def pick(opt):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
opt.debug = max(opt.debug, 1)
Detector = detector_factory[opt.task]
detector = Detector(opt)
if os.path.isdir(opt.data):
image_names = []
ls = os.listdir(opt.data)
for file_name in sorted(ls):
ext = file_name[file_name.rfind('.') + 1:].lower()
if ext in image_ext:
image_names.append(os.path.join(opt.data, file_name))
else:
image_names = [opt.data]
#visual_path = os.path.join(opt.demo, 'visual_1024/')
#if not os.path.exists(visual_path):
# os.makedirs(visual_path)
if opt.data_type == 'mrc':
mrc_thi = []
for (image_name) in image_names:
with open(image_name, "rb") as f:
content = f.read()
data, header, _ = parse(content=content)
print('downsampling', image_name, '...')
print(int(data.shape[0] / data.shape[1] * 1024))
data = downsample_with_size(
data, 1024, int(data.shape[0] / data.shape[1] * 1024))
data = quantize(data)
data = cv2.equalizeHist(data)
data = cv2.merge([data, data, data])
name = image_name.split('/')[-1].replace('.mrc', '')
thi_name = image_name.split('/')[-1].replace('.mrc', '.thi')
mrc_thi.append((image_name[-1], thi_name))
ret = detector.run(data, header, name)
time_str = ''
for stat in time_stats:
time_str = time_str + '{} {:.3f}s |'.format(stat, ret[stat])
print(time_str)
with open('merge_results.thi', "w") as f:
f.write(
'[Micrograph Particle coordinate:\n #0:MICROGRAPH_PATH STRING\n #1:PARTICLE_PATH STRING]\n'
)
for item in mrc_thi:
f.write('@%s\t@%s\n' % (item[0], item[1]))
elif opt.data_type == 'png':
for (image_name) in image_names:
ret = detector.run(image_name, None, image_name)
time_str = ''
for stat in time_stats:
time_str = time_str + '{} {:.3f}s |'.format(stat, ret[stat])
print(time_str)
def load_and_downsample(path, target_size):
with open(path, "rb") as f:
content = f.read()
data, header, _ = mrc.parse(content=content)
name = path.split('/')[-1].split('.')[0]
#averge frame
if header[2] > 1:
avg_mrc = np.zeros_like(data, data[0, ...])
for j in range(header[2]):
avg_mrc += data[j, ...]
avg_mrc /= header[2]
data = avg_mrc
data = mrc.downsample_with_size(data, target_size,
int(data.shape[1] / data.shape[0] * 1024))
return mrc.MrcData(name, data, header)
def downsample(inputs, use_factor=False, para1=None, para2=None):
#This method executes a downsampling on the mrc
#Inputs is a list of MrcData
#If use_factor is True, para1 represents factor and para2 represents shape
#Else para1 and para2 represents the target size(y, x)
for i in range(len(inputs)):
if use_factor:
print("Processing %s ..." % (inputs[i].name))
inputs[i].data = mrc.downsample_with_factor(inputs[i].data,
factor=para1,
shape=para2)
else:
print("Prcocessing %s ..." % (inputs[i].name))
inputs[i].data = mrc.downsample_with_size(inputs[i].data,
size1=para1,
size2=para2)
return inputs