refactoring

core/imagelib/warp.py

@@ -2,11 +2,7 @@ import numpy as np
 import cv2
 from core import randomex
 
-def gen_warp_params (source, flip, rotation_range=[-10,10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None  ):
+def gen_warp_params (w, flip, rotation_range=[-10,10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None  ):
-    h,w,c = source.shape
-    if (h != w):
-        raise ValueError ('gen_warp_params accepts only square images.')
-
     if rnd_state is None:
         rnd_state = np.random
 

facelib/LandmarksProcessor.py

@@ -308,6 +308,15 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
     mat = cv2.getAffineTransform(l_t,pts2)
     return mat
   
+def get_rect_from_landmarks(image_landmarks):
+    mat = get_transform_mat(image_landmarks, 256, FaceType.FULL_NO_ALIGN)
+    
+    g_p = transform_points (  np.float32([(0,0),(255,255) ]) , mat, True)
+    
+    (l,t,r,b) = g_p[0][0], g_p[0][1], g_p[1][0], g_p[1][1]
+    
+    return (l,t,r,b)
+
 def expand_eyebrows(lmrks, eyebrows_expand_mod=1.0):
     if len(lmrks) != 68:
         raise Exception('works only with 68 landmarks')
@@ -649,7 +658,7 @@ def draw_landmarks (image, image_landmarks, color=(0,255,0), draw_circles=True,
         mask = get_image_hull_mask (image.shape, image_landmarks, ie_polys=ie_polys)
         image[...] = ( image * (1-mask) + image * mask / 2 )[...]
 
-def draw_rect_landmarks (image, rect, image_landmarks, face_size, face_type, transparent_mask=False, ie_polys=None, landmarks_color=(0,255,0)):
+def draw_rect_landmarks (image, rect, image_landmarks, face_type, face_size=256, transparent_mask=False, ie_polys=None, landmarks_color=(0,255,0)):
     draw_landmarks(image, image_landmarks, color=landmarks_color, transparent_mask=transparent_mask, ie_polys=ie_polys)
     imagelib.draw_rect (image, rect, (255,0,0), 2 )
 

mainscripts/Extractor.py

@@ -248,7 +248,7 @@ class ExtractSubprocessor(Subprocessor):
                             continue
 
                         if output_debug_path is not None:
-                            LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, image_size, face_type, transparent_mask=True)
+                            LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, face_type, image_size, transparent_mask=True)
 
                     output_path = final_output_path
                     if data.force_output_path is not None:
@@ -601,7 +601,7 @@ class ExtractSubprocessor(Subprocessor):
                 view_landmarks = LandmarksProcessor.transform_points (view_landmarks, mat)
 
             landmarks_color = (255,255,0) if self.rect_locked else (0,255,0)
-            LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.image_size, self.face_type, landmarks_color=landmarks_color)
+            LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.face_type, self.image_size, landmarks_color=landmarks_color)
             self.extract_needed = False
 
             io.show_image (self.wnd_name, image)

mainscripts/Util.py

@@ -4,7 +4,7 @@ from pathlib import Path
 import cv2
 
 from DFLIMG import *
-from facelib import LandmarksProcessor
+from facelib import LandmarksProcessor, FaceType
 from core.imagelib import IEPolys
 from core.interact import interact as io
 from core import pathex
@@ -170,8 +170,16 @@ def add_landmarks_debug_images(input_path):
         
         if img is not None:
             face_landmarks = dflimg.get_landmarks()
+            face_type = FaceType.fromString ( dflimg.get_face_type() )
+            
+            if face_type == FaceType.MARK_ONLY:
+                rect = dflimg.get_source_rect()
+                LandmarksProcessor.draw_rect_landmarks(img, rect, face_landmarks, FaceType.FULL )
+            else:
                 LandmarksProcessor.draw_landmarks(img, face_landmarks, transparent_mask=True, ie_polys=IEPolys.load(dflimg.get_ie_polys()) )
             
+            
+            
             output_file = '{}{}'.format( str(Path(str(input_path)) / filepath.stem),  '_debug.jpg')
             cv2_imwrite(output_file, img, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
 

mainscripts/dev_misc.py

@@ -9,7 +9,7 @@ from DFLIMG import *
 from facelib import FaceType, LandmarksProcessor
 from core.interact import interact as io
 from core.joblib import Subprocessor
-from core import pathex
+from core import pathex, imagelib
 from core.cv2ex import *
 
 from . import Extractor, Sorter
@@ -392,6 +392,71 @@ def extract_fanseg(input_dir, device_args={} ):
         io.log_info ("Performing extract fanseg for %d files..." % (paths_to_extract_len) )
         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 ):
+    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+'_aligned')
+
+    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)
+
+    for filepath in io.progress_bar_generator(images_paths, "Processing"):
+        filepath = Path(filepath)
+
+
+        pts_filepath = filepath.parent / (filepath.stem+'.pts')
+        if pts_filepath.exists():
+            pts = pts_filepath.read_text()
+            pts_lines = pts.split('\n')
+
+            lmrk_lines = None
+            for pts_line in pts_lines:
+                if pts_line == '{':
+                    lmrk_lines = []
+                elif pts_line == '}':
+                    break
+                else:
+                    if lmrk_lines is not None:
+                        lmrk_lines.append (pts_line)
+
+            if lmrk_lines is not None and len(lmrk_lines) == 68:
+                try:
+                    lmrks = [ np.array ( lmrk_line.strip().split(' ') ).astype(np.float32).tolist() for lmrk_line in lmrk_lines]
+                except Exception as e:
+                    print(e)
+                    print(filepath)
+                    continue
+
+                rect = LandmarksProcessor.get_rect_from_landmarks(lmrks)
+    
+                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), 95] )
+
+                DFLJPG.embed_data(output_filepath, face_type=FaceType.toString(FaceType.MARK_ONLY),
+                                                landmarks=lmrks,
+                                                source_filename=filepath.name,
+                                                source_rect=rect,
+                                                source_landmarks=lmrks
+                                    )
+
+    io.log_info("Done.")
+
 #unused in end user workflow
 def extract_umd_csv(input_file_csv,
                     face_type='full_face',
@@ -464,7 +529,7 @@ def extract_umd_csv(input_file_csv,
     io.log_info ('Faces detected:      %d' % (faces_detected) )
     io.log_info ('-------------------------')
 
-def dev_test(input_dir):
+def dev_test1(input_dir):
     input_path = Path(input_dir)
 
     dir_names = pathex.get_all_dir_names(input_path)

samplelib/SampleProcessor.py

@@ -51,6 +51,7 @@ class SampleProcessor(object):
 
         outputs = []
         for sample in samples:
+            sample_face_type = sample.face_type
             sample_bgr = sample.load_bgr()
             ct_sample_bgr = None
             h,w,c = sample_bgr.shape
@@ -60,7 +61,12 @@ class SampleProcessor(object):
             if debug and is_face_sample:
                 LandmarksProcessor.draw_landmarks (sample_bgr, sample.landmarks, (0, 1, 0))
         
-            params = imagelib.gen_warp_params(sample_bgr, sample_process_options.random_flip, rotation_range=sample_process_options.rotation_range, scale_range=sample_process_options.scale_range, tx_range=sample_process_options.tx_range, ty_range=sample_process_options.ty_range )
+            if sample_face_type == FaceType.MARK_ONLY:
+                warp_resolution = np.max( [ opts.get('resolution', 0) for opts in output_sample_types ] )
+            else:
+                warp_resolution = w
+            
+            params = imagelib.gen_warp_params(warp_resolution, sample_process_options.random_flip, rotation_range=sample_process_options.rotation_range, scale_range=sample_process_options.scale_range, tx_range=sample_process_options.tx_range, ty_range=sample_process_options.ty_range )
 
             outputs_sample = []
             for opts in output_sample_types:
@@ -124,14 +130,15 @@ class SampleProcessor(object):
                         if sample.ie_polys is not None:
                             sample.ie_polys.overlay_mask(img)
 
-                        if sample.face_type == FaceType.MARK_ONLY:
+                        if sample_face_type == FaceType.MARK_ONLY:
-                            mat  = LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], face_type)
+                            mat  = LandmarksProcessor.get_transform_mat (sample.landmarks, warp_resolution, face_type)
-                            img = cv2.warpAffine( img, mat, (sample.shape[0],sample.shape[0]), flags=cv2.INTER_LINEAR )
+                            img = cv2.warpAffine( img, mat, (warp_resolution, warp_resolution), flags=cv2.INTER_LINEAR )
+                            
                             img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=False, cv2_inter=cv2.INTER_LINEAR)
                             img = cv2.resize( img, (resolution,resolution), cv2.INTER_LINEAR )[...,None]
                         else:
-                            mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
                             img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=False, cv2_inter=cv2.INTER_LINEAR)
+                            mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)                            
                             img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LINEAR )[...,None]
 
                         if channel_type == SPCT.G:
@@ -164,14 +171,14 @@ class SampleProcessor(object):
                             if gblur_rnd_chance < chance:
                                 img = cv2.GaussianBlur(img, (gblur_rnd_kernel,) *2 , 0)
 
-                        if sample.face_type == FaceType.MARK_ONLY:
+                        if sample_face_type == FaceType.MARK_ONLY:
-                            mat  = LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], face_type)
+                            mat  = LandmarksProcessor.get_transform_mat (sample.landmarks, warp_resolution, face_type)
-                            img  = cv2.warpAffine( img,  mat, (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
+                            img  = cv2.warpAffine( img,  mat, (warp_resolution,warp_resolution), flags=cv2.INTER_CUBIC )
                             img  = imagelib.warp_by_params (params, img,  warp, transform, can_flip=True, border_replicate=True)
                             img  = cv2.resize( img,  (resolution,resolution), cv2.INTER_CUBIC )
                         else:                            
-                            mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
                             img  = imagelib.warp_by_params (params, img,  warp, transform, can_flip=True, border_replicate=True)
+                            mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
                             img  = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
 
                         img = np.clip(img.astype(np.float32), 0, 1)