...
Once we have these images collected in a folder named “DeviceX”, we define our lighting conditions, initialize the Imatest IT library and run our processing function on this and a number of other devices:
| Code Block |
|---|
led5k = "5000KLED_1000lux" # Light Levels
tl84 = "TL84_100lux"
tung = "Tung_10lux"
light_conditions = [led5k, tl84, tung]
imatest = ImatestLibrary()
phones = ['DeviceX', # Just for example...
'DeviceY',
'DeviceZ']
for phone in phones:
calc_phone(phone)
imatest.terminate_library() |
|
The main processing script invokes the five modules, collects their data outputs and saves them to a JSON file:
| Code Block |
|---|
| def calc_phone(phone):
print('Calculating Metrics for '+phone+ ' *****************************')
uniformity_results = uniformity(phone) # Uniformity
multitest_results = multitest(phone) # Multitest
dotpattern_results = dotpattern(phone) # Dot Pattern
esfriso_results = esfriso(phone) # eSFR ISO
random_results = random(phone) # Random
# Output data to disk
file_name = os.path.join(images_dir, phone, phone + '.json')
with open(file_name, 'w') as outfile:
full_data = {'multitest' : multitest_results,
'dotpattern' : dotpattern_results,
'esfriso' : esfriso_results,
'random' : random_results,
'uniformity' : uniformity_results}
json.dump(full_data, outfile, indent=4, separators=(',',': '))
return full_data |
|
We call the Imatest eSFR ISO module for each specified lighting condition:
| Code Block |
|---|
| # eSFR ISO (ISO 12233:2014) is used to test Visual Noise (VN)
# and Spatial Frequency Response (SFR)
def esfriso(base):
global images_dir, ini_file, light_conditions, op_mode
output = {}
for light_source in light_conditions:
print('Testing ' + base + ' eSFR ISO at ' + light_source)
input_file = os.path.join(images_dir, base, base+'_eSFR_'+light_source+'.jpg')
if os.path.exists(input_file) != True:
raise Exception('File ' + input_file + 'not found!')
print('opening ' + input_file)
result = imatest.esfriso_json(input_file=input_file, root_dir=root_dir,
op_mode=op_mode, ini_file=ini_file)
data = json.loads(result)
output[light_source] = data['esfrisoResults'];
return output |
|
On the fly INI file changes
...
In this case, we are we replacing the [sg] roi, nwid_save, and nht_save keys with the values stored in this multicharts-rois.json file:
| Code Block |
|---|
|
{
"DeviceX_SG_5000KLED_1000lux.JPG": "468 417 2122 1578 468 1588 2102 397",
"DeviceX_SG_TL84_100lux.JPG":"482 376 2142 1571 482 1561 2157 376",
"DeviceX_SG_Tung_10lux.JPG":"386 336 2016 1492 401 1497 2021 321",
"width":"2592",
"height":"1936"
} |
We use this script to modify INI files to dynamically insert different settings:
| Code Block |
|---|
| import ConfigParser
def get_config(path): # Used make ConfigParser case sensitive
config = ConfigParser.ConfigParser()
config.optionxform=str # make configparser preserve case
try:
config.read(os.path.expanduser(path))
return config
except Exception, e:
log.error(e)
def setROI(roi,width,height):
global ini_file
imatestini = get_config(ini_file)
imatestini.set('ccsg', 'roi', roi)
imatestini.set('ccsg', 'nwid_save', width)
imatestini.set('ccsg', 'nht_save', height)
with open(tempINI, 'w') as outfile:
imatestini.write(outfile)
return |
|
| Note |
|---|
Imatest INIs are case sensitive. Make sure any INI manipulator will preserve the case of sections and keys. |
These functions are then called using the values loaded from the JSON file inside of our multicharts function call:
| Code Block |
|---|
| roi_overrides = {}
roi_overrides_lower = {}
roi_override_file = os.path.join(images_dir,base,'multicharts-rois.json')
if os.path.exists(roi_override_file):
with open(roi_override_file,'r') as fh: # read ROI override file
roi_overrides = json.load(fh)
for override, rois in roi_overrides.iteritems(): # convert to lowercase
roi_overrides_lower[override.lower()] = rois
else:
raise Exception('ROI override file ' + roi_override_file + ' not found.')
if image_file.lower() in roi_overrides_lower:
setROI(roi_overrides_lower[image_file.lower()], roi_overrides['width'], roi_overrides['height'])
selectedINI = tempINI
else:
raise Exception('Missing ROI override for ' + image_file) |
|
Extracting Scores
Finally, we extract Key Performance Indicators (KPI), which are all in perceptual quality loss (QL) in units of just noticeable differences (JND). This script is a bit long for this post, so view the source on Github if you are interested./
Here is the resulting set of calculated metrics in JSON format:
| Code Block |
|---|
|
{ 'DeviceX':
{ 'CL_QL': { '5000KLED_1000lux': 0.9142,
'TL84_100lux': 0.1803,
'Tung_10lux': 2.565},
'CU_QL': 0.07498,
'LCD_QL': 0,
'LGD_QL': 0,
'SFR_QL': { '5000KLED_1000lux': 0.0,
'TL84_100lux': 0.1969,
'Tung_10lux': 0.3946},
'TB_QL': { '5000KLED_1000lux': 1.2,
'TL84_100lux': 6.25,
'Tung_10lux': 9.1},
'VN_QL': { '5000KLED_1000lux': 1.113,
'TL84_100lux': 2.409,
'Tung_10lux': 8.531}},
'Combined_QL': 11.0245
} |
...