def nearby(self, coords, radius=None, limit=None, with_properties=True):
"""
Returns records around coords.
Parameters:
coords (tuple): Coordinates like (x, y).
radius (int): Distance in meters.
limit (int): Limits fetched records.
with_properties (bool): Fetch properties.
Returns:
RecordSet
"""
coords = extract_coords(coords)
point = '%.3f,%.3f' % coords
params = {
'properties': with_properties and 1 or 0
}
if radius:
params['radius'] = radius
if limit:
params['limit'] = limit
path = '/api/1.0/search/%s/nearby/%s.json' % (self.name, point)
command = self.pincaster.server
response = command(path, 'GET', data=params)
matches = response.get('matches', [])
return RecordSet([Record(layer=self, **m) for m in matches])
def main():
args = parse_args()
print('Loading ...')
PATH = './data/'
test = pd.read_csv(PATH + 'sample_submission.csv')
#test = test.iloc[:50]
test_images_dir = PATH + 'test_images/{}.jpg'
df_test = test
test_dataset = CarDataset(df_test, test_images_dir, sigma=1, training=False)
load_model = args.load_model
predictions, predictions_dropmask = [], []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
model = torch.load(load_model)
else:
model = torch.load(load_model, map_location='cpu')
if isinstance(model, torch.nn.DataParallel):
model = model.module
test_loader = DataLoader(dataset=test_dataset, batch_size=4, shuffle=False, num_workers=4)
model.eval()
print('Start Evaluation ...')
for img, _, _, _, dropmasks in tqdm(test_loader):
img = img.float().to(device)
dropmasks = dropmasks.data.cpu().numpy()
with torch.no_grad():
output = model(img)
if type(output) is list:
output = output[-1]
output = output['hm'] if type(output) is dict else output
output = output.data.cpu().numpy()
for out, test_mask in zip(output, dropmasks):
# get unprocessed value
coords = extract_coords(out, args.threshold)
s = coords2str(coords)
predictions.append(s)
#test_mask = cv2.resize(test_mask[0], (IMG_WIDTH // MODEL_SCALE, IMG_HEIGHT // MODEL_SCALE))
# test_mask = np.where(test_mask > 255 // 2, 100, 0) # subtract from logits
#print(test_mask.shape, out[0].shape)
#print(out[0].mean())
#print(sth.sum())
#out[0, test_mask > (255 // 2)] = -100
#print(out[0].mean())
#coords = extract_coords(out, args.threshold)
#s = coords2str(coords)
#predictions_dropmask.append(s)
save_dir = load_model.split('/')[:-1]
save_dir = '/'.join(save_dir)
save_submission_file(test.copy(), save_dir, predictions, args.threshold, 'origin')
def nearby(self, coords, radius=None, limit=None, with_properties=True):
"""
Returns records around coords.
:keyword coords: see :attr:`coords`
:keyword radius: see :attr:`radius`
:keyword limit: see :attr:`limit`
:keyword with_properties: see :attr:`with_properties`
.. attribute:: coords
Coordinates like (x, y).
.. attribute:: radius
Distance in meters.
.. attribute:: limit
Limits fetched records.
.. attribute:: with_properties
Fetch properties.
"""
coords = extract_coords(coords)
point = '%.3f,%.3f' % coords
params = {
'properties': with_properties and 1 or 0
}
if radius:
params['radius'] = radius
if limit:
params['limit'] = limit
path = '/api/1.0/search/%s/nearby/%s.json' % (self.name, point)
command = self.pincaster.server
response = command(path, 'GET', data=params)
matches = response.get('matches', [])
return RecordSet([Record(layer=self, **m) for m in matches])
from model import CentResnet
from utils import extract_coords, coords2str
PATH = Config.PATH
device = Config.device
predictions = []
test_images_dir = PATH + 'test_images/{}.jpg'
test = pd.read_csv(PATH + 'sample_submission.csv')
df_test = test
test_dataset = CarDataset(df_test, test_images_dir, False)
test_loader = DataLoader(dataset=test_dataset,
batch_size=4,
shuffle=False,
num_workers=4)
model = CentResnet(8).to(device)
model.load_state_dict(torch.load(Config.save_path))
model.eval()
if __name__ == '__main__':
for img, _, _ in tqdm(test_loader):
with torch.no_grad():
output = model(img.to(device))
output = output.data.cpu().numpy()
for out in output:
coords = extract_coords(out)
s = coords2str(coords)
predictions.append(s)
test = pd.read_csv(PATH + 'sample_submission.csv')
test['PredictionString'] = predictions
test.to_csv('submission.csv', index=False)
def minibatch_(functions, clf,scaler,w, loss__,mse,hinge1,hinge2,full_image,img_nr,alphas,learning_rate,subsamples, mode):
X_p, y_p, inv = get_data_from_img_nr(class_,img_nr, subsamples)
if X_p != []:
boxes = []
ground_truth = inv[0][2]
img_nr = inv[0][0]
print img_nr
if less_features:
X_p = [fts[0:features_used] for fts in X_p]
if os.path.isfile('/var/node436/local/tstahl/Coords_prop_windows/'+ (format(img_nr, "06d")) +'.txt'):
f = open('/var/node436/local/tstahl/Coords_prop_windows/'+ (format(img_nr, "06d")) +'.txt', 'r')
else:
print 'warning'
for line, y in zip(f, inv):
tmp = line.split(',')
coord = []
for s in tmp:
coord.append(float(s))
boxes.append([coord, y[2]])
#assert(len(boxes)<500)
boxes, y_p, X_p = sort_boxes(boxes, y_p, X_p, 0,5000)
if os.path.isfile('/var/node436/local/tstahl/GroundTruth/%s/%s.txt'%(class_,format(img_nr, "06d"))):
gr = open('/var/node436/local/tstahl/GroundTruth/%s/%s.txt'%(class_,format(img_nr, "06d")), 'r')
else:
gr = []
ground_truths = []
for line in gr:
tmp = line.split(',')
ground_truth = []
for s in tmp:
ground_truth.append(int(s))
ground_truths.append(ground_truth)
#prune boxes
pruned_x = []
pruned_y = []
pruned_boxes = []
if prune:
for i, y_ in enumerate(y_p):
if y_ > 0:
pruned_x.append(X_p[i])
pruned_y.append(y_p[i])
pruned_boxes.append(boxes[i])
else:
pruned_x = X_p
pruned_y = y_p
pruned_boxes = boxes
if subsampling and pruned_boxes > subsamples:
pruned_x = pruned_x[0:subsamples]
pruned_y = pruned_y[0:subsamples]
pruned_boxes = pruned_boxes[0:subsamples]
# create_tree
G, levels = create_tree(pruned_boxes)
#prune tree to only have levels which fully cover the image, tested
if prune_fully_covered:
nr_levels_covered = 100
total_size = surface_area(pruned_boxes, levels[0])
for level in levels:
sa = surface_area(pruned_boxes, levels[level])
sa_co = sa/total_size
if sa_co != 1.0:
G.remove_nodes_from(levels[level])
else:
nr_levels_covered = level
levels = {k: levels[k] for k in range(0,nr_levels_covered + 1)}
# prune levels, speedup + performance
levels_tmp = {k:v for k,v in levels.iteritems() if k<prune_tree_levels}
levels_gone = {k:v for k,v in levels.iteritems() if k>=prune_tree_levels}
levels = levels_tmp
#prune tree as well, for patches training
for trash_level in levels_gone.values():
G.remove_nodes_from(trash_level)
coords = []
features = []
f_c = []
f = []
#either subsampling or prune_fully_covered
#assert(subsampling != prune_fully_covered)
if subsampling:
if os.path.isfile('/var/node436/local/tstahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples)):
f_c = open('/var/node436/local/tstahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples), 'r+')
else:
if mode == 'extract_train' or mode == 'extract_test':
print 'coords for %s with %s samples have to be extracted'%(img_nr,subsamples)
f_c = open('/var/node436/local/tstahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples), 'w')
for level in levels:
levl_boxes = extract_coords(levels[level], pruned_boxes)
if levl_boxes != []:
for lvl_box in levl_boxes:
if lvl_box not in coords:
coords.append(lvl_box)
f_c.write('%s,%s,%s,%s'%(lvl_box[0],lvl_box[1],lvl_box[2],lvl_box[3]))
f_c.write('\n')
f_c.close()
print 'features for %s with %s samples have to be extracted'%(img_nr,subsamples)
os.system('export PATH=$PATH:/home/koelma/impala/lib/x86_64-linux-gcc')
os.system('export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/koelma/impala/third.13.03/x86_64-linux/lib')
#print "EuVisual /var/node436/local/tstahl/Images/%s.jpg /var/node436/local/tstahl/Features_prop_windows/Features_upper/sheep_%s_%s.txt --eudata /home/koelma/EuDataBig --imageroifile /var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep_%s_%s.txt"%((format(img_nr, "06d")),format(img_nr, "06d"),subsamples,format(img_nr, "06d"),subsamples)
os.system("EuVisual /var/node436/local/tstahl/Images/%s.jpg /var/node436/local/tstahl/Features_prop_windows/Features_upper/%s_%s_%s.txt --eudata /home/koelma/EuDataBig --imageroifile /var/node436/local/tstahl/Features_prop_windows/upper_levels/%s_%s_%s.txt"%(class_,(format(img_nr, "06d")),format(img_nr, "06d"),subsamples,class_,format(img_nr, "06d"),subsamples))
if os.path.isfile('/var/node436/local/tstahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples)):
f_c = open('/var/node436/local/tstahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples), 'r')
else:
f_c = []
coords = []
if os.path.isfile('/var/node436/local/tstahl/Features_prop_windows/Features_upper/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples)):
f = open('/var/node436/local/tstahl/Features_prop_windows/Features_upper/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples), 'r')
elif prune_fully_covered:
if os.path.isfile('/var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d"))):
f_c = open('/var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d")), 'r+')
else:
if mode == 'extract_train' or mode == 'extract_test':
print 'coords for %s with fully_cover_tree samples have to be extracted'%(img_nr)
f_c = open('/var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d")), 'w')
for level in levels:
levl_boxes = extract_coords(levels[level], pruned_boxes)
if levl_boxes != []:
for lvl_box in levl_boxes:
if lvl_box not in coords:
coords.append(lvl_box)
f_c.write('%s,%s,%s,%s'%(lvl_box[0],lvl_box[1],lvl_box[2],lvl_box[3]))
f_c.write('\n')
f_c.close()
print 'features for %s with fully_cover_tree samples have to be extracted'%(img_nr)
os.system('export PATH=$PATH:/home/koelma/impala/lib/x86_64-linux-gcc')
os.system('export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/koelma/impala/third.13.03/x86_64-linux/lib')
#print "EuVisual /var/node436/local/tstahl/Images/%s.jpg /var/node436/local/tstahl/Features_prop_windows/Features_upper/sheep_%s_%s.txt --eudata /home/koelma/EuDataBig --imageroifile /var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep_%s_%s.txt"%((format(img_nr, "06d")),format(img_nr, "06d"),subsamples,format(img_nr, "06d"),subsamples)
print "EuVisual /var/node436/local/tstahl/Images/%s.jpg /var/node436/local/tstahl/Features_prop_windows/Features_upper/sheep_%s_fully_cover_tree.txt --eudata /home/koelma/EuDataBig --imageroifile /var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep_%s_fully_cover_tree.txt"%((format(img_nr, "06d")),format(img_nr, "06d"),format(img_nr, "06d"))
os.system("EuVisual /var/node436/local/tstahl/Images/%s.jpg /var/node436/local/tstahl/Features_prop_windows/Features_upper/sheep_%s_fully_cover_tree.txt --eudata /home/koelma/EuDataBig --imageroifile /var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep_%s_fully_cover_tree.txt"%((format(img_nr, "06d")),format(img_nr, "06d"),format(img_nr, "06d")))
if os.path.isfile('/var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d"))):
f_c = open('/var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d")), 'r')
else:
f_c = []
coords = []
if os.path.isfile('/var/node436/local/tstahl/Features_prop_windows/Features_upper/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d"))):
f = open('/var/node436/local/tstahl/Features_prop_windows/Features_upper/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d")), 'r')
else:
if os.path.isfile('/var/node436/local/tstahl/Features_prop_windows/Features_upper/sheep%s.txt'%(format(img_nr, "06d"))):
f = open('/var/node436/local/tstahl/Features_prop_windows/Features_upper/sheep%s.txt'%(format(img_nr, "06d")), 'r')
if os.path.isfile('/var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep%s.txt'%(format(img_nr, "06d"))):
f_c = open('/var/node436/local/tstahl/Features_prop_windows/upper_levels/sheep%s.txt'%(format(img_nr, "06d")), 'r+')
if f_c != []:
for i,line in enumerate(f_c):
str_ = line.rstrip('\n').split(',')
cc = []
for s in str_:
cc.append(float(s))
coords.append(cc)
if f != []:
for i,line in enumerate(f):
str_ = line.rstrip('\n').split(',')
ff = []
for s in str_:
ff.append(float(s))
features.append(ff)
#assert len(coords) == len(features)
# append x,y of intersections
if learn_intersections:
for inters,coord in zip(features,coords):
# if inters not in pruned_x:
pruned_x.append(inters)
ol = 0.0
ol = get_intersection_count(coord, ground_truths)
pruned_y.append(ol)
if mode == 'mean_variance':
print 'normalizing'
sum_x += np.array(pruned_x).sum(axis=0)
n_samples += len(pruned_x)
sum_sq_x += (np.array(pruned_x)**2).sum(axis=0)
scaler.partial_fit(pruned_x) # Don't cheat - fit only on training data
return sum_x,n_samples,sum_sq_x, scaler
if less_features:
features = [fts[0:features_used] for fts in features]
#normalize
norm_x = []
if normalize and (mode != 'extract_train' and mode != 'extract_test'):
# for p_x in pruned_x:
# norm_x.append((p_x-mean)/variance)
norm_x = scaler.transform(pruned_x)
if features != []:
features = scaler.transform(features)
else:
norm_x = pruned_x
data = (G, levels, pruned_y, norm_x, pruned_boxes, ground_truths, alphas)
sucs = nx.dfs_successors(G)
predecs = nx.dfs_predecessors(G)
#preprocess: node - children
children = {}
last = -1
for node,children_ in zip(sucs.keys(),sucs.values()):
if node != last+1:
for i in range(last+1,node):
children[i] = []
children[node] = children_
elif node == last +1:
children[node] = children_
last = node
if mode == 'training':
if alphas[0] == 0: #if we don't learn the proposals, we learn just the levels: better, because every level has same importance and faster
print 'training levels', img_nr
for level in levels:
print 'level' , level
if img_nr in functions:
if level in functions[img_nr]:
function = functions[img_nr][level]
else:
function = []
else:
functions[img_nr] = {}
function = []
w, function = tree_level_regression(class_,function,levels,level,features,coords,scaler,w,norm_x,pruned_y,None,predecs,children,pruned_boxes,learning_rate,alphas,img_nr,jans_idea)
if level not in functions[img_nr]:
functions[img_nr][level] = function
return w, len(pruned_y), len(levels)
else: #if we learn proposals, levels with more proposals have more significance...., slow - need to change
print 'training patches', img_nr
print predecs
nodes = list(G.nodes())
for node in nodes:
print node
if node == 0:
w = learn_root(w,norm_x[0],pruned_y[0],learning_rate,alphas)
else:
for num,n in enumerate(levels.values()):
if node in n:
level = num
break
if img_nr in functions:
if level in functions[img_nr]:
function = functions[img_nr][level]
else:
function = []
else:
functions[img_nr] = {}
function = []
#w, function = tree_level_regression(class_,function,levels,level,features,coords,scaler,w,norm_x,pruned_y,node,predecs,children,pruned_boxes,learning_rate,alphas,img_nr)
w, function = constrained_regression(class_,function,features,coords,scaler,w,norm_x,pruned_y,node,predecs,children,pruned_boxes,learning_rate,alphas,img_nr,squared_hinge_loss)
#TODO: train regressor/classifier that predicts/chooses level. Features: level, number of proposals, number of intersections, avg size of proposal, predictions(for regressor), etc.
if level not in functions[img_nr]:
functions[img_nr][level] = function
return w, len(pruned_y), len(G.nodes())
elif mode == 'scikit_train':
clf.partial_fit(norm_x,pruned_y)
return clf
elif mode == 'loss_train':
if alphas[0] == 0: #levels
loss__.append(tree_level_loss(class_,features,coords,scaler, w, data, predecs, children,img_nr,-1,functions))
return loss__
else:
loss__.append(loss(class_,squared_hinge_loss,features,coords,scaler,w, data, predecs, children,img_nr, -1))
elif mode == 'loss_test' or mode == 'loss_eval':
print mode, loss__
if alphas[0] == 0: #levels
loss__.append(tree_level_loss(class_,features,coords,scaler, w, data, predecs, children,img_nr,-1,functions))
cpl = max(0, np.dot(w,np.array(norm_x[0]).T))
full_image.append([pruned_y[0],cpl])
return loss__,full_image
else:
loss__.append(loss(class_,squared_hinge_loss,features,coords,scaler,w, data, predecs, children,img_nr, -1))
cpl = max(0, np.dot(w,np.array(norm_x[0]).T))
full_image.append([pruned_y[0],cpl])
return loss__,full_image
elif mode == 'loss_scikit_test' or mode == 'loss_scikit_train':
loss__.append(((clf.predict(norm_x) - pruned_y)**2).sum())
return loss__
elif mode == 'levels_train' or mode == 'levels_test':
preds = []
for i,x_ in enumerate(norm_x):
preds.append(np.dot(w, x_))
cpls = []
truelvls = []
used_boxes_ = []
total_size = surface_area(pruned_boxes, levels[0])
fully_covered_score = 0.0
fully_covered_score_lvls = 0.0
covered_levels = []
print mode, len(levels)
for level in levels:
function = functions[img_nr][level]
cpl,used_boxes,_ = count_per_level([],class_,features,coords,scaler,w, preds, img_nr, pruned_boxes,levels[level], '',function)
# clipp negative predictions
cpl = max(0,cpl)
if used_boxes != []:
used_boxes_.append(used_boxes[0][1])
tru = y_p[0]
cpls.append(cpl)
sa = surface_area(pruned_boxes, levels[level])
sa_co = sa/total_size
if sa_co == 1.0:
fully_covered_score += cpl
fully_covered_score_lvls += 1
covered_levels.append(cpl)
truelvls.append(tru)
return cpls,truelvls
PATH = Config.PATH
device = Config.device
predictions = []
train_images_dir = PATH + 'train_images/{}.jpg'
train = pd.read_csv(PATH + 'train.csv')
df_train, df_dev = train_test_split(train, test_size=0.2, random_state=118)
model = CentResnet().to(device)
# model.load_state_dict(torch.load(Config.save_path))
model.eval()
if __name__ == '__main__':
import numpy as np
reg_cout = 0
pre_cout = 0
for idx in range(len(df_dev)):
data = df_dev.iloc[idx]
imageid, prestr = data["ImageId"], data["PredictionString"]
path = PATH + 'train_images/{}.jpg'.format(imageid)
img0 = imread(path, True, True)
img = preprocess_image(img0)
img = np.rollaxis(img, 2, 0)
reg_cout += len(prestr.split()) // 7
img = img[np.newaxis, :]
img = torch.tensor(img).to(device)
with torch.no_grad():
out = model(img)
coords = extract_coords(out[0].cpu().numpy(), threshold=0)
pre_cout += len(coords)
print("reg cout:{} pre count:{}".format(reg_cout, pre_cout))
def minibatch_(all_train_imgs,all_test_imgs,clf,scaler,w, loss__,mse,hinge1,hinge2,full_image,alphas,learning_rate,test_imgs, train_imgs,minibatch,subsamples,sum_x,n_samples,sum_sq_x,mean,variance, mode,mous):
if mode == 'loss_test' or mode == 'loss_scikit_test' or mode == 'levels_test':
X_p, y_p, inv = get_data(class_, test_imgs, train_imgs, minibatch, minibatch + 1, 'test', c, subsamples)
else:
X_p, y_p, inv = get_data(class_, test_imgs, train_imgs, minibatch, minibatch + 1, 'training', c, subsamples)
if X_p != []:
boxes = []
ground_truth = inv[0][2]
img_nr = inv[0][0]
if less_features:
X_p = [fts[0:features_used] for fts in X_p]
if os.path.isfile('/home/stahl/Coords_prop_windows/'+ (format(img_nr, "06d")) +'.txt'):
f = open('/home/stahl/Coords_prop_windows/'+ (format(img_nr, "06d")) +'.txt', 'r')
else:
print 'warning'
for line, y in zip(f, inv):
tmp = line.split(',')
coord = []
for s in tmp:
coord.append(float(s))
boxes.append([coord,y[2]])
assert(len(boxes)<1500)
boxes, y_p, X_p = sort_boxes(boxes, y_p, X_p, 0,1500)
gr = []
if os.path.isfile('/home/stahl/GroundTruth/%s/%s.txt'%(class_,format(img_nr, "06d"))):
gr = open('/home/stahl/GroundTruth/%s/%s.txt'%(class_,format(img_nr, "06d")), 'r')
ground_truths = []
if gr != []: #if no class image -> no ground truth. (I think this is only needed for learn _ntersection)
for line in gr:
tmp = line.split(',')
ground_truth = []
for s in tmp:
ground_truth.append(int(s))
ground_truths.append(ground_truth)
if mode == 'mean_variance':
scaler.partial_fit(X_p) # Don't cheat - fit only on training data
return scaler
# create_tree
G, levels = create_tree(boxes)
#prune tree to only have levels which fully cover the image
# tested
if prune_fully_covered:
nr_levels_covered = 100
total_size = surface_area(boxes, levels[0])
for level in levels:
sa = surface_area(boxes, levels[level])
sa_co = sa/total_size
if sa_co != 1.0:
G.remove_nodes_from(levels[level])
else:
nr_levels_covered = level
levels = {k: levels[k] for k in range(0,nr_levels_covered + 1)}
#either subsampling or prune_fully_covered
#assert(subsampling != prune_fully_covered)
# prune levels, speedup + performance
levels = {k:v for k,v in levels.iteritems() if k<prune_tree_levels}
#prune G in order to remove nodes of the lower levels
remaining_nodes = []
for lev in levels.values():
remaining_nodes.extend(lev)
for g_node in G.nodes():
if g_node not in remaining_nodes:
G.remove_node(g_node)
coords = []
features = []
f_c = []
f = []
if learn_intersections and not prune_fully_covered:
if os.path.isfile('/home/stahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples)):
f_c = open('/home/stahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples), 'r+')
else:
if mode == 'extract_train' or mode == 'extract_test':
print 'coords for %s with %s samples have to be extracted'%(img_nr,subsamples)
f_c = open('/home/stahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples), 'w')
for level in levels:
levl_boxes = extract_coords(levels[level], boxes)
if levl_boxes != []:
for lvl_box in levl_boxes:
if lvl_box not in coords:
coords.append(lvl_box)
f_c.write('%s,%s,%s,%s'%(lvl_box[0],lvl_box[1],lvl_box[2],lvl_box[3]))
f_c.write('\n')
f_c.close()
print 'features for %s with %s samples have to be extracted'%(img_nr,subsamples)
os.system('export PATH=$PATH:/home/koelma/impala/lib/x86_64-linux-gcc')
os.system('export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/koelma/impala/third.13.03/x86_64-linux/lib')
#print "EuVisual /home/stahl/Images/%s.jpg /home/stahl/Features_prop_windows/Features_upper/sheep_%s_%s.txt --eudata /home/koelma/EuDataBig --imageroifile /home/stahl/Features_prop_windows/upper_levels/sheep_%s_%s.txt"%((format(img_nr, "06d")),format(img_nr, "06d"),subsamples,format(img_nr, "06d"),subsamples)
os.system("EuVisual /home/stahl/Images/%s.jpg /home/stahl/Features_prop_windows/Features_upper/%s_%s_%s.txt --eudata /home/koelma/EuDataBig --imageroifile /home/stahl/Features_prop_windows/upper_levels/%s_%s_%s.txt"%(class_,(format(img_nr, "06d")),format(img_nr, "06d"),subsamples,class_,format(img_nr, "06d"),subsamples))
if os.path.isfile('/home/stahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples)):
f_c = open('/home/stahl/Features_prop_windows/upper_levels/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples), 'r')
else:
f_c = []
coords = []
if os.path.isfile('/home/stahl/Features_prop_windows/Features_upper/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples)):
f = open('/home/stahl/Features_prop_windows/Features_upper/%s_%s_%s.txt'%(class_,format(img_nr, "06d"),subsamples), 'r')
elif prune_fully_covered:
if os.path.isfile('/home/stahl/Features_prop_windows/upper_levels/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d"))):
f_c = open('/home/stahl/Features_prop_windows/upper_levels/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d")), 'r+')
else:
if mode == 'extract_train' or mode == 'extract_test':
print 'coords for %s with fully_cover_tree samples have to be extracted'%(img_nr)
f_c = open('/home/stahl/Features_prop_windows/upper_levels/%s_%s_fully_cover_tree.txt'%(class_,format(img_nr, "06d")), 'w')
for level in levels:
levl_boxes = extract_coords(levels[level], boxes)
if levl_boxes != []:
for lvl_box in levl_boxes:
if lvl_box not in coords:
coords.append(lvl_box)
f_c.write('%s,%s,%s,%s'%(lvl_box[0],lvl_box[1],lvl_box[2],lvl_box[3]))
f_c.write('\n')
f_c.close()
print 'features for %s with fully_cover_tree samples have to be extracted'%(img_nr)
os.system('export PATH=$PATH:/home/koelma/impala/lib/x86_64-linux-gcc')
os.system('export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/koelma/impala/third.13.03/x86_64-linux/lib')
#print "EuVisual /home/stahl/Images/%s.jpg /home/stahl/Features_prop_windows/Features_upper/sheep_%s_%s.txt --eudata /home/koelma/EuDataBig --imageroifile /home/stahl/Features_prop_windows/upper_levels/sheep_%s_%s.txt"%((format(img_nr, "06d")),format(img_nr, "06d"),subsamples,format(img_nr, "06d"),subsamples)
os.system("EuVisual /home/stahl/Images/%s.jpg /home/stahl/Features_prop_windows/Features_upper/%s_%s_fully_cover_tree.txt --eudata /home/koelma/EuDataBig --imageroifile /home/stahl/Features_prop_windows/upper_levels/%s_%s_fully_cover_tree.txt"%(class_,(format(img_nr, "06d")),format(img_nr, "06d"),class_,format(img_nr, "06d")))
if os.path.isfile('/home/stahl/Features_prop_windows/upper_levels/%s_%s_fully_cover_tree.txt'%(class_,format(img_nr, "06d"))):
f_c = open('/home/stahl/Features_prop_windows/upper_levels/%s_%s_fully_cover_tree.txt'%(class_,format(img_nr, "06d")), 'r')
else:
f_c = []
coords = []
if os.path.isfile('/home/stahl/Features_prop_windows/Features_upper/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d"))):
f = open('/home/stahl/Features_prop_windows/Features_upper/sheep_%s_fully_cover_tree.txt'%(format(img_nr, "06d")), 'r')
# else: #we don't need to load intersections
# if os.path.isfile('/home/stahl/Features_prop_windows/Features_upper/%s%s.txt'%(class_,format(img_nr, "06d"))):
# f = open('/home/stahl/Features_prop_windows/Features_upper/%s%s.txt'%(class_,format(img_nr, "06d")), 'r')
# if os.path.isfile('/home/stahl/Features_prop_windows/upper_levels/%s%s.txt'%(class_,format(img_nr, "06d"))):
# f_c = open('/home/stahl/Features_prop_windows/upper_levels/%s%s.txt'%(class_,format(img_nr, "06d")), 'r+')
# else:
# print '/home/stahl/Features_prop_windows/upper_levels/%s%s.txt does not exist'%(class_,format(img_nr, "06d"))
for i,line in enumerate(f_c):
str_ = line.rstrip('\n').split(',')
cc = []
for s in str_:
cc.append(float(s))
coords.append(cc)
for i,line in enumerate(f):
str_ = line.rstrip('\n').split(',')
ff = []
for s in str_:
ff.append(float(s))
features.append(ff)
if less_features:
features = [fts[0:features_used] for fts in features]
if normalize and features != []:
features = scaler.transform(features)
print len(y_p), len(X_p)
print len(features), len(coords)
assert len(coords) == len(features)
# append x,y of intersections
#if learn_intersections:
# for inters,coord in zip(features,coords):
# if inters not in pruned_x:
# X_p.append(inters)
# ol = 0.0
# ol = get_intersection_count(coord, ground_truths)
# y_p.append(ol)
print len(y_p), len(X_p)
#normalize
norm_x = []
if normalize:
# for p_x in pruned_x:
# norm_x.append((p_x-mean)/variance)
norm_x = scaler.transform(X_p)
else:
norm_x = X_p
data = (G, levels, y_p, norm_x, boxes, ground_truths, alphas)
sucs = nx.dfs_successors(G)
predecs = nx.dfs_predecessors(G)
#preprocess: node - children
children = {}
last = -1
for node,children_ in zip(sucs.keys(),sucs.values()):
if node != last+1:
for i in range(last+1,node):
children[i] = []
children[node] = children_
elif node == last +1:
children[node] = children_
last = node
if mode == 'train':
if alphas[2] == 0 and alphas[3] == 0: #just learn proposals and intersections
# only use proposals and intersections used in pruned tree
used_ind = get_used_proposals(G, boxes, coords, levels)
used_x = []
used_y = []
for ind in used_ind['prop']:
used_x.append(norm_x[ind])
used_y.append(y_p[ind])
for ind in used_ind['inters']:
used_x.append(features[ind])
used_y.append(get_intersection_count(coords[ind], ground_truths))
print len(used_x),len(used_y)
for x_i,y_i in zip(used_x,used_y):
w = learn_root(w,x_i,y_i,learning_rate,alphas)
else:
nodes = list(G.nodes())
for node in nodes:
if node == 0:
if alphas[0] != 0:
w = learn_root(w,norm_x[0],y_p[0],learning_rate,alphas)
else:
print 'learn nothing'
else:
w = constrained_regression(class_,features,coords,scaler,w,norm_x,y_p,node,predecs,children,boxes,learning_rate,alphas,img_nr, squared_hinge_loss)
return w, len(y_p), len(G.nodes())
elif mode == 'scikit_train':
print norm_x,y_p
clf.partial_fit(norm_x,y_p)
return clf
elif mode == 'loss_train' or mode == 'loss_test':
loss__.append(loss(class_,squared_hinge_loss,features,coords,scaler, w, data, predecs, children,img_nr,-1))
# mse.append(((data[2] - np.dot(w,np.array(data[3]).T)) ** 2).sum())
# a2 = alphas[2]
# data = (G, levels, y_p, norm_x, boxes, ground_truths, [0,0,a2,0])
# hinge1.append(loss(class_,squared_hinge_loss,features,coords,scaler, w, data, predecs, children,img_nr,-1))
# a3 = alphas[3]
# data = (G, levels, y_p, norm_x, boxes, ground_truths, [0,0,0,a3])
# hinge2.append(loss(class_,squared_hinge_loss,features,coords,scaler, w, data, predecs, children,img_nr,-1))
full_image.append([y_p[0],max(0,np.dot(w,np.array(norm_x[0]).T))])
return loss__, full_image
elif mode == 'loss_scikit_test' or mode == 'loss_scikit_train':
loss__.append(((clf.predict(norm_x) - y_p)**2).sum())
return loss__
elif mode == 'levels_train' or mode == 'levels_test':
#im = mpimg.imread('/home/stahl/Images/'+ (format(img_nr, "06d")) +'.jpg')
preds = []
for i,x_ in enumerate(norm_x):
preds.append(np.dot(w, x_))
cpls = []
truelvls = []
used_boxes_ = []
total_size = surface_area(boxes, levels[0])
fully_covered_score = 0.0
fully_covered_score_lvls = 0.0
covered_levels = []
for level in levels:
#tru and truelvls was in order to check if count_per_level method is correct
cpl,used_boxes,_ = count_per_level(class_,features,coords,scaler,w, preds, img_nr, boxes,levels[level], '',[])
cpl = max(0,cpl)
if used_boxes != []:
used_boxes_.append(used_boxes[0][1])
tru,_,_ = count_per_level(class_,features,coords,scaler,w, preds, img_nr, boxes,levels[level], 'gt',[])
cpls.append(cpl)
sa = surface_area(boxes, levels[level])
sa_co = sa/total_size
if sa_co == 1.0:
fully_covered_score += cpl
fully_covered_score_lvls += 1
covered_levels.append(cpl)
truelvls.append(tru)
return cpls, truelvls, used_boxes_,boxes,preds,fully_covered_score/fully_covered_score_lvls,covered_levels