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added dev_segmented_extract,

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extracts marked images in 'labelme' tool, that can be used in FANseg training
This commit is contained in:
Colombo 2020-03-08 10:13:15 +04:00
parent 143792fd31
commit 6f4ea69d4d
2 changed files with 69 additions and 51 deletions
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11
main.py
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@ -105,6 +105,17 @@ if __name__ == "__main__":
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
p.set_defaults (func=process_dev_test)
def process_dev_segmented_extract(arguments):
osex.set_process_lowest_prio()
from mainscripts import dev_misc
dev_misc.dev_segmented_extract(arguments.input_dir, arguments.output_dir)
p = subparsers.add_parser( "dev_segmented_extract", help="")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
p.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir")
p.set_defaults (func=process_dev_segmented_extract)
def process_sort(arguments):
osex.set_process_lowest_prio()
from mainscripts import Sorter

97
mainscripts/dev_misc.py
View file

@ -553,22 +553,19 @@ def dev_test1(input_dir):
#import code
#code.interact(local=dict(globals(), **locals()))
#unused in end user workflow
def dev_test(input_dir ):
def dev_segmented_extract(input_dir, output_dir ):
# extract and merge .json labelme files within the faces
device_config = nn.DeviceConfig.GPUIndexes( nn.ask_choose_device_idxs(suggest_all_gpu=True) )
input_path = Path(input_dir)
if not input_path.exists():
raise ValueError('input_dir not found. Please ensure it exists.')
output_path = input_path.parent / (input_path.name+'_merged')
io.log_info(f'Output dir is % {output_path}')
output_path = Path(output_dir)
io.log_info("Performing extract segmented faces.")
io.log_info(f'Output dir is {output_path}')
if output_path.exists():
output_images_paths = pathex.get_image_paths(output_path)
@ -582,27 +579,34 @@ def dev_test(input_dir ):
images_paths = pathex.get_image_paths(input_path)
extract_data = []
images_jsons = {}
images_processed = 0
for filepath in io.progress_bar_generator(images_paths, "Processing"):
filepath = Path(filepath)
json_filepath = filepath.parent / (filepath.stem+'.json')
if json_filepath.exists():
json_dict = json.loads(json_filepath.read_text())
images_jsons[filepath] = json_dict
try:
json_dict = json.loads(json_filepath.read_text())
images_jsons[filepath] = json_dict
total_points = [ [x,y] for shape in json_dict['shapes'] for x,y in shape['points'] ]
total_points = np.array(total_points)
total_points = [ [x,y] for shape in json_dict['shapes'] for x,y in shape['points'] ]
total_points = np.array(total_points)
l,r = int(total_points[:,0].min()), int(total_points[:,0].max())
t,b = int(total_points[:,1].min()), int(total_points[:,1].max())
if len(total_points) == 0:
io.log_info(f"No points found in {json_filepath}, skipping.")
continue
extract_data.append ( ExtractSubprocessor.Data(filepath, rects=[ [l,t,r,b] ] ) )
l,r = int(total_points[:,0].min()), int(total_points[:,0].max())
t,b = int(total_points[:,1].min()), int(total_points[:,1].max())
extract_data.append ( ExtractSubprocessor.Data(filepath, rects=[ [l,t,r,b] ] ) )
images_processed += 1
except:
io.log_err(f"err {filepath}")
return
image_size = 1024
face_type = FaceType.HEAD
@ -629,32 +633,35 @@ def dev_test(input_dir ):
dflimg.embed_and_set (filepath, ie_polys=ie_polys)
"""
#mark only
for data in extract_data:
filepath = data.filepath
output_filepath = output_path / (filepath.stem+'.jpg')
img = cv2_imread(filepath)
img = imagelib.normalize_channels(img, 3)
cv2_imwrite(output_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )
json_dict = images_jsons[filepath]
ie_polys = IEPolys()
for shape in json_dict['shapes']:
ie_poly = ie_polys.add(1)
for x,y in shape['points']:
ie_poly.add( int(x), int(y) )
io.log_info(f"Images found: {len(images_paths)}")
io.log_info(f"Images processed: {images_processed}")
DFLJPG.embed_data(output_filepath, face_type=FaceType.toString(FaceType.MARK_ONLY),
landmarks=data.landmarks[0],
ie_polys=ie_polys,
source_filename=filepath.name,
source_rect=data.rects[0],
source_landmarks=data.landmarks[0]
)
"""
io.log_info("Done.")
"""
#mark only
for data in extract_data:
filepath = data.filepath
output_filepath = output_path / (filepath.stem+'.jpg')
img = cv2_imread(filepath)
img = imagelib.normalize_channels(img, 3)
cv2_imwrite(output_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )
json_dict = images_jsons[filepath]
ie_polys = IEPolys()
for shape in json_dict['shapes']:
ie_poly = ie_polys.add(1)
for x,y in shape['points']:
ie_poly.add( int(x), int(y) )
DFLJPG.embed_data(output_filepath, face_type=FaceType.toString(FaceType.MARK_ONLY),
landmarks=data.landmarks[0],
ie_polys=ie_polys,
source_filename=filepath.name,
source_rect=data.rects[0],
source_landmarks=data.landmarks[0]
)
"""