou_threshold:
matched_crowd = True
break
# All this crowd code so that we can make sure that our eval code gives the
# same result as COCOEval. There aren't even that many crowd annotations to
# begin with, but accuracy is of the utmost importance.
if not matched_crowd:
ap_obj.push(score_func(i), False)
timer.stop('Main loop')
def nms(boxes, scores, overlap=0.5, top_k=200, force_cpu=True):
"""Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
scores: (tensor) The class predscores for the img, Shape:[num_priors].
overlap: (float) The overlap thresh for suppressing unnecessary boxes.
top_k: (int) The Maximum number of box preds to consider.
Return:
The indices of the kept boxes with respect to num_priors.
"""
cuda_enabled = boxes.is_cuda
if force_cpu:
boxes = boxes.cpu()
scores = scores.cpu()
timer.start('NMS')
keep = scores.new(scores.size(0)).zero_().long()
if boxes.numel() == 0:
return keep
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
area = torch.mul(x2 - x1, y2 - y1)
v, idx = scores.sort(0) # sort in ascending order
# I = I[v >= 0.01]
idx = idx[-top_k:] # indices of the top-k largest vals
xx1 = boxes.new()
yy1 = boxes.new()
xx2 = boxes.new()
yy2 = boxes.new()
w = boxes.new()
h = boxes.new()
# keep = torch.Tensor()
count = 0
while idx.numel() > 0:
i = idx[-1] # index of current largest val
# keep.append(i)
keep[count] = i
count += 1
if idx.size(0) == 1:
break
idx = idx[:-1] # remove kept element from view
# load bboxes of next highest vals
torch.index_select(x1, 0, idx, out=xx1)
torch.index_select(y1, 0, idx, out=yy1)
torch.index_select(x2, 0, idx, out=xx2)
torch.index_select(y2, 0, idx, out=yy2)
# store element-wise max with next highest score
xx1 = torch.clamp(xx1, min=x1[i])
yy1 = torch.clamp(yy1, min=y1[i])
xx2 = torch.clamp(xx2, max=x2[i])
yy2 = torch.clamp(yy2, max=y2[i])
w.resize_as_(xx2)
h.resize_as_(yy2)
w = xx2 - xx1
h = yy2 - yy1
# check sizes of xx1 and xx2.. after each iteration
w = torch.clamp(w, min=0.0)
h = torch.clamp(h, min=0.0)
inter = w*h
# IoU = i / (area(a) + area(b) - i)
rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
union = (rem_areas - inter) + area[i]
IoU = inter/union # store result in iou
# keep only elements with an IoU <= overlap
idx = idx[IoU.le(overlap)]
if cuda_enabled:
keep = keep.cuda()
timer.stop()
return keep, count