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Update face_preprocess.py #620

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284 changes: 138 additions & 146 deletions face_adapter/face_preprocess.py
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Original file line number Diff line number Diff line change
@@ -1,151 +1,143 @@
import cv2
import numpy as np
from skimage import transform as trans

def read_image(img_path, **kwargs):
mode = kwargs.get('mode', 'rgb')
layout = kwargs.get('layout', 'HWC')
if mode=='gray':
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
else:
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
if mode=='rgb':
#print('to rgb')
img = img[...,::-1]
if layout=='CHW':
img = np.transpose(img, (2,0,1))
return img


def preprocess(img, bbox=None, landmark=None, **kwargs):
if isinstance(img, str):
img = read_image(img, **kwargs)
M = None
image_size = []
str_image_size = kwargs.get('image_size', '')
if len(str_image_size)>0:
image_size = [int(x) for x in str_image_size.split(',')]
if len(image_size)==1:
image_size = [image_size[0], image_size[0]]
assert len(image_size)==2
assert image_size[0]==112
assert image_size[0]==112 or image_size[1]==96
if landmark is not None:
assert len(image_size)==2
src = np.array([
[30.2946, 51.6963],
[65.5318, 51.5014],
[48.0252, 71.7366],
[33.5493, 92.3655],
[62.7299, 92.2041] ], dtype=np.float32 )
if image_size[1]==112:
src[:,0] += 8.0
dst = landmark.astype(np.float32)

tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2,:]
#M = cv2.estimateRigidTransform( dst.reshape(1,5,2), src.reshape(1,5,2), False)

if M is None:
if bbox is None: #use center crop
det = np.zeros(4, dtype=np.int32)
det[0] = int(img.shape[1]*0.0625)
det[1] = int(img.shape[0]*0.0625)
det[2] = img.shape[1] - det[0]
det[3] = img.shape[0] - det[1]
from typing import List, Union, Optional

def read_image(img_path: str, mode: str = 'rgb', layout: str = 'HWC') -> np.ndarray:
"""
Read and process the image based on the specified mode and layout.

:param img_path: Path to the image file.
:param mode: Image mode ('rgb' or 'gray').
:param layout: Image layout ('HWC' or 'CHW').
:return: Processed image.
"""
if mode == 'gray':
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
else:
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
if mode == 'rgb':
img = img[..., ::-1]
if layout == 'CHW':
img = np.transpose(img, (2, 0, 1))
return img

def preprocess(img: Union[str, np.ndarray], bbox: Optional[List[int]] = None, landmark: Optional[np.ndarray] = None,
image_size: Optional[List[int]] = None, margin: int = 44, mode: str = 'rgb', layout: str = 'HWC') -> np.ndarray:
"""
Preprocess the image by resizing and aligning based on the bounding box and landmark.

:param img: Input image or image path.
:param bbox: Bounding box coordinates.
:param landmark: Landmark points.
:param image_size: Desired image size.
:param margin: Margin for bounding box.
:param mode: Image mode ('rgb' or 'gray').
:param layout: Image layout ('HWC' or 'CHW').
:return: Preprocessed image.
"""
if isinstance(img, str):
img = read_image(img, mode=mode, layout=layout)

image_size = image_size or [112, 112]

if landmark is not None:
src = np.array([
[30.2946, 51.6963],
[65.5318, 51.5014],
[48.0252, 71.7366],
[33.5493, 92.3655],
[62.7299, 92.2041]
], dtype=np.float32)

if image_size[1] == 112:
src[:, 0] += 8.0

dst = landmark.astype(np.float32)
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2, :]
else:
M = None

if M is None:
if bbox is None:
det = np.zeros(4, dtype=np.int32)
det[0] = int(img.shape[1] * 0.0625)
det[1] = int(img.shape[0] * 0.0625)
det[2] = img.shape[1] - det[0]
det[3] = img.shape[0] - det[1]
else:
det = bbox

bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2, img.shape[1])
bb[3] = np.minimum(det[3] + margin / 2, img.shape[0])
ret = img[bb[1]:bb[3], bb[0]:bb[2], :]
if len(image_size) > 0:
ret = cv2.resize(ret, (image_size[1], image_size[0]))
return ret
else:
warped = cv2.warpAffine(img, M, (image_size[1], image_size[0]), borderValue=0.0)
return warped

def preprocess_3pt(img: Union[str, np.ndarray], bbox: Optional[List[int]] = None, landmark: Optional[np.ndarray] = None,
image_size: Optional[List[int]] = None, margin: int = 44, mode: str = 'rgb', layout: str = 'HWC') -> np.ndarray:
"""
Preprocess the image by resizing and aligning based on the bounding box and landmark using 3 points.

:param img: Input image or image path.
:param bbox: Bounding box coordinates.
:param landmark: Landmark points.
:param image_size: Desired image size.
:param margin: Margin for bounding box.
:param mode: Image mode ('rgb' or 'gray').
:param layout: Image layout ('HWC' or 'CHW').
:return: Preprocessed image.
"""
if isinstance(img, str):
img = read_image(img, mode=mode, layout=layout)

image_size = image_size or [112, 112]

if landmark is not None:
src = np.array([
[30.2946, 51.6963],
[65.5318, 51.5014],
[48.0252, 71.7366]
], dtype=np.float32)

if image_size[1] == 112:
src[:, 0] += 8.0

dst = landmark.astype(np.float32)
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2, :]
else:
det = bbox
margin = kwargs.get('margin', 44)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0]-margin/2, 0)
bb[1] = np.maximum(det[1]-margin/2, 0)
bb[2] = np.minimum(det[2]+margin/2, img.shape[1])
bb[3] = np.minimum(det[3]+margin/2, img.shape[0])
ret = img[bb[1]:bb[3],bb[0]:bb[2],:]
if len(image_size)>0:
ret = cv2.resize(ret, (image_size[1], image_size[0]))
return ret
else: #do align using landmark
assert len(image_size)==2

#src = src[0:3,:]
#dst = dst[0:3,:]


#print(src.shape, dst.shape)
#print(src)
#print(dst)
#print(M)
warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)

#tform3 = trans.ProjectiveTransform()
#tform3.estimate(src, dst)
#warped = trans.warp(img, tform3, output_shape=_shape)
return warped


def preprocess_3pt(img, bbox=None, landmark=None, **kwargs):
if isinstance(img, str):
img = read_image(img, **kwargs)
M = None
image_size = []
str_image_size = kwargs.get('image_size', '')
if len(str_image_size)>0:
image_size = [int(x) for x in str_image_size.split(',')]
if len(image_size)==1:
image_size = [image_size[0], image_size[0]]
assert len(image_size)==2
assert image_size[0]==112
assert image_size[0]==112 or image_size[1]==96
if landmark is not None:
assert len(image_size)==2
src = np.array([
[30.2946, 51.6963],
[65.5318, 51.5014],
[48.0252, 71.7366]], dtype=np.float32 )
if image_size[1]==112:
src[:,0] += 8.0
dst = landmark.astype(np.float32)

tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2,:]
#M = cv2.estimateRigidTransform( dst.reshape(1,5,2), src.reshape(1,5,2), False)

if M is None:
if bbox is None: #use center crop
det = np.zeros(4, dtype=np.int32)
det[0] = int(img.shape[1]*0.0625)
det[1] = int(img.shape[0]*0.0625)
det[2] = img.shape[1] - det[0]
det[3] = img.shape[0] - det[1]
M = None

if M is None:
if bbox is None:
det = np.zeros(4, dtype=np.int32)
det[0] = int(img.shape[1] * 0.0625)
det[1] = int(img.shape[0] * 0.0625)
det[2] = img.shape[1] - det[0]
det[3] = img.shape[0] - det[1]
else:
det = bbox

bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2, img.shape[1])
bb[3] = np.minimum(det[3] + margin / 2, img.shape[0])
ret = img[bb[1]:bb[3], bb[0]:bb[2], :]
if len(image_size) > 0:
ret = cv2.resize(ret, (image_size[1], image_size[0]))
return ret
else:
det = bbox
margin = kwargs.get('margin', 44)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0]-margin/2, 0)
bb[1] = np.maximum(det[1]-margin/2, 0)
bb[2] = np.minimum(det[2]+margin/2, img.shape[1])
bb[3] = np.minimum(det[3]+margin/2, img.shape[0])
ret = img[bb[1]:bb[3],bb[0]:bb[2],:]
if len(image_size)>0:
ret = cv2.resize(ret, (image_size[1], image_size[0]))
return ret
else: #do align using landmark
assert len(image_size)==2

#src = src[0:3,:]
#dst = dst[0:3,:]


#print(src.shape, dst.shape)
#print(src)
#print(dst)
#print(M)
warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)

#tform3 = trans.ProjectiveTransform()
#tform3.estimate(src, dst)
#warped = trans.warp(img, tform3, output_shape=_shape)
return warped
warped = cv2.warpAffine(img, M, (image_size[1], image_size[0]), borderValue=0.0)
return warped