added fanseg for future WF segmentation model

mainscripts/dev_misc.py

@@ -1,3 +1,4 @@
+import json
 import multiprocessing
 import shutil
 from pathlib import Path
@@ -5,12 +6,14 @@ from pathlib import Path
 import cv2
 import numpy as np
 
-from DFLIMG import *
-from facelib import FaceType, LandmarksProcessor
+from core import imagelib, pathex
+from core.cv2ex import *
+from core.imagelib import IEPolys
 from core.interact import interact as io
 from core.joblib import Subprocessor
-from core import pathex, imagelib
-from core.cv2ex import *
+from core.leras import nn
+from DFLIMG import *
+from facelib import FaceType, LandmarksProcessor
 
 from . import Extractor, Sorter
 from .Extractor import ExtractSubprocessor
@@ -393,7 +396,8 @@ def extract_fanseg(input_dir, device_args={} ):
         data = ExtractSubprocessor ([ ExtractSubprocessor.Data(filename) for filename in paths_to_extract ], 'fanseg', multi_gpu=multi_gpu, cpu_only=cpu_only).run()
 
 #unused in end user workflow
-def dev_test(input_dir ):
+def dev_test_68(input_dir ):
+    # process 68 landmarks dataset with .pts files
     input_path = Path(input_dir)
     if not input_path.exists():
         raise ValueError('input_dir not found. Please ensure it exists.')
@@ -441,7 +445,7 @@ def dev_test(input_dir ):
                     continue
 
                 rect = LandmarksProcessor.get_rect_from_landmarks(lmrks)
-    
+
                 output_filepath = output_path / (filepath.stem+'.jpg')
 
                 img = cv2_imread(filepath)
@@ -549,3 +553,108 @@ def dev_test1(input_dir):
             #import code
             #code.interact(local=dict(globals(), **locals()))
 
+#unused in end user workflow
+def dev_test(input_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}')
+    
+    if output_path.exists():
+        output_images_paths = pathex.get_image_paths(output_path)
+        if len(output_images_paths) > 0:
+            io.input_bool("WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue." % (str(output_path)), False )
+            for filename in output_images_paths:
+                Path(filename).unlink()
+    else:
+        output_path.mkdir(parents=True, exist_ok=True)
+
+    images_paths = pathex.get_image_paths(input_path)
+
+    extract_data = []
+    
+    images_jsons = {}
+    
+    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
+            
+            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())
+            
+            extract_data.append ( ExtractSubprocessor.Data(filepath, rects=[ [l,t,r,b] ] ) ) 
+            
+    image_size = 1024
+    face_type = FaceType.HEAD  
+    extract_data = ExtractSubprocessor (extract_data, 'landmarks', image_size, face_type, device_config=device_config).run()
+    extract_data = ExtractSubprocessor (extract_data, 'final', image_size, face_type, final_output_path=output_path, device_config=device_config).run()
+                
+    for data in extract_data:
+        filepath = output_path / (data.filepath.stem+'_0.jpg')
+
+        dflimg = DFLIMG.load(filepath)
+        image_to_face_mat = dflimg.get_image_to_face_mat()
+        
+        json_dict = images_jsons[data.filepath]
+
+        ie_polys = IEPolys()
+        for shape in json_dict['shapes']:                
+            ie_poly = ie_polys.add(1)
+ 
+            points = np.array( [ [x,y] for x,y in shape['points'] ] )
+            points = LandmarksProcessor.transform_points(points, image_to_face_mat)
+            
+            for x,y in points:
+                ie_poly.add( int(x), int(y) )
+
+        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) )            
+            
+            
+        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.")