def main_2(expt_name, fn1, fn2, feature='sift', **template_information):
import os
kw = {'fn1':fn1, 'fn2':fn2, 'feature':feature, 'template_information':template_information}
imgQ, imgT, detector, matcher, temp_inf = expt_setting(**kw)
print('using', feature)
temp_inf = slac.TmpInf(**template_information)
a = temp_inf.tmp_img
try:
with Timer('Lording pickle'):
splt_k, splt_d = slac.affine_load_into_mesh(os.path.splitext(temp_inf.tmp_img)[0],
temp_inf.get_splitnum())
except ValueError as e:
print(e)
print('If you need to save {} to file as datavase. ¥n'
+ ' Execute /Users/tiwasaki/PycharmProjects/makedb/make_split_combine_featureDB_from_templates.py')
with Timer('Detection and dividing'):
splt_k, splt_d = slac.affine_detect_into_mesh(detector, temp_inf.get_splitnum(),
imgQ, simu_param='default')
img = imgQ.copy()
def plot_kps(img, keypoints):
if not len(keypoints) == 0:
out_img = cv2.drawKeypoints(img, keypoints.pop(0), None, color=(0,255,0))
return plot_kps(out_img, keypoints)
return img
out_img = plot_kps(img, splt_k)
output_dir = slac.myfm.setup_output_directory(expt_name, "plots")
cv2.imwrite(os.path.join(output_dir, 'analyse_'+temp_inf.tmp_img+'.png'), out_img)
def expt_setting(**kwargs):
import sys
imgQ = cv2.imread(kwargs['fn1'], 0)
imgT = cv2.imread(kwargs['fn2'], 0)
detector, matcher = init_feature(kwargs['feature'])
if imgQ is None:
print('Failed to load fn1:', kwargs['fn1'])
sys.exit(1)
if imgT is None:
print('Failed to load fn2:', kwargs['fn2'])
sys.exit(1)
if detector is None:
print('unknown feature:', kwargs['feature'])
sys.exit(1)
temp_inf = slac.TmpInf(**kwargs['template_information'])
return imgQ, imgT, detector, matcher, temp_inf
print("FEATURE: {}".format(feature_name))
scols = 8
srows = 8
h, w = imgQ.shape[:2]
template_fn, ext = os.path.splitext(os.path.basename(fn1_full))
template_information = {
"_fn": "tmp.png",
"template_img": template_fn,
"_cols": w,
"_rows": h,
"_scols": scols,
"_srows": srows,
"_nneighbor": 4
}
temp_inf = splta.TmpInf(**template_information)
try:
with splta.Timer('Lording pickle'):
splt_kpQ, splt_descQ = splta.affine_load_into_mesh(
template_fn, temp_inf.get_splitnum())
except ValueError as e:
print(e.args)
print('If you need to save {} to file as datavase. ¥n' +
' Execute makedb/make_split_combine_featureDB_from_templates.py')
with splta.Timer('Detection and dividing'):
splt_kpQ, splt_descQ = splta.affine_detect_into_mesh(
detector, temp_inf.get_splitnum(), imgQ, simu_param='default')
m_skQ, m_sdQ, m_k_num, merged_map = splta.combine_mesh_compact(
splt_kpQ, splt_descQ, temp_inf)
sys.exit(1)
template_full_fn = myfsys.get_template_file_full_path_(template_fn)
try:
imgQ = get_img(template_full_fn)
except ValueError as e:
print(e)
print('テンプレートがないだけなので続ける')
continue
template_information = {
"_fn": "tmp.png",
"template_img": template_fn,
"_cols": 800,
"_rows": 600,
"_scols": 8,
"_srows": 8,
"_nneighbor": 4
}
temp_inf = slac.TmpInf(**template_information)
pool = ThreadPool(processes=cv2.getNumberOfCPUs())
with Timer('calculate Keypoints Descriptors'):
kp, des = asift.affine_detect(detector, imgQ, pool=pool)
##キーポイントを出力する
index = []
for p in kp:
temp = (p.pt, p.size, p.angle, p.response, p.octave, p.class_id)
index.append(temp)
pickle_path = myfsys.get_pikle_path(fn)
with open(pickle_path, mode='wb') as f:
pickle.dump((index, des), f)
def main_1(expt_name, fn1, fn2, feature='sift', **template_information):
kw = {'fn1':fn1, 'fn2':fn2, 'feature':feature, 'template_information':template_information}
imgQ, imgT, detector, matcher, temp_inf = expt_setting(**kw)
print('using', feature)
temp_inf = slac.TmpInf(**template_information)
try:
with Timer('Lording pickle'):
splt_k, splt_d = slac.affine_load_into_mesh(os.path.splitext(temp_inf.tmp_img)[0],
temp_inf.get_splitnum())
except ValueError as e:
print(e)
print('If you need to save {} to file as datavase. ¥n'
+ ' Execute /Users/tiwasaki/PycharmProjects/makedb/make_split_combine_featureDB_from_templates.py')
with Timer('Detection and dividing'):
splt_k, splt_d = slac.affine_detect_into_mesh(detector, temp_inf.get_splitnum(),
imgQ, simu_param='default')
mesh_k_num = np.array([len(keypoints) for keypoints in splt_k]).reshape(temp_inf.get_mesh_shape())
# mean, median, max, min, peak2peak, standard_deviation, variance = analysis_num(mesh_k_num)
print("plot mesh keypoint heatmap")
al_vals = slac.analysis_num(mesh_k_num)
print("平均, 中央値, 最大値, 最小値, 値の範囲, 標準偏差, 分散")
print("{0:4f}, {1:4f}, {2:4d}, {3:4d}, {4:4d}, {5:4f}, {6:4f}".format(*al_vals))
output_dir = slac.myfm.setup_output_directory(expt_name, "plots")
plt.figure(figsize=(16, 12))
sns.set("paper", "whitegrid", "dark", font_scale=3)
h = sns.heatmap(mesh_k_num, annot=True, fmt='d', cmap='Blues')
h.set(xlabel="x")
h.set(ylabel="y")
h.set(title="Heatmap of keypoint amounts -" + temp_inf.tmp_img)
h_fig = h.get_figure()
pp = PdfPages(os.path.join(output_dir, 'analyse_'+temp_inf.tmp_img+'.pdf'))
h_fig.savefig(pp, format='pdf')
df = slac.analysis_kp(splt_k, temp_inf)
# with Timer('plotting Kernel De'):
# for i in range(temp_inf.get_splitnum()):
# ax = sns.kdeplot(df.query('mesh_id == ' + str(i))['x'], df.query('mesh_id == ' + str(i))['y'], shade=True)
# ax.set(ylim=(600, 0))
# ax.set(xlim=(0, 800))
# ax.set(xlabel="x")
# ax.set(ylabel="y")
# ax.set(title="Kernel density estimation")
plt.figure(figsize=(16, 12))
sns.set("paper", "whitegrid", "dark", font_scale=3)
g = sns.kdeplot(df['x'], df['y'], shade=True, shade_lowest=False)
g.set(ylim=(600, 0))
g.set(xlim=(0, 800))
g.set(xlabel="Width of image")
g.set(ylabel="Height of image")
g.set(title="Kernel density estimation-"+temp_inf.tmp_img)
g_fig = g.get_figure()
g_fig.savefig(pp, format='pdf')
# logger.info('show mesh map')
# plt.figure()
# sns.set("paper", "whitegrid", "dark", font_scale=1.5)
# mesh_map = temp_inf.get_mesh_map()
# mmap_ax = sns.heatmap(mesh_map, annot=True, fmt="d")
# mmap_ax.set(xlabel="x")
# mmap_ax.set(ylabel="y")
# mmap_ax.set(title="Mesh map -" + temp_inf.tmp_img)
# mmap_ax_fig = h.get_figure()
# mmap_ax_fig.savefig(pp, format='pdf')
with Timer('merging'):
msplt_k, msplt_d, mmesh_k_num, mmesh_map = slac.combine_mesh(splt_k, splt_d, temp_inf)
logger.info('show merged mesh map')
plt.figure(figsize=(16, 12))
sns.set("paper", "whitegrid", "dark", font_scale=3)
merged_map_ax = sns.heatmap(mmesh_map, annot=True, fmt="d")
merged_map_ax.set(xlabel="x")
merged_map_ax.set(ylabel="y")
merged_map_ax.set(title="Merged mesh map -" + temp_inf.tmp_img)
merged_map_ax_fig = merged_map_ax.get_figure()
merged_map_ax_fig.savefig(pp, format='pdf')
plt.figure(figsize=(16, 12))
sns.set("paper", "whitegrid", "dark", font_scale=3)
mh = sns.heatmap(mmesh_k_num, annot=True, fmt='d', cmap='Blues')
mh.set(xlabel="x")
mh.set(ylabel="y")
mh.set(title="Heatmap of merged keypoint amounts -" + temp_inf.tmp_img)
mh_fig = mh.get_figure()
mh_fig.savefig(pp, format='pdf')
# pp.savefig()
pp.close()