0f7f73d47f
Added ability to upload multiple source face files and have it apply them all to each diffused image
659 lines
25 KiB
Python
659 lines
25 KiB
Python
import copy
|
|
import os
|
|
from dataclasses import dataclass
|
|
from typing import List, Tuple, Union
|
|
|
|
import cv2
|
|
import numpy as np
|
|
from numpy import uint8
|
|
from PIL import Image, ImageDraw
|
|
from scripts.inferencers.bisenet_mask_generator import BiSeNetMaskGenerator
|
|
from scripts.entities.face import Face
|
|
from scripts.entities.rect import Rect
|
|
import insightface
|
|
from torchvision.transforms.functional import to_pil_image
|
|
from scripts.reactor_helpers import get_image_md5hash, get_Device
|
|
from modules.face_restoration import FaceRestoration
|
|
try: # A1111
|
|
from modules import codeformer_model
|
|
except: # SD.Next
|
|
from modules.postprocess import codeformer_model
|
|
from modules.upscaler import UpscalerData
|
|
from modules.shared import state
|
|
from scripts.reactor_logger import logger
|
|
|
|
try:
|
|
from modules.paths_internal import models_path
|
|
except:
|
|
try:
|
|
from modules.paths import models_path
|
|
except:
|
|
model_path = os.path.abspath("models")
|
|
|
|
import warnings
|
|
|
|
np.warnings = warnings
|
|
np.warnings.filterwarnings('ignore')
|
|
|
|
|
|
DEVICE = get_Device()
|
|
if DEVICE == "CUDA":
|
|
PROVIDERS = ["CUDAExecutionProvider"]
|
|
else:
|
|
PROVIDERS = ["CPUExecutionProvider"]
|
|
class MaskOption:
|
|
DEFAULT_FACE_AREAS = ["Face"]
|
|
DEFAULT_FACE_SIZE:int = 512
|
|
DEFAULT_VIGNETTE_THRESHOLD:float = 0.1
|
|
DEFAULT_MASK_BLUR:int = 12,
|
|
DEFAULT_USE_MINIMAL_AREA:bool = True
|
|
|
|
@dataclass
|
|
class EnhancementOptions:
|
|
do_restore_first: bool = True
|
|
scale: int = 1
|
|
upscaler: UpscalerData = None
|
|
upscale_visibility: float = 0.5
|
|
face_restorer: FaceRestoration = None
|
|
restorer_visibility: float = 0.5
|
|
codeformer_weight: float = 0.5
|
|
|
|
|
|
@dataclass
|
|
class MaskOptions:
|
|
mask_areas:List[str]
|
|
save_face_mask: bool = False
|
|
mask_blur:int = 12
|
|
face_size:int = 512
|
|
vignette_fallback_threshold:float =0.10
|
|
use_minimal_area:bool = True
|
|
|
|
MESSAGED_STOPPED = False
|
|
MESSAGED_SKIPPED = False
|
|
|
|
def reset_messaged():
|
|
global MESSAGED_STOPPED, MESSAGED_SKIPPED
|
|
if not state.interrupted:
|
|
MESSAGED_STOPPED = False
|
|
if not state.skipped:
|
|
MESSAGED_SKIPPED = False
|
|
|
|
def check_process_halt(msgforced: bool = False):
|
|
global MESSAGED_STOPPED, MESSAGED_SKIPPED
|
|
if state.interrupted:
|
|
if not MESSAGED_STOPPED or msgforced:
|
|
logger.status("Stopped by User")
|
|
MESSAGED_STOPPED = True
|
|
return True
|
|
if state.skipped:
|
|
if not MESSAGED_SKIPPED or msgforced:
|
|
logger.status("Skipped by User")
|
|
MESSAGED_SKIPPED = True
|
|
return True
|
|
return False
|
|
|
|
|
|
FS_MODEL = None
|
|
MASK_MODEL = None
|
|
CURRENT_FS_MODEL_PATH = None
|
|
CURRENT_MASK_MODEL_PATH = None
|
|
ANALYSIS_MODEL = None
|
|
|
|
SOURCE_FACES = None
|
|
SOURCE_IMAGE_HASH = None
|
|
TARGET_FACES = None
|
|
TARGET_IMAGE_HASH = None
|
|
|
|
|
|
def getAnalysisModel():
|
|
global ANALYSIS_MODEL
|
|
if ANALYSIS_MODEL is None:
|
|
ANALYSIS_MODEL = insightface.app.FaceAnalysis(
|
|
name="buffalo_l", providers=PROVIDERS, root=os.path.join(models_path, "insightface") # note: allowed_modules=['detection', 'genderage']
|
|
)
|
|
return ANALYSIS_MODEL
|
|
|
|
|
|
def getFaceSwapModel(model_path: str):
|
|
global FS_MODEL
|
|
global CURRENT_FS_MODEL_PATH
|
|
if CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
|
|
CURRENT_FS_MODEL_PATH = model_path
|
|
FS_MODEL = insightface.model_zoo.get_model(model_path, providers=PROVIDERS)
|
|
|
|
return FS_MODEL
|
|
|
|
|
|
|
|
|
|
def restore_face(image: Image, enhancement_options: EnhancementOptions):
|
|
result_image = image
|
|
|
|
if check_process_halt(msgforced=True):
|
|
return result_image
|
|
|
|
if enhancement_options.face_restorer is not None:
|
|
original_image = result_image.copy()
|
|
logger.status("Restoring the face with %s", enhancement_options.face_restorer.name())
|
|
numpy_image = np.array(result_image)
|
|
if enhancement_options.face_restorer.name() == "CodeFormer":
|
|
numpy_image = codeformer_model.codeformer.restore(
|
|
numpy_image, w=enhancement_options.codeformer_weight
|
|
)
|
|
else:
|
|
numpy_image = enhancement_options.face_restorer.restore(numpy_image)
|
|
restored_image = Image.fromarray(numpy_image)
|
|
result_image = Image.blend(
|
|
original_image, restored_image, enhancement_options.restorer_visibility
|
|
)
|
|
|
|
return result_image
|
|
|
|
def upscale_image(image: Image, enhancement_options: EnhancementOptions):
|
|
result_image = image
|
|
|
|
if check_process_halt(msgforced=True):
|
|
return result_image
|
|
|
|
if enhancement_options.upscaler is not None and enhancement_options.upscaler.name != "None":
|
|
original_image = result_image.copy()
|
|
logger.status(
|
|
"Upscaling with %s scale = %s",
|
|
enhancement_options.upscaler.name,
|
|
enhancement_options.scale,
|
|
)
|
|
result_image = enhancement_options.upscaler.scaler.upscale(
|
|
original_image, enhancement_options.scale, enhancement_options.upscaler.data_path
|
|
)
|
|
if enhancement_options.scale == 1:
|
|
result_image = Image.blend(
|
|
original_image, result_image, enhancement_options.upscale_visibility
|
|
)
|
|
|
|
return result_image
|
|
|
|
def enhance_image(image: Image, enhancement_options: EnhancementOptions):
|
|
result_image = image
|
|
|
|
if check_process_halt(msgforced=True):
|
|
return result_image
|
|
|
|
if enhancement_options.do_restore_first:
|
|
|
|
result_image = restore_face(result_image, enhancement_options)
|
|
result_image = upscale_image(result_image, enhancement_options)
|
|
|
|
else:
|
|
|
|
result_image = upscale_image(result_image, enhancement_options)
|
|
result_image = restore_face(result_image, enhancement_options)
|
|
|
|
return result_image
|
|
def enhance_image_and_mask(image: Image.Image, enhancement_options: EnhancementOptions,target_img_orig:Image.Image,entire_mask_image:Image.Image)->Tuple[Image.Image,Image.Image]:
|
|
result_image = image
|
|
|
|
if check_process_halt(msgforced=True):
|
|
return result_image
|
|
|
|
if enhancement_options.do_restore_first:
|
|
|
|
|
|
result_image = restore_face(result_image, enhancement_options)
|
|
|
|
transparent = Image.new("RGBA",result_image.size)
|
|
masked_faces = Image.composite(result_image.convert("RGBA"),transparent,entire_mask_image)
|
|
result_image = Image.composite(result_image,target_img_orig,entire_mask_image)
|
|
result_image = upscale_image(result_image, enhancement_options)
|
|
|
|
else:
|
|
|
|
result_image = upscale_image(result_image, enhancement_options)
|
|
entire_mask_image = Image.fromarray(cv2.resize(np.array(entire_mask_image),result_image.size, interpolation=cv2.INTER_AREA)).convert("L")
|
|
|
|
result_image = Image.composite(result_image,target_img_orig,entire_mask_image)
|
|
result_image = restore_face(result_image, enhancement_options)
|
|
transparent = Image.new("RGBA",result_image.size)
|
|
masked_faces = Image.composite(result_image.convert("RGBA"),transparent,entire_mask_image)
|
|
return result_image, masked_faces
|
|
|
|
|
|
|
|
def get_gender(face, face_index):
|
|
gender = [
|
|
x.sex
|
|
for x in face
|
|
]
|
|
gender.reverse()
|
|
try:
|
|
face_gender = gender[face_index]
|
|
except:
|
|
logger.error("Gender Detection: No face with index = %s was found", face_index)
|
|
return "None"
|
|
return face_gender
|
|
|
|
def get_face_gender(
|
|
face,
|
|
face_index,
|
|
gender_condition,
|
|
operated: str,
|
|
gender_detected,
|
|
):
|
|
face_gender = gender_detected
|
|
if face_gender == "None":
|
|
return None, 0
|
|
logger.status("%s Face %s: Detected Gender -%s-", operated, face_index, face_gender)
|
|
if (gender_condition == 1 and face_gender == "F") or (gender_condition == 2 and face_gender == "M"):
|
|
logger.status("OK - Detected Gender matches Condition")
|
|
try:
|
|
return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
|
|
except IndexError:
|
|
return None, 0
|
|
else:
|
|
logger.status("WRONG - Detected Gender doesn't match Condition")
|
|
return sorted(face, key=lambda x: x.bbox[0])[face_index], 1
|
|
|
|
def get_face_age(face, face_index):
|
|
age = [
|
|
x.age
|
|
for x in face
|
|
]
|
|
age.reverse()
|
|
try:
|
|
face_age = age[face_index]
|
|
except:
|
|
logger.error("Age Detection: No face with index = %s was found", face_index)
|
|
return "None"
|
|
return face_age
|
|
|
|
def half_det_size(det_size):
|
|
logger.status("Trying to halve 'det_size' parameter")
|
|
return (det_size[0] // 2, det_size[1] // 2)
|
|
|
|
def analyze_faces(img_data: np.ndarray, det_size=(640, 640)):
|
|
logger.info("Applied Execution Provider: %s", PROVIDERS[0])
|
|
face_analyser = copy.deepcopy(getAnalysisModel())
|
|
face_analyser.prepare(ctx_id=0, det_size=det_size)
|
|
return face_analyser.get(img_data)
|
|
|
|
def get_face_single(img_data: np.ndarray, face, face_index=0, det_size=(640, 640), gender_source=0, gender_target=0):
|
|
|
|
buffalo_path = os.path.join(models_path, "insightface/models/buffalo_l.zip")
|
|
if os.path.exists(buffalo_path):
|
|
os.remove(buffalo_path)
|
|
|
|
face_age = "None"
|
|
try:
|
|
face_age = get_face_age(face, face_index)
|
|
except:
|
|
logger.error("Cannot detect any Age for Face index = %s", face_index)
|
|
|
|
face_gender = "None"
|
|
try:
|
|
face_gender = get_gender(face, face_index)
|
|
gender_detected = face_gender
|
|
face_gender = "Female" if face_gender == "F" else ("Male" if face_gender == "M" else "None")
|
|
except:
|
|
logger.error("Cannot detect any Gender for Face index = %s", face_index)
|
|
|
|
if gender_source != 0:
|
|
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
|
|
det_size_half = half_det_size(det_size)
|
|
return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
|
|
faces, wrong_gender = get_face_gender(face,face_index,gender_source,"Source",gender_detected)
|
|
return faces, wrong_gender, face_age, face_gender
|
|
|
|
if gender_target != 0:
|
|
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
|
|
det_size_half = half_det_size(det_size)
|
|
return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
|
|
faces, wrong_gender = get_face_gender(face,face_index,gender_target,"Target",gender_detected)
|
|
return faces, wrong_gender, face_age, face_gender
|
|
|
|
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
|
|
det_size_half = half_det_size(det_size)
|
|
return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
|
|
|
|
try:
|
|
return sorted(face, key=lambda x: x.bbox[0])[face_index], 0, face_age, face_gender
|
|
except IndexError:
|
|
return None, 0, face_age, face_gender
|
|
|
|
|
|
def swap_face(
|
|
source_img: Image.Image,
|
|
target_img: Image.Image,
|
|
model: Union[str, None] = None,
|
|
source_faces_index: List[int] = [0],
|
|
faces_index: List[int] = [0],
|
|
enhancement_options: Union[EnhancementOptions, None] = None,
|
|
gender_source: int = 0,
|
|
gender_target: int = 0,
|
|
source_hash_check: bool = True,
|
|
target_hash_check: bool = False,
|
|
device: str = "CPU",
|
|
mask_face:bool = False,
|
|
mask_options:Union[MaskOptions, None]= None
|
|
):
|
|
global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, PROVIDERS
|
|
result_image = target_img
|
|
masked_faces = None
|
|
PROVIDERS = ["CUDAExecutionProvider"] if device == "CUDA" else ["CPUExecutionProvider"]
|
|
|
|
if check_process_halt():
|
|
return result_image, [], 0
|
|
|
|
if model is not None:
|
|
|
|
if isinstance(source_img, str): # source_img is a base64 string
|
|
import base64, io
|
|
if 'base64,' in source_img: # check if the base64 string has a data URL scheme
|
|
# split the base64 string to get the actual base64 encoded image data
|
|
base64_data = source_img.split('base64,')[-1]
|
|
# decode base64 string to bytes
|
|
img_bytes = base64.b64decode(base64_data)
|
|
else:
|
|
# if no data URL scheme, just decode
|
|
img_bytes = base64.b64decode(source_img)
|
|
|
|
source_img = Image.open(io.BytesIO(img_bytes))
|
|
|
|
source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
|
|
target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
|
|
target_img_orig = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
|
|
entire_mask_image = np.zeros_like(np.array(target_img))
|
|
output: List = []
|
|
output_info: str = ""
|
|
swapped = 0
|
|
|
|
if source_hash_check:
|
|
|
|
source_image_md5hash = get_image_md5hash(source_img)
|
|
|
|
if SOURCE_IMAGE_HASH is None:
|
|
SOURCE_IMAGE_HASH = source_image_md5hash
|
|
source_image_same = False
|
|
else:
|
|
source_image_same = True if SOURCE_IMAGE_HASH == source_image_md5hash else False
|
|
if not source_image_same:
|
|
SOURCE_IMAGE_HASH = source_image_md5hash
|
|
|
|
logger.info("Source Image MD5 Hash = %s", SOURCE_IMAGE_HASH)
|
|
logger.info("Source Image the Same? %s", source_image_same)
|
|
|
|
if SOURCE_FACES is None or not source_image_same:
|
|
logger.status("Analyzing Source Image...")
|
|
source_faces = analyze_faces(source_img)
|
|
SOURCE_FACES = source_faces
|
|
elif source_image_same:
|
|
logger.status("Using Ready Source Face(s) Model...")
|
|
source_faces = SOURCE_FACES
|
|
|
|
else:
|
|
logger.status("Analyzing Source Image...")
|
|
source_faces = analyze_faces(source_img)
|
|
|
|
if source_faces is not None:
|
|
|
|
if target_hash_check:
|
|
|
|
target_image_md5hash = get_image_md5hash(target_img)
|
|
|
|
if TARGET_IMAGE_HASH is None:
|
|
TARGET_IMAGE_HASH = target_image_md5hash
|
|
target_image_same = False
|
|
else:
|
|
target_image_same = True if TARGET_IMAGE_HASH == target_image_md5hash else False
|
|
if not target_image_same:
|
|
TARGET_IMAGE_HASH = target_image_md5hash
|
|
|
|
logger.info("Target Image MD5 Hash = %s", TARGET_IMAGE_HASH)
|
|
logger.info("Target Image the Same? %s", target_image_same)
|
|
|
|
if TARGET_FACES is None or not target_image_same:
|
|
logger.status("Analyzing Target Image...")
|
|
target_faces = analyze_faces(target_img)
|
|
TARGET_FACES = target_faces
|
|
elif target_image_same:
|
|
logger.status("Using Ready Target Face(s) Model...")
|
|
target_faces = TARGET_FACES
|
|
|
|
else:
|
|
logger.status("Analyzing Target Image...")
|
|
target_faces = analyze_faces(target_img)
|
|
|
|
logger.status("Detecting Source Face, Index = %s", source_faces_index[0])
|
|
source_face, wrong_gender, source_age, source_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source)
|
|
if source_age != "None" or source_gender != "None":
|
|
logger.status("Detected: -%s- y.o. %s", source_age, source_gender)
|
|
|
|
output_info = f"SourceFaceIndex={source_faces_index[0]};Age={source_age};Gender={source_gender}\n"
|
|
output.append(output_info)
|
|
|
|
if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
|
|
logger.status("Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.")
|
|
elif source_face is not None:
|
|
|
|
result = target_img
|
|
face_swapper = getFaceSwapModel(model)
|
|
|
|
source_face_idx = 0
|
|
|
|
for face_num in faces_index:
|
|
if check_process_halt():
|
|
return result_image, [], 0
|
|
if len(source_faces_index) > 1 and source_face_idx > 0:
|
|
logger.status("Detecting Source Face, Index = %s", source_faces_index[source_face_idx])
|
|
source_face, wrong_gender, source_age, source_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source)
|
|
if source_age != "None" or source_gender != "None":
|
|
logger.status("Detected: -%s- y.o. %s", source_age, source_gender)
|
|
|
|
output_info = f"SourceFaceIndex={source_faces_index[source_face_idx]};Age={source_age};Gender={source_gender}\n"
|
|
output.append(output_info)
|
|
|
|
source_face_idx += 1
|
|
|
|
if source_face is not None and wrong_gender == 0:
|
|
logger.status("Detecting Target Face, Index = %s", face_num)
|
|
target_face, wrong_gender, target_age, target_gender = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target)
|
|
if target_age != "None" or target_gender != "None":
|
|
logger.status("Detected: -%s- y.o. %s", target_age, target_gender)
|
|
|
|
output_info = f"TargetFaceIndex={face_num};Age={target_age};Gender={target_gender}\n"
|
|
output.append(output_info)
|
|
|
|
if target_face is not None and wrong_gender == 0:
|
|
logger.status("Swapping Source into Target")
|
|
swapped_image = face_swapper.get(result, target_face, source_face)
|
|
|
|
if mask_face:
|
|
result = apply_face_mask(swapped_image=swapped_image,target_image=result,target_face=target_face,entire_mask_image=entire_mask_image,mask_options=mask_options)
|
|
else:
|
|
result = swapped_image
|
|
swapped += 1
|
|
|
|
elif wrong_gender == 1:
|
|
wrong_gender = 0
|
|
|
|
if source_face_idx == len(source_faces_index):
|
|
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
|
|
|
|
if enhancement_options is not None and len(source_faces_index) > 1:
|
|
result_image = enhance_image(result_image, enhancement_options)
|
|
|
|
return result_image, output, swapped
|
|
|
|
else:
|
|
logger.status(f"No target face found for {face_num}")
|
|
|
|
elif wrong_gender == 1:
|
|
wrong_gender = 0
|
|
|
|
if source_face_idx == len(source_faces_index):
|
|
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
|
|
|
|
if enhancement_options is not None and len(source_faces_index) > 1:
|
|
result_image = enhance_image(result_image, enhancement_options)
|
|
|
|
return result_image, output, swapped
|
|
|
|
else:
|
|
logger.status(f"No source face found for face number {source_face_idx}.")
|
|
|
|
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
|
|
|
|
if enhancement_options is not None and swapped > 0:
|
|
if mask_face and entire_mask_image is not None:
|
|
result_image, masked_faces = enhance_image_and_mask(result_image, enhancement_options,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L"))
|
|
else:
|
|
result_image = enhance_image(result_image, enhancement_options)
|
|
elif mask_face and entire_mask_image is not None and swapped > 0:
|
|
result_image = Image.composite(result_image,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L"))
|
|
|
|
else:
|
|
logger.status("No source face(s) in the provided Index")
|
|
else:
|
|
logger.status("No source face(s) found")
|
|
|
|
return result_image, output, swapped,masked_faces
|
|
|
|
|
|
|
|
def apply_face_mask(swapped_image:np.ndarray,target_image:np.ndarray,target_face,entire_mask_image:np.array,mask_options:Union[MaskOptions,None] = None)->np.ndarray:
|
|
logger.status("Masking Face")
|
|
mask_generator = BiSeNetMaskGenerator()
|
|
face = Face(target_image,Rect.from_ndarray(np.array(target_face.bbox)),1.6,mask_options.face_size,"")
|
|
face_image = np.array(face.image)
|
|
face_area_on_image = face.face_area_on_image
|
|
|
|
mask = mask_generator.generate_mask(face_image,face_area_on_image=face_area_on_image,affected_areas=mask_options.mask_areas,mask_size=0,use_minimal_area=mask_options.use_minimal_area)
|
|
mask = cv2.blur(mask, (mask_options.mask_blur, mask_options.mask_blur))
|
|
larger_mask = cv2.resize(mask, dsize=(face.width, face.height))
|
|
entire_mask_image[
|
|
face.top : face.bottom,
|
|
face.left : face.right,
|
|
] = larger_mask
|
|
|
|
|
|
result = Image.composite(Image.fromarray(swapped_image),Image.fromarray(target_image), Image.fromarray(entire_mask_image).convert("L"))
|
|
return np.array(result)
|
|
|
|
|
|
def correct_face_tilt(angle: float) -> bool:
|
|
|
|
angle = abs(angle)
|
|
if angle > 180:
|
|
angle = 360 - angle
|
|
return angle > 40
|
|
def _dilate(arr: np.ndarray, value: int) -> np.ndarray:
|
|
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
|
|
return cv2.dilate(arr, kernel, iterations=1)
|
|
|
|
|
|
def _erode(arr: np.ndarray, value: int) -> np.ndarray:
|
|
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
|
|
return cv2.erode(arr, kernel, iterations=1)
|
|
colors = [
|
|
(255, 0, 0),
|
|
(0, 255, 0),
|
|
(0, 0, 255),
|
|
(255, 255, 0),
|
|
(255, 0, 255),
|
|
(0, 255, 255),
|
|
(255, 255, 255),
|
|
(128, 0, 0),
|
|
(0, 128, 0),
|
|
(128, 128, 0),
|
|
(0, 0, 128),
|
|
(0, 128, 128),
|
|
]
|
|
|
|
|
|
def color_generator(colors):
|
|
while True:
|
|
for color in colors:
|
|
yield color
|
|
|
|
|
|
color_iter = color_generator(colors)
|
|
|
|
def dilate_erode(img: Image.Image, value: int) -> Image.Image:
|
|
"""
|
|
The dilate_erode function takes an image and a value.
|
|
If the value is positive, it dilates the image by that amount.
|
|
If the value is negative, it erodes the image by that amount.
|
|
|
|
Parameters
|
|
----------
|
|
img: PIL.Image.Image
|
|
the image to be processed
|
|
value: int
|
|
kernel size of dilation or erosion
|
|
|
|
Returns
|
|
-------
|
|
PIL.Image.Image
|
|
The image that has been dilated or eroded
|
|
"""
|
|
if value == 0:
|
|
return img
|
|
|
|
arr = np.array(img)
|
|
arr = _dilate(arr, value) if value > 0 else _erode(arr, -value)
|
|
|
|
return Image.fromarray(arr)
|
|
|
|
def mask_to_pil(masks, shape: tuple[int, int]) -> list[Image.Image]:
|
|
"""
|
|
Parameters
|
|
----------
|
|
masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W).
|
|
The device can be CUDA, but `to_pil_image` takes care of that.
|
|
|
|
shape: tuple[int, int]
|
|
(width, height) of the original image
|
|
"""
|
|
n = masks.shape[0]
|
|
return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]
|
|
|
|
def create_mask_from_bbox(
|
|
bboxes: list[list[float]], shape: tuple[int, int]
|
|
) -> list[Image.Image]:
|
|
"""
|
|
Parameters
|
|
----------
|
|
bboxes: list[list[float]]
|
|
list of [x1, y1, x2, y2]
|
|
bounding boxes
|
|
shape: tuple[int, int]
|
|
shape of the image (width, height)
|
|
|
|
Returns
|
|
-------
|
|
masks: list[Image.Image]
|
|
A list of masks
|
|
|
|
"""
|
|
masks = []
|
|
for bbox in bboxes:
|
|
mask = Image.new("L", shape, 0)
|
|
mask_draw = ImageDraw.Draw(mask)
|
|
mask_draw.rectangle(bbox, fill=255)
|
|
masks.append(mask)
|
|
return masks
|
|
|
|
def rotate_image(image: Image, angle: float) -> Image:
|
|
if angle == 0:
|
|
return image
|
|
return Image.fromarray(rotate_array(np.array(image), angle))
|
|
|
|
|
|
def rotate_array(image: np.ndarray, angle: float) -> np.ndarray:
|
|
if angle == 0:
|
|
return image
|
|
|
|
h, w = image.shape[:2]
|
|
center = (w // 2, h // 2)
|
|
|
|
M = cv2.getRotationMatrix2D(center, angle, 1.0)
|
|
return cv2.warpAffine(image, M, (w, h))
|