import multiprocessing import shutil from pathlib import Path import cv2 import numpy as np 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.cv2ex import * from . import Extractor, Sorter from .Extractor import ExtractSubprocessor def extract_vggface2_dataset(input_dir, device_args={} ): multi_gpu = device_args.get('multi_gpu', False) cpu_only = device_args.get('cpu_only', False) input_path = Path(input_dir) if not input_path.exists(): raise ValueError('Input directory not found. Please ensure it exists.') bb_csv = input_path / 'loose_bb_train.csv' if not bb_csv.exists(): raise ValueError('loose_bb_train.csv found. Please ensure it exists.') bb_lines = bb_csv.read_text().split('\n') bb_lines.pop(0) bb_dict = {} for line in bb_lines: name, l, t, w, h = line.split(',') name = name[1:-1] l, t, w, h = [ int(x) for x in (l, t, w, h) ] bb_dict[name] = (l,t,w, h) output_path = input_path.parent / (input_path.name + '_out') dir_names = pathex.get_all_dir_names(input_path) if not output_path.exists(): output_path.mkdir(parents=True, exist_ok=True) data = [] for dir_name in io.progress_bar_generator(dir_names, "Collecting"): cur_input_path = input_path / dir_name cur_output_path = output_path / dir_name if not cur_output_path.exists(): cur_output_path.mkdir(parents=True, exist_ok=True) input_path_image_paths = pathex.get_image_paths(cur_input_path) for filename in input_path_image_paths: filename_path = Path(filename) name = filename_path.parent.name + '/' + filename_path.stem if name not in bb_dict: continue l,t,w,h = bb_dict[name] if min(w,h) < 128: continue data += [ ExtractSubprocessor.Data(filename=filename,rects=[ (l,t,l+w,t+h) ], landmarks_accurate=False, force_output_path=cur_output_path ) ] face_type = FaceType.fromString('full_face') io.log_info ('Performing 2nd pass...') data = ExtractSubprocessor (data, 'landmarks', 256, face_type, debug_dir=None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).run() io.log_info ('Performing 3rd pass...') ExtractSubprocessor (data, 'final', 256, face_type, debug_dir=None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=None).run() """ import code code.interact(local=dict(globals(), **locals())) data_len = len(data) i = 0 while i < data_len-1: i_name = Path(data[i].filename).parent.name sub_data = [] for j in range (i, data_len): j_name = Path(data[j].filename).parent.name if i_name == j_name: sub_data += [ data[j] ] else: break i = j cur_output_path = output_path / i_name io.log_info (f"Processing: {str(cur_output_path)}, {i}/{data_len} ") if not cur_output_path.exists(): cur_output_path.mkdir(parents=True, exist_ok=True) for dir_name in dir_names: cur_input_path = input_path / dir_name cur_output_path = output_path / dir_name input_path_image_paths = pathex.get_image_paths(cur_input_path) l = len(input_path_image_paths) #if l < 250 or l > 350: # continue io.log_info (f"Processing: {str(cur_input_path)} ") if not cur_output_path.exists(): cur_output_path.mkdir(parents=True, exist_ok=True) data = [] for filename in input_path_image_paths: filename_path = Path(filename) name = filename_path.parent.name + '/' + filename_path.stem if name not in bb_dict: continue bb = bb_dict[name] l,t,w,h = bb if min(w,h) < 128: continue data += [ ExtractSubprocessor.Data(filename=filename,rects=[ (l,t,l+w,t+h) ], landmarks_accurate=False ) ] io.log_info ('Performing 2nd pass...') data = ExtractSubprocessor (data, 'landmarks', 256, face_type, debug_dir=None, multi_gpu=False, cpu_only=False, manual=False).run() io.log_info ('Performing 3rd pass...') data = ExtractSubprocessor (data, 'final', 256, face_type, debug_dir=None, multi_gpu=False, cpu_only=False, manual=False, final_output_path=cur_output_path).run() io.log_info (f"Sorting: {str(cur_output_path)} ") Sorter.main (input_path=str(cur_output_path), sort_by_method='hist') import code code.interact(local=dict(globals(), **locals())) #try: # io.log_info (f"Removing: {str(cur_input_path)} ") # shutil.rmtree(cur_input_path) #except: # io.log_info (f"unable to remove: {str(cur_input_path)} ") def extract_vggface2_dataset(input_dir, device_args={} ): multi_gpu = device_args.get('multi_gpu', False) cpu_only = device_args.get('cpu_only', False) input_path = Path(input_dir) if not input_path.exists(): raise ValueError('Input directory not found. Please ensure it exists.') output_path = input_path.parent / (input_path.name + '_out') dir_names = pathex.get_all_dir_names(input_path) if not output_path.exists(): output_path.mkdir(parents=True, exist_ok=True) for dir_name in dir_names: cur_input_path = input_path / dir_name cur_output_path = output_path / dir_name l = len(pathex.get_image_paths(cur_input_path)) if l < 250 or l > 350: continue io.log_info (f"Processing: {str(cur_input_path)} ") if not cur_output_path.exists(): cur_output_path.mkdir(parents=True, exist_ok=True) Extractor.main( str(cur_input_path), str(cur_output_path), detector='s3fd', image_size=256, face_type='full_face', max_faces_from_image=1, device_args=device_args ) io.log_info (f"Sorting: {str(cur_input_path)} ") Sorter.main (input_path=str(cur_output_path), sort_by_method='hist') try: io.log_info (f"Removing: {str(cur_input_path)} ") shutil.rmtree(cur_input_path) except: io.log_info (f"unable to remove: {str(cur_input_path)} ") """ class CelebAMASKHQSubprocessor(Subprocessor): class Cli(Subprocessor.Cli): #override def on_initialize(self, client_dict): self.masks_files_paths = client_dict['masks_files_paths'] return None #override def process_data(self, data): filename = data[0] dflimg = DFLIMG.load(Path(filename)) image_to_face_mat = dflimg.get_image_to_face_mat() src_filename = dflimg.get_source_filename() img = cv2_imread(filename) h,w,c = img.shape fanseg_mask = LandmarksProcessor.get_image_hull_mask(img.shape, dflimg.get_landmarks() ) idx_name = '%.5d' % int(src_filename.split('.')[0]) idx_files = [ x for x in self.masks_files_paths if idx_name in x ] skin_files = [ x for x in idx_files if 'skin' in x ] eye_glass_files = [ x for x in idx_files if 'eye_g' in x ] for files, is_invert in [ (skin_files,False), (eye_glass_files,True) ]: if len(files) > 0: mask = cv2_imread(files[0]) mask = mask[...,0] mask[mask == 255] = 1 mask = mask.astype(np.float32) mask = cv2.resize(mask, (1024,1024) ) mask = cv2.warpAffine(mask, image_to_face_mat, (w, h), cv2.INTER_LANCZOS4) if not is_invert: fanseg_mask *= mask[...,None] else: fanseg_mask *= (1-mask[...,None]) dflimg.embed_and_set (filename, fanseg_mask=fanseg_mask) return 1 #override def get_data_name (self, data): #return string identificator of your data return data[0] #override def __init__(self, image_paths, masks_files_paths ): self.image_paths = image_paths self.masks_files_paths = masks_files_paths self.result = [] super().__init__('CelebAMASKHQSubprocessor', CelebAMASKHQSubprocessor.Cli, 60) #override def process_info_generator(self): for i in range(min(multiprocessing.cpu_count(), 8)): yield 'CPU%d' % (i), {}, {'masks_files_paths' : self.masks_files_paths } #override def on_clients_initialized(self): io.progress_bar ("Processing", len (self.image_paths)) #override def on_clients_finalized(self): io.progress_bar_close() #override def get_data(self, host_dict): if len (self.image_paths) > 0: return [self.image_paths.pop(0)] return None #override def on_data_return (self, host_dict, data): self.image_paths.insert(0, data[0]) #override def on_result (self, host_dict, data, result): io.progress_bar_inc(1) #override def get_result(self): return self.result #unused in end user workflow def apply_celebamaskhq(input_dir ): input_path = Path(input_dir) img_path = input_path / 'aligned' mask_path = input_path / 'mask' if not img_path.exists(): raise ValueError(f'{str(img_path)} directory not found. Please ensure it exists.') CelebAMASKHQSubprocessor(pathex.get_image_paths(img_path), pathex.get_image_paths(mask_path, subdirs=True) ).run() return paths_to_extract = [] for filename in io.progress_bar_generator(pathex.get_image_paths(img_path), desc="Processing"): filepath = Path(filename) dflimg = DFLIMG.load(filepath) if dflimg is not None: paths_to_extract.append (filepath) image_to_face_mat = dflimg.get_image_to_face_mat() src_filename = dflimg.get_source_filename() #img = cv2_imread(filename) h,w,c = dflimg.get_shape() fanseg_mask = LandmarksProcessor.get_image_hull_mask( (h,w,c), dflimg.get_landmarks() ) idx_name = '%.5d' % int(src_filename.split('.')[0]) idx_files = [ x for x in masks_files if idx_name in x ] skin_files = [ x for x in idx_files if 'skin' in x ] eye_glass_files = [ x for x in idx_files if 'eye_g' in x ] for files, is_invert in [ (skin_files,False), (eye_glass_files,True) ]: if len(files) > 0: mask = cv2_imread(files[0]) mask = mask[...,0] mask[mask == 255] = 1 mask = mask.astype(np.float32) mask = cv2.resize(mask, (1024,1024) ) mask = cv2.warpAffine(mask, image_to_face_mat, (w, h), cv2.INTER_LANCZOS4) if not is_invert: fanseg_mask *= mask[...,None] else: fanseg_mask *= (1-mask[...,None]) #cv2.imshow("", (fanseg_mask*255).astype(np.uint8) ) #cv2.waitKey(0) dflimg.embed_and_set (filename, fanseg_mask=fanseg_mask) #import code #code.interact(local=dict(globals(), **locals())) #unused in end user workflow def extract_fanseg(input_dir, device_args={} ): multi_gpu = device_args.get('multi_gpu', False) cpu_only = device_args.get('cpu_only', False) input_path = Path(input_dir) if not input_path.exists(): raise ValueError('Input directory not found. Please ensure it exists.') paths_to_extract = [] for filename in pathex.get_image_paths(input_path) : filepath = Path(filename) dflimg = DFLIMG.load ( filepath ) if dflimg is not None: paths_to_extract.append (filepath) paths_to_extract_len = len(paths_to_extract) if paths_to_extract_len > 0: 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 extract_umd_csv(input_file_csv, image_size=256, face_type='full_face', device_args={} ): #extract faces from umdfaces.io dataset csv file with pitch,yaw,roll info. multi_gpu = device_args.get('multi_gpu', False) cpu_only = device_args.get('cpu_only', False) face_type = FaceType.fromString(face_type) input_file_csv_path = Path(input_file_csv) if not input_file_csv_path.exists(): raise ValueError('input_file_csv not found. Please ensure it exists.') input_file_csv_root_path = input_file_csv_path.parent output_path = input_file_csv_path.parent / ('aligned_' + input_file_csv_path.name) io.log_info("Output dir is %s." % (str(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) try: with open( str(input_file_csv_path), 'r') as f: csv_file = f.read() except Exception as e: io.log_err("Unable to open or read file " + str(input_file_csv_path) + ": " + str(e) ) return strings = csv_file.split('\n') keys = strings[0].split(',') keys_len = len(keys) csv_data = [] for i in range(1, len(strings)): values = strings[i].split(',') if keys_len != len(values): io.log_err("Wrong string in csv file, skipping.") continue csv_data += [ { keys[n] : values[n] for n in range(keys_len) } ] data = [] for d in csv_data: filename = input_file_csv_root_path / d['FILE'] x,y,w,h = float(d['FACE_X']), float(d['FACE_Y']), float(d['FACE_WIDTH']), float(d['FACE_HEIGHT']) data += [ ExtractSubprocessor.Data(filename=filename, rects=[ [x,y,x+w,y+h] ]) ] images_found = len(data) faces_detected = 0 if len(data) > 0: io.log_info ("Performing 2nd pass from csv file...") data = ExtractSubprocessor (data, 'landmarks', multi_gpu=multi_gpu, cpu_only=cpu_only).run() io.log_info ('Performing 3rd pass...') data = ExtractSubprocessor (data, 'final', image_size, face_type, None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=output_path).run() faces_detected += sum([d.faces_detected for d in data]) io.log_info ('-------------------------') io.log_info ('Images found: %d' % (images_found) ) io.log_info ('Faces detected: %d' % (faces_detected) ) io.log_info ('-------------------------') def dev_test(input_dir): input_path = Path(input_dir) dir_names = pathex.get_all_dir_names(input_path) for dir_name in io.progress_bar_generator(dir_names, desc="Processing"): img_paths = pathex.get_image_paths (input_path / dir_name) for filename in img_paths: filepath = Path(filename) dflimg = DFLIMG.load (filepath) if dflimg is None: raise ValueError dflimg.embed_and_set(filename, person_name=dir_name) #import code #code.interact(local=dict(globals(), **locals()))