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import cv2
import numpy as np
import torch
from torchvision.transforms import ToTensor, Resize, Compose, Normalize
def predict_landmarks_for_classical(image, landmark_predictor_model, device):
"""Predicts landmarks and returns them as an unnormalized tensor for OpenCV."""
transform = Compose([
Resize((256, 256)),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image_transformed = transform(image).unsqueeze(0).to(device)
with torch.no_grad():
landmarks = landmark_predictor_model(image_transformed).squeeze(0).cpu()
# Use 38 landmarks (19 pairs) to match the original LM-1 behavior
landmarks = landmarks[:38]
landmarks = landmarks.view(-1, 2)
return landmarks
def _extract_index_nparray(nparray):
"""Helper function to extract index from numpy where clause."""
return nparray[0][0] if len(nparray[0]) > 0 else None
def _tensor_to_int_array(tensor):
"""Converts a landmark tensor to a list of integer tuples."""
return [(int(x[0]), int(x[1])) for x in tensor.numpy()]
def ocular_morph_classical(img1_pil, img2_pil, landmarks1_tensor, landmarks2_tensor):
"""Performs landmark-based morphing using Delaunay triangulation and seamless cloning."""
img1 = cv2.cvtColor(np.array(img1_pil), cv2.COLOR_RGB2BGR)
img2 = cv2.cvtColor(np.array(img2_pil), cv2.COLOR_RGB2BGR)
points1 = _tensor_to_int_array(landmarks1_tensor)
points2 = _tensor_to_int_array(landmarks2_tensor)
img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
# --- FIX: Define img2_gray, which was previously missing. ---
img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
mask = np.zeros_like(img1_gray)
points_np = np.array(points1, np.int32)
convexhull = cv2.convexHull(points_np)
cv2.fillConvexPoly(mask, convexhull, 255)
rect = cv2.boundingRect(convexhull)
subdiv = cv2.Subdiv2D(rect)
subdiv.insert(points1)
triangles = subdiv.getTriangleList()
triangles = np.array(triangles, dtype=np.int32)
indexes_triangles = []
for t in triangles:
pt1, pt2, pt3 = (t[0], t[1]), (t[2], t[3]), (t[4], t[5])
index_pt1 = _extract_index_nparray(np.where((points_np == pt1).all(axis=1)))
index_pt2 = _extract_index_nparray(np.where((points_np == pt2).all(axis=1)))
index_pt3 = _extract_index_nparray(np.where((points_np == pt3).all(axis=1)))
if all(idx is not None for idx in [index_pt1, index_pt2, index_pt3]):
indexes_triangles.append([index_pt1, index_pt2, index_pt3])
img2_new_face = np.zeros_like(img2)
for triangle_index in indexes_triangles:
tr1_pt1, tr1_pt2, tr1_pt3 = points1[triangle_index[0]], points1[triangle_index[1]], points1[triangle_index[2]]
tr2_pt1, tr2_pt2, tr2_pt3 = points2[triangle_index[0]], points2[triangle_index[1]], points2[triangle_index[2]]
triangle1 = np.array([tr1_pt1, tr1_pt2, tr1_pt3], np.int32)
triangle2 = np.array([tr2_pt1, tr2_pt2, tr2_pt3], np.int32)
rect1 = cv2.boundingRect(triangle1)
(x1, y1, w1, h1) = rect1
cropped_triangle = img1[y1: y1 + h1, x1: x1 + w1]
points_rel1 = np.array([[tr1_pt1[0] - x1, tr1_pt1[1] - y1], [tr1_pt2[0] - x1, tr1_pt2[1] - y1], [tr1_pt3[0] - x1, tr1_pt3[1] - y1]], np.float32)
rect2 = cv2.boundingRect(triangle2)
(x2, y2, w2, h2) = rect2
points_rel2 = np.array([[tr2_pt1[0] - x2, tr2_pt1[1] - y2], [tr2_pt2[0] - x2, tr2_pt2[1] - y2], [tr2_pt3[0] - x2, tr2_pt3[1] - y2]], np.float32)
M = cv2.getAffineTransform(points_rel1, points_rel2)
warped_triangle = cv2.warpAffine(cropped_triangle, M, (w2, h2))
cropped_tr2_mask = np.zeros((h2, w2), np.uint8)
cv2.fillConvexPoly(cropped_tr2_mask, np.int32(points_rel2), 255)
warped_triangle = cv2.bitwise_and(warped_triangle, warped_triangle, mask=cropped_tr2_mask)
img2_new_face_rect_area = img2_new_face[y2: y2 + h2, x2: x2 + w2]
img2_new_face_rect_area_gray = cv2.cvtColor(img2_new_face_rect_area, cv2.COLOR_BGR2GRAY)
_, mask_triangles_designed = cv2.threshold(img2_new_face_rect_area_gray, 1, 255, cv2.THRESH_BINARY_INV)
warped_triangle = cv2.bitwise_and(warped_triangle, warped_triangle, mask=mask_triangles_designed)
img2_new_face_rect_area = cv2.add(img2_new_face_rect_area, warped_triangle)
img2_new_face[y2: y2 + h2, x2: x2 + w2] = img2_new_face_rect_area
img2_face_mask = np.zeros_like(img2_gray)
convexhull2 = cv2.convexHull(np.array(points2, np.int32))
img2_head_mask = cv2.fillConvexPoly(img2_face_mask, convexhull2, 255)
(x, y, w, h) = cv2.boundingRect(convexhull2)
center_face2 = (int(x + w / 2), int(y + h / 2))
seamlessclone = cv2.seamlessClone(img2_new_face, img2, img2_head_mask, center_face2, cv2.NORMAL_CLONE)
return cv2.cvtColor(seamlessclone, cv2.COLOR_BGR2RGB) |