def test_raw_object_regression(self):
raw_8bit = convert_to_8bit(self.raw_image)
features = quick_features(raw_8bit)
with open("expected/case_1_blob_qf.pickle", "rb") as f:
expected_features = pickle.load(f)
#pickle.dump(features, open("expected/case_1_blob_qf.pickle", "wb"))
expect_rep = sorted(expected_features)
features_rep = sorted(features)
self.assertEqual(str(expect_rep), str(features_rep))
def test_simple_object(self):
features = quick_features(self.red_blob)
blob_mask = np.uint8(np.mean(self.red_blob, 2)) > 0
diff = blob_mask != (features['binary'] > 0)
self.assertLess(np.count_nonzero(diff), 4)
# uses edges so it is slightly different
self.assertEqual(14, features['area'])
self.assertAlmostEqual(6.876, features['major_axis_length'], 2)
self.assertAlmostEqual(2.781, features['minor_axis_length'], 2)
self.assertAlmostEqual(0.068, features['orientation'], 2)
self.assertEqual(0.01, features['clipped_fraction'])
self.assertEqual(0.875, features['solidity']) # is it spread a bit
def test_intensity_object(self):
red_blue_blob = self.red_blob.copy()
red_blue_blob[5:8, 2:7, 2] = 20
features = quick_features(red_blue_blob)
red_intensity = features['intensity_red']
self.assertAlmostEqual(129, red_intensity['median_intensity'], 1)
self.assertAlmostEqual(79, red_intensity['mean_intensity'],
0) # it includes some borders
self.assertGreater(70, red_intensity['std_intensity'], 3)
self.assertAlmostEqual(1, red_intensity['perc_25_intensity'], 3)
self.assertAlmostEqual(129, red_intensity['perc_75_intensity'], 3)
blue_intensity = features['intensity_blue']
self.assertAlmostEqual(20, blue_intensity['median_intensity'], 1)
self.assertAlmostEqual(15.7, blue_intensity['mean_intensity'],
0) # it includes some borders
self.assertGreater(8, blue_intensity['std_intensity'], 3)
self.assertAlmostEqual(20, blue_intensity['perc_25_intensity'], 3)
self.assertAlmostEqual(20, blue_intensity['perc_75_intensity'], 3)
gray_intensity = features['intensity_gray']
self.assertAlmostEqual(49, gray_intensity['median_intensity'], 1)
self.assertAlmostEqual(31.5, gray_intensity['mean_intensity'], 0)
self.assertAlmostEqual(22.85, gray_intensity['std_intensity'], 1)
self.assertAlmostEqual(6, gray_intensity['perc_25_intensity'], 3)
self.assertAlmostEqual(50, gray_intensity['perc_75_intensity'], 3)
# there is nothing in green only noise
green_intensity = features['intensity_green']
self.assertGreater(1, green_intensity['mean_intensity'])
self.assertGreater(1, green_intensity['std_intensity'], 3)
self.assertAlmostEqual(0, green_intensity['perc_25_intensity'], 3)
self.assertAlmostEqual(1, green_intensity['perc_75_intensity'], 3)
self.assertGreater(0.1, green_intensity['mass_displace_in_images'], 3)
self.assertGreater(0.1, green_intensity['mass_displace_in_minors'], 3)
def process_image(bundle):
image_path = bundle['image_path']
image = bundle['image']
data_path = bundle['data_path']
image_dir = bundle['image_dir']
cfg = bundle['cfg']
total_images = bundle['total_images']
filename = os.path.basename(image_path)
# Patch bug in PCAM where timestamp string is somtimes incorrectly set to
# 0 or a small value. Use the file creation time instead.
#
# This is okay so long as the queue in PCAM mostly empty. We can adapt
# the frame counter to fix this in the future.
timestamp = 0
for substr in filename.split('-'):
try:
timestamp = int(substr)
break
except ValueError:
pass
# use the file creation time if the timestamp is funky
if timestamp < 100000:
timestamp = os.path.getctime(image_path)
prefix = filename.split('.')[0]
# image is preloaded so no need to load here
#image = cvtools.import_image(data_path,filename,bayer_pattern=cv2.COLOR_BAYER_BG2RGB)
img_c_8bit = cvtools.convert_to_8bit(image)
# images will be saved out later so set save_to_disk to False
features = cvtools.quick_features(img_c_8bit,
save_to_disk=False,
abs_path=image_dir,
file_prefix=prefix)
filename = os.path.basename(image_path).split('.')[0] + '.png'
timestring = datetime.datetime.fromtimestamp(timestamp).strftime(
'%Y-%m-%d %H:%M:%S')
entry = {}
entry['maj_axis_len'] = features['major_axis_length']
entry['min_axis_len'] = features['minor_axis_length']
entry['aspect_ratio'] = features['aspect_ratio']
entry['area'] = features['area']
entry['clipped_fraction'] = features['clipped_fraction']
entry['orientation'] = features['orientation'] * 180 / math.pi
entry['timestring'] = timestring
entry['timestamp'] = timestamp
entry['width'] = img_c_8bit.shape[1]
entry['height'] = img_c_8bit.shape[0]
entry['url'] = bundle['reldir'] + '/' + filename
entry['file_size'] = os.path.getsize(image_path)
output = {}
output['entry'] = entry
output['image_path'] = image_dir
output['prefix'] = prefix
output['features'] = features
return output
def process_image(bundle):
image_path = bundle['image_path']
image = bundle['image']
data_path = bundle['data_path']
image_dir = bundle['image_dir']
cfg = bundle['cfg']
total_images = bundle['total_images']
filename = os.path.basename(image_path)
# Patch bug in PCAM where timestamp string is somtimes incorrectly set to
# 0 or a small value. Use the file creation time instead.
#
# This is okay so long as the queue in PCAM mostly empty. We can adapt
# the frame counter to fix this in the future.
timestamp = 0
for substr in filename.split('-'):
try:
timestamp = int(substr)
break
except ValueError:
pass
# Range check the timestamp
if timestamp < 100000:
print("" + filename + " strange timestamp.")
#timestamp = os.path.getctime(image_path)
output = {}
return output
prefix = filename.split('.')[0]
# image is preloaded so no need to load here
#image = cvtools.import_image(data_path,filename,bayer_pattern=cv2.COLOR_BAYER_BG2RGB)
img_c_8bit = cvtools.convert_to_8bit(image)
# images will be saved out later so set save_to_disk to False
features = cvtools.quick_features(img_c_8bit,
save_to_disk=False,
abs_path=image_dir,
file_prefix=prefix,
cfg=cfg)
use_jpeg = use_jpeg = cfg.get("UseJpeg").lower() == 'true'
if use_jpeg:
filename = os.path.basename(image_path).split('.')[0] + '.jpeg'
else:
filename = os.path.basename(image_path).split('.')[0] + '.png'
# handle new file formwat with unixtime in microseconds
if timestamp > 1498093400000000:
timestamp = timestamp / 1000000
# Range check the timestamp
if timestamp < 100000 or timestamp > time.time():
print("" + filename + " strange timestamp.")
#timestamp = os.path.getctime(image_path)
output = {}
return output
# print "Timestamp: " + str(timestamp)
timestring = datetime.datetime.fromtimestamp(timestamp).strftime(
'%Y-%m-%d %H:%M:%S')
entry = {}
entry['maj_axis_len'] = features['major_axis_length']
entry['min_axis_len'] = features['minor_axis_length']
entry['aspect_ratio'] = features['aspect_ratio']
entry['area'] = features['area']
entry['clipped_fraction'] = features['clipped_fraction']
entry['orientation'] = features['orientation'] * 180 / math.pi
entry['eccentricity'] = features['eccentricity']
entry['solidity'] = features['solidity']
entry['estimated_volume'] = features['estimated_volume']
entry['intensity_gray'] = features['intensity_gray']
entry['intensity_red'] = features['intensity_red']
entry['intensity_green'] = features['intensity_green']
entry['intensity_blue'] = features['intensity_blue']
entry['timestring'] = timestring
entry['timestamp'] = timestamp
entry['width'] = img_c_8bit.shape[1]
entry['height'] = img_c_8bit.shape[0]
entry['url'] = bundle['reldir'] + '/' + filename
entry['file_size'] = os.path.getsize(image_path)
output = {}
output['entry'] = entry
output['image_path'] = image_dir
output['prefix'] = prefix
output['features'] = features
return output