dev_misc: code to extract microsoft/FaceSynthetics dataset

mainscripts/dev_misc.py

@@ -13,7 +13,6 @@ from core.joblib import Subprocessor
 from core.leras import nn
 from DFLIMG import *
 from facelib import FaceType, LandmarksProcessor
-
 from . import Extractor, Sorter
 from .Extractor import ExtractSubprocessor
 
@@ -359,7 +358,7 @@ def extract_umd_csv(input_file_csv,
 
 
     
-def dev_test(input_dir):
+def dev_test1(input_dir):
     # LaPa dataset
     
     image_size = 1024
@@ -500,3 +499,96 @@ def dev_segmented_trash(input_dir):
         except:
             io.log_info ('fail to trashing %s' % (src.name) )
 
+
+
+def dev_test(input_dir):
+    """
+    extract FaceSynthetics dataset https://github.com/microsoft/FaceSynthetics
+    
+    BACKGROUND = 0
+    SKIN = 1
+    NOSE = 2
+    RIGHT_EYE = 3
+    LEFT_EYE = 4
+    RIGHT_BROW = 5
+    LEFT_BROW = 6
+    RIGHT_EAR = 7
+    LEFT_EAR = 8
+    MOUTH_INTERIOR = 9
+    TOP_LIP = 10
+    BOTTOM_LIP = 11
+    NECK = 12
+    HAIR = 13
+    BEARD = 14
+    CLOTHING = 15
+    GLASSES = 16
+    HEADWEAR = 17
+    FACEWEAR = 18
+    IGNORE = 255
+    """
+    
+    
+    image_size = 1024
+    face_type = FaceType.WHOLE_FACE
+    
+    input_path = Path(input_dir)
+    
+    
+    
+    output_path = input_path.parent / f'{input_path.name}_out'    
+    if output_path.exists():
+        output_images_paths = pathex.get_image_paths(output_path)
+        if len(output_images_paths) != 0:
+            io.input(f"\n WARNING !!! \n {output_path} contains files! \n They will be deleted. \n Press enter to continue.\n")
+            for filename in output_images_paths:
+                Path(filename).unlink()
+    output_path.mkdir(parents=True, exist_ok=True)
+    
+    data = []
+    
+    for filepath in io.progress_bar_generator(pathex.get_paths(input_path), "Processing"):
+        if filepath.suffix == '.txt':
+            
+            image_filepath = filepath.parent / f'{filepath.name.split("_")[0]}.png'
+            if not image_filepath.exists():
+                print(f'{image_filepath} does not exist, skipping') 
+                
+            lmrks = []
+            for lmrk_line in filepath.read_text().split('\n'):
+                if len(lmrk_line) == 0:
+                    continue
+                    
+                x, y = lmrk_line.split(' ')
+                x, y = float(x), float(y)
+                
+                lmrks.append( (x,y) )
+                
+            lmrks = np.array(lmrks[:68], np.float32)
+            rect = LandmarksProcessor.get_rect_from_landmarks(lmrks)
+            data += [ ExtractSubprocessor.Data(filepath=image_filepath, rects=[rect], landmarks=[ lmrks ] ) ]
+
+    if len(data) > 0:
+        io.log_info ("Performing 3rd pass...")
+        data = ExtractSubprocessor (data, 'final', image_size, 95, face_type, final_output_path=output_path, device_config=nn.DeviceConfig.CPU()).run()
+
+        for filename in io.progress_bar_generator(pathex.get_image_paths (output_path), "Processing"):
+            filepath = Path(filename)
+            
+            dflimg = DFLJPG.load(filepath)
+            
+            src_filename = dflimg.get_source_filename()
+            image_to_face_mat = dflimg.get_image_to_face_mat()
+            
+            seg_filepath = input_path / ( Path(src_filename).stem + '_seg.png')        
+            if not seg_filepath.exists():
+                raise ValueError(f'{seg_filepath} does not exist')
+            
+            seg = cv2_imread(seg_filepath)     
+            seg_inds = np.isin(seg, [1,2,3,4,5,6,9,10,11]) 
+            seg[~seg_inds] = 0
+            seg[seg_inds] = 1
+            seg = seg.astype(np.float32)
+            seg = cv2.warpAffine(seg, image_to_face_mat, (image_size, image_size), cv2.INTER_LANCZOS4)
+            dflimg.set_xseg_mask(seg)
+            dflimg.save()
+