653 lines
24 KiB
Python
653 lines
24 KiB
Python
import copy
|
|
import os
|
|
from dataclasses import dataclass
|
|
from typing import List, 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"]
|
|
|
|
|
|
@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
|
|
|
|
|
|
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)->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)
|
|
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)
|
|
|
|
return result_image
|
|
|
|
|
|
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
|
|
):
|
|
global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, PROVIDERS
|
|
result_image = target_img
|
|
|
|
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)
|
|
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 = 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
|
|
|
|
|
|
|
|
def apply_face_mask(swapped_image:np.ndarray,target_image:np.ndarray,target_face,entire_mask_image:np.array)->np.ndarray:
|
|
logger.status("Masking Face")
|
|
mask_generator = BiSeNetMaskGenerator()
|
|
face = Face(target_image,Rect.from_ndarray(np.array(target_face.bbox)),1.6,512,"")
|
|
face_image = np.array(face.image)
|
|
process_face_image(face)
|
|
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=["Face"],mask_size=0,use_minimal_area=True)
|
|
mask = cv2.blur(mask, (12, 12))
|
|
"""entire_mask_image = np.zeros_like(target_image)"""
|
|
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 process_face_image(
|
|
face: Face,
|
|
**kwargs,
|
|
) -> Image:
|
|
image = np.array(face.image)
|
|
overlay = image.copy()
|
|
cv2.rectangle(overlay, (0, 0), (image.shape[1], image.shape[0]), next(color_iter), -1)
|
|
l, t, r, b = face.face_area_on_image
|
|
cv2.rectangle(overlay, (l, t), (r, b), (0, 0, 0), 10)
|
|
if face.landmarks_on_image is not None:
|
|
for landmark in face.landmarks_on_image:
|
|
cv2.circle(overlay, (int(landmark.x), int(landmark.y)), 6, (0, 0, 0), 10)
|
|
alpha = 0.3
|
|
output = cv2.addWeighted(image, 1 - alpha, overlay, alpha, 0)
|
|
|
|
return Image.fromarray(output)
|
|
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))
|