from typing import Any, List import cv2 import insightface import threading import numpy as np import modules.globals import logging import modules.processors.frame.core from modules.core import update_status from modules.face_analyser import get_one_face, get_many_faces, default_source_face from modules.typing import Face, Frame from modules.utilities import ( conditional_download, is_image, is_video, ) from modules.cluster_analysis import find_closest_centroid import os FACE_SWAPPER = None THREAD_LOCK = threading.Lock() NAME = "DLC.FACE-SWAPPER" abs_dir = os.path.dirname(os.path.abspath(__file__)) models_dir = os.path.join( os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models" ) def pre_check() -> bool: download_directory_path = abs_dir conditional_download( download_directory_path, [ "https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx" ], ) return True def pre_start() -> bool: if not modules.globals.map_faces and not is_image(modules.globals.source_path): update_status("Select an image for source path.", NAME) return False elif not modules.globals.map_faces and not get_one_face( cv2.imread(modules.globals.source_path) ): update_status("No face in source path detected.", NAME) return False if not is_image(modules.globals.target_path) and not is_video( modules.globals.target_path ): update_status("Select an image or video for target path.", NAME) return False return True def get_face_swapper() -> Any: global FACE_SWAPPER with THREAD_LOCK: if FACE_SWAPPER is None: model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx") FACE_SWAPPER = insightface.model_zoo.get_model( model_path, providers=modules.globals.execution_providers ) return FACE_SWAPPER def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame: face_swapper = get_face_swapper() # Apply the face swap swapped_frame_result = face_swapper.get( # Renamed to avoid confusion temp_frame, target_face, source_face, paste_back=True ) # Ensure swapped_frame_result is not None and is a valid image if swapped_frame_result is None or not isinstance(swapped_frame_result, np.ndarray): logging.error("Face swap operation failed or returned invalid result.") return temp_frame # Return original frame if swap failed # Color Correction if modules.globals.color_correction: # Get the bounding box of the target face to apply color correction # more accurately to the swapped region. # The target_face object should have bbox attribute (x1, y1, x2, y2) if hasattr(target_face, 'bbox'): x1, y1, x2, y2 = target_face.bbox.astype(int) # Ensure coordinates are within frame bounds x1, y1 = max(0, x1), max(0, y1) x2, y2 = min(swapped_frame_result.shape[1], x2), min(swapped_frame_result.shape[0], y2) if x1 < x2 and y1 < y2: swapped_face_region = swapped_frame_result[y1:y2, x1:x2] target_face_region_original = temp_frame[y1:y2, x1:x2] if swapped_face_region.size > 0 and target_face_region_original.size > 0: corrected_swapped_face_region = apply_histogram_matching_color_correction(swapped_face_region, target_face_region_original) swapped_frame_result[y1:y2, x1:x2] = corrected_swapped_face_region else: # Fallback to full frame color correction if regions are invalid swapped_frame_result = apply_histogram_matching_color_correction(swapped_frame_result, temp_frame) else: # Fallback to full frame color correction if bbox is invalid swapped_frame_result = apply_histogram_matching_color_correction(swapped_frame_result, temp_frame) else: # Fallback to full frame color correction if no bbox swapped_frame_result = apply_histogram_matching_color_correction(swapped_frame_result, temp_frame) if modules.globals.mouth_mask: # Create a mask for the target face face_mask = create_face_mask(target_face, temp_frame) # Create the mouth mask mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = ( create_lower_mouth_mask(target_face, temp_frame) ) # Apply the mouth area swapped_frame_result = apply_mouth_area( swapped_frame_result, mouth_cutout, mouth_box, face_mask, lower_lip_polygon ) if modules.globals.show_mouth_mask_box: mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon) swapped_frame_result = draw_mouth_mask_visualization( swapped_frame_result, target_face, mouth_mask_data ) # Poisson Blending if modules.globals.use_poisson_blending and hasattr(target_face, 'bbox'): # Create a mask for the swapped face region for Poisson blending # This mask should cover the area of the swapped face. # We can use the target_face.bbox and perhaps expand it slightly, # or use a more precise mask from face parsing if available. # For simplicity, using a slightly feathered convex hull of landmarks. face_mask_for_blending = np.zeros(temp_frame.shape[:2], dtype=np.uint8) # Prioritize using the bounding box for a tighter mask if hasattr(target_face, 'bbox'): x1, y1, x2, y2 = target_face.bbox.astype(int) # Ensure coordinates are within frame bounds x1_b, y1_b = max(0, x1), max(0, y1) # Use different var names to avoid conflict with center calculation x2_b, y2_b = min(temp_frame.shape[1], x2), min(temp_frame.shape[0], y2) # Create a rectangular mask based on the bounding box if x1_b < x2_b and y1_b < y2_b: face_mask_for_blending[y1_b:y2_b, x1_b:x2_b] = 255 else: logging.warning("Invalid bounding box for Poisson mask. Attempting landmark-based mask.") # Fallback to landmark-based convex hull if bbox is invalid landmarks = target_face.landmark_2d_106 if hasattr(target_face, 'landmark_2d_106') else None if landmarks is not None and len(landmarks) > 0: try: hull_points = cv2.convexHull(landmarks.astype(np.int32)) cv2.fillConvexPoly(face_mask_for_blending, hull_points, 255) except Exception as e: logging.error(f"Could not form convex hull for Poisson mask from landmarks: {e}. Blending will be skipped.") else: logging.error("No valid bbox or landmarks for Poisson mask. Blending will be skipped.") else: # Fallback to landmark-based convex hull if no bbox attribute landmarks = target_face.landmark_2d_106 if hasattr(target_face, 'landmark_2d_106') else None if landmarks is not None and len(landmarks) > 0: try: hull_points = cv2.convexHull(landmarks.astype(np.int32)) cv2.fillConvexPoly(face_mask_for_blending, hull_points, 255) except Exception as e: logging.error(f"Could not form convex hull for Poisson mask from landmarks (no bbox): {e}. Blending will be skipped.") else: logging.error("No bbox or landmarks available for Poisson mask. Blending will be skipped.") # Subtract ear regions if preserve_target_ears is enabled if modules.globals.preserve_target_ears and np.any(face_mask_for_blending > 0): mfx1, mfy1, mfx2, mfy2 = target_face.bbox.astype(int) mfw = mfx2 - mfx1 mfh = mfy2 - mfy1 ear_w = int(mfw * modules.globals.ear_width_ratio) ear_h = int(mfh * modules.globals.ear_height_ratio) ear_v_offset = int(mfh * modules.globals.ear_vertical_offset_ratio) ear_overlap = int(mfw * modules.globals.ear_horizontal_overlap_ratio) # Person's Right Ear (image left side of face bbox) # This region in face_mask_for_blending will be set to 0 rex1 = max(0, mfx1 - ear_w + ear_overlap) rey1 = max(0, mfy1 + ear_v_offset) rex2 = min(temp_frame.shape[1], mfx1 + ear_overlap) # Extends slightly into face bbox for smoother transition rey2 = min(temp_frame.shape[0], rey1 + ear_h) if rex1 < rex2 and rey1 < rey2: cv2.rectangle(face_mask_for_blending, (rex1, rey1), (rex2, rey2), 0, -1) # Person's Left Ear (image right side of face bbox) lex1 = max(0, mfx2 - ear_overlap) ley1 = max(0, mfy1 + ear_v_offset) lex2 = min(temp_frame.shape[1], mfx2 + ear_w - ear_overlap) ley2 = min(temp_frame.shape[0], ley1 + ear_h) if lex1 < lex2 and ley1 < ley2: cv2.rectangle(face_mask_for_blending, (lex1, ley1), (lex2, ley2), 0, -1) # Feather the mask to smooth edges for Poisson blending if np.any(face_mask_for_blending > 0): # Only feather if there's a mask feather_amount = modules.globals.poisson_blending_feather_amount if feather_amount > 0: # Ensure kernel size is odd kernel_size = 2 * feather_amount + 1 face_mask_for_blending = cv2.GaussianBlur(face_mask_for_blending, (kernel_size, kernel_size), 0) # Calculate the center of the target face bbox for seamlessClone if hasattr(target_face, 'bbox'): x1, y1, x2, y2 = target_face.bbox.astype(int) center_x = (x1 + x2) // 2 center_y = (y1 + y2) // 2 # Ensure center is within frame dimensions center_x = np.clip(center_x, 0, temp_frame.shape[1] -1) center_y = np.clip(center_y, 0, temp_frame.shape[0] -1) center = (center_x, center_y) # Apply Poisson blending # swapped_frame_result is the source, temp_frame is the destination if np.any(face_mask_for_blending > 0): # Proceed only if mask is not empty try: # Ensure swapped_frame_result and temp_frame are 8-bit 3-channel images if swapped_frame_result.dtype != np.uint8: swapped_frame_result = np.clip(swapped_frame_result, 0, 255).astype(np.uint8) if temp_frame.dtype != np.uint8: temp_frame_uint8 = np.clip(temp_frame, 0, 255).astype(np.uint8) else: temp_frame_uint8 = temp_frame swapped_frame_result = cv2.seamlessClone(swapped_frame_result, temp_frame_uint8, face_mask_for_blending, center, cv2.NORMAL_CLONE) except cv2.error as e: logging.error(f"Error during Poisson blending: {e}") # Fallback to non-blended result if seamlessClone fails pass # swapped_frame_result remains as is else: logging.warning("Poisson blending mask is empty. Skipping Poisson blending.") return swapped_frame_result def process_frame(source_face: Face, temp_frame: Frame) -> Frame: # The color_correction logic was moved into swap_face. # The initial temp_frame modification `cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)` # was incorrect as it changes the color space of the whole frame before processing, # which is not what we want for color correction of the swapped part. # Histogram matching is now done BGR to BGR. if modules.globals.many_faces: many_faces = get_many_faces(temp_frame) if many_faces: for target_face in many_faces: if source_face and target_face: temp_frame = swap_face(source_face, target_face, temp_frame) else: print("Face detection failed for target/source.") else: target_face = get_one_face(temp_frame) if target_face and source_face: temp_frame = swap_face(source_face, target_face, temp_frame) else: logging.error("Face detection failed for target or source.") return temp_frame def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame: if is_image(modules.globals.target_path): if modules.globals.many_faces: source_face = default_source_face() for map in modules.globals.source_target_map: target_face = map["target"]["face"] temp_frame = swap_face(source_face, target_face, temp_frame) elif not modules.globals.many_faces: for map in modules.globals.source_target_map: if "source" in map: source_face = map["source"]["face"] target_face = map["target"]["face"] temp_frame = swap_face(source_face, target_face, temp_frame) elif is_video(modules.globals.target_path): if modules.globals.many_faces: source_face = default_source_face() for map in modules.globals.source_target_map: target_frame = [ f for f in map["target_faces_in_frame"] if f["location"] == temp_frame_path ] for frame in target_frame: for target_face in frame["faces"]: temp_frame = swap_face(source_face, target_face, temp_frame) elif not modules.globals.many_faces: for map in modules.globals.source_target_map: if "source" in map: target_frame = [ f for f in map["target_faces_in_frame"] if f["location"] == temp_frame_path ] source_face = map["source"]["face"] for frame in target_frame: for target_face in frame["faces"]: temp_frame = swap_face(source_face, target_face, temp_frame) else: detected_faces = get_many_faces(temp_frame) if modules.globals.many_faces: if detected_faces: source_face = default_source_face() for target_face in detected_faces: temp_frame = swap_face(source_face, target_face, temp_frame) elif not modules.globals.many_faces: if detected_faces: if len(detected_faces) <= len( modules.globals.simple_map["target_embeddings"] ): for detected_face in detected_faces: closest_centroid_index, _ = find_closest_centroid( modules.globals.simple_map["target_embeddings"], detected_face.normed_embedding, ) temp_frame = swap_face( modules.globals.simple_map["source_faces"][ closest_centroid_index ], detected_face, temp_frame, ) else: detected_faces_centroids = [] for face in detected_faces: detected_faces_centroids.append(face.normed_embedding) i = 0 for target_embedding in modules.globals.simple_map[ "target_embeddings" ]: closest_centroid_index, _ = find_closest_centroid( detected_faces_centroids, target_embedding ) temp_frame = swap_face( modules.globals.simple_map["source_faces"][i], detected_faces[closest_centroid_index], temp_frame, ) i += 1 return temp_frame def process_frames( source_path: str, temp_frame_paths: List[str], progress: Any = None ) -> None: if not modules.globals.map_faces: source_face = get_one_face(cv2.imread(source_path)) for temp_frame_path in temp_frame_paths: temp_frame = cv2.imread(temp_frame_path) try: result = process_frame(source_face, temp_frame) cv2.imwrite(temp_frame_path, result) except Exception as exception: print(exception) pass if progress: progress.update(1) else: for temp_frame_path in temp_frame_paths: temp_frame = cv2.imread(temp_frame_path) try: result = process_frame_v2(temp_frame, temp_frame_path) cv2.imwrite(temp_frame_path, result) except Exception as exception: print(exception) pass if progress: progress.update(1) def process_image(source_path: str, target_path: str, output_path: str) -> None: if not modules.globals.map_faces: source_face = get_one_face(cv2.imread(source_path)) target_frame = cv2.imread(target_path) result = process_frame(source_face, target_frame) cv2.imwrite(output_path, result) else: if modules.globals.many_faces: update_status( "Many faces enabled. Using first source image. Progressing...", NAME ) target_frame = cv2.imread(output_path) result = process_frame_v2(target_frame) cv2.imwrite(output_path, result) def process_video(source_path: str, temp_frame_paths: List[str]) -> None: if modules.globals.map_faces and modules.globals.many_faces: update_status( "Many faces enabled. Using first source image. Progressing...", NAME ) modules.processors.frame.core.process_video( source_path, temp_frame_paths, process_frames ) def create_lower_mouth_mask( face: Face, frame: Frame ) -> (np.ndarray, np.ndarray, tuple, np.ndarray): mask = np.zeros(frame.shape[:2], dtype=np.uint8) mouth_cutout = None landmarks = face.landmark_2d_106 if landmarks is not None: # 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 lower_lip_order = [ 65, 66, 62, 70, 69, 18, 19, 20, 21, 22, 23, 24, 0, 8, 7, 6, 5, 4, 3, 2, 65, ] lower_lip_landmarks = landmarks[lower_lip_order].astype( np.float32 ) # Use float for precise calculations # Calculate the center of the landmarks center = np.mean(lower_lip_landmarks, axis=0) # Expand the landmarks outward expansion_factor = ( 1 + modules.globals.mask_down_size ) # Adjust this for more or less expansion expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center # Extend the top lip part toplip_indices = [ 20, 0, 1, 2, 3, 4, 5, ] # Indices for landmarks 2, 65, 66, 62, 70, 69, 18 toplip_extension = ( modules.globals.mask_size * 0.5 ) # Adjust this factor to control the extension for idx in toplip_indices: direction = expanded_landmarks[idx] - center direction = direction / np.linalg.norm(direction) expanded_landmarks[idx] += direction * toplip_extension # Extend the bottom part (chin area) chin_indices = [ 11, 12, 13, 14, 15, 16, ] # Indices for landmarks 21, 22, 23, 24, 0, 8 chin_extension = 2 * 0.2 # Adjust this factor to control the extension for idx in chin_indices: expanded_landmarks[idx][1] += ( expanded_landmarks[idx][1] - center[1] ) * chin_extension # Convert back to integer coordinates expanded_landmarks = expanded_landmarks.astype(np.int32) # Calculate bounding box for the expanded lower mouth min_x, min_y = np.min(expanded_landmarks, axis=0) max_x, max_y = np.max(expanded_landmarks, axis=0) # Add some padding to the bounding box padding = int((max_x - min_x) * 0.1) # 10% padding min_x = max(0, min_x - padding) min_y = max(0, min_y - padding) max_x = min(frame.shape[1], max_x + padding) max_y = min(frame.shape[0], max_y + padding) # Ensure the bounding box dimensions are valid if max_x <= min_x or max_y <= min_y: if (max_x - min_x) <= 1: max_x = min_x + 1 if (max_y - min_y) <= 1: max_y = min_y + 1 # Create the mask mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8) cv2.fillPoly(mask_roi, [expanded_landmarks - [min_x, min_y]], 255) # Apply Gaussian blur to soften the mask edges mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5) # Place the mask ROI in the full-sized mask mask[min_y:max_y, min_x:max_x] = mask_roi # Extract the masked area from the frame mouth_cutout = frame[min_y:max_y, min_x:max_x].copy() # Return the expanded lower lip polygon in original frame coordinates lower_lip_polygon = expanded_landmarks return mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon def draw_mouth_mask_visualization( frame: Frame, face: Face, mouth_mask_data: tuple ) -> Frame: landmarks = face.landmark_2d_106 if landmarks is not None and mouth_mask_data is not None: mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = ( mouth_mask_data ) vis_frame = frame.copy() # Ensure coordinates are within frame bounds height, width = vis_frame.shape[:2] min_x, min_y = max(0, min_x), max(0, min_y) max_x, max_y = min(width, max_x), min(height, max_y) # Adjust mask to match the region size mask_region = mask[0 : max_y - min_y, 0 : max_x - min_x] # Remove the color mask overlay # color_mask = cv2.applyColorMap((mask_region * 255).astype(np.uint8), cv2.COLORMAP_JET) # Ensure shapes match before blending vis_region = vis_frame[min_y:max_y, min_x:max_x] # Remove blending with color_mask # if vis_region.shape[:2] == color_mask.shape[:2]: # blended = cv2.addWeighted(vis_region, 0.7, color_mask, 0.3, 0) # vis_frame[min_y:max_y, min_x:max_x] = blended # Draw the lower lip polygon cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2) # Remove the red box # cv2.rectangle(vis_frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 2) # Visualize the feathered mask feather_amount = max( 1, min( 30, (max_x - min_x) // modules.globals.mask_feather_ratio, (max_y - min_y) // modules.globals.mask_feather_ratio, ), ) # Ensure kernel size is odd kernel_size = 2 * feather_amount + 1 feathered_mask = cv2.GaussianBlur( mask_region.astype(float), (kernel_size, kernel_size), 0 ) feathered_mask = (feathered_mask / feathered_mask.max() * 255).astype(np.uint8) # Remove the feathered mask color overlay # color_feathered_mask = cv2.applyColorMap(feathered_mask, cv2.COLORMAP_VIRIDIS) # Ensure shapes match before blending feathered mask # if vis_region.shape == color_feathered_mask.shape: # blended_feathered = cv2.addWeighted(vis_region, 0.7, color_feathered_mask, 0.3, 0) # vis_frame[min_y:max_y, min_x:max_x] = blended_feathered # Add labels cv2.putText( vis_frame, "Lower Mouth Mask", (min_x, min_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, ) cv2.putText( vis_frame, "Feathered Mask", (min_x, max_y + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, ) return vis_frame return frame def apply_mouth_area( frame: np.ndarray, mouth_cutout: np.ndarray, mouth_box: tuple, face_mask: np.ndarray, mouth_polygon: np.ndarray, ) -> np.ndarray: min_x, min_y, max_x, max_y = mouth_box box_width = max_x - min_x box_height = max_y - min_y if ( mouth_cutout is None or box_width is None or box_height is None or face_mask is None or mouth_polygon is None ): return frame try: resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height)) roi = frame[min_y:max_y, min_x:max_x] if roi.shape != resized_mouth_cutout.shape: resized_mouth_cutout = cv2.resize( resized_mouth_cutout, (roi.shape[1], roi.shape[0]) ) color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi) # Use the provided mouth polygon to create the mask polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8) adjusted_polygon = mouth_polygon - [min_x, min_y] cv2.fillPoly(polygon_mask, [adjusted_polygon], 255) # Apply feathering to the polygon mask feather_amount = min( 30, box_width // modules.globals.mask_feather_ratio, box_height // modules.globals.mask_feather_ratio, ) feathered_mask = cv2.GaussianBlur( polygon_mask.astype(float), (0, 0), feather_amount ) feathered_mask = feathered_mask / feathered_mask.max() face_mask_roi = face_mask[min_y:max_y, min_x:max_x] combined_mask = feathered_mask * (face_mask_roi / 255.0) combined_mask = combined_mask[:, :, np.newaxis] blended = ( color_corrected_mouth * combined_mask + roi * (1 - combined_mask) ).astype(np.uint8) # Apply face mask to blended result face_mask_3channel = ( np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0 ) final_blend = blended * face_mask_3channel + roi * (1 - face_mask_3channel) frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8) except Exception as e: pass return frame def create_face_mask(face: Face, frame: Frame) -> np.ndarray: mask = np.zeros(frame.shape[:2], dtype=np.uint8) landmarks = face.landmark_2d_106 if landmarks is not None: # Convert landmarks to int32 landmarks = landmarks.astype(np.int32) # Extract facial features right_side_face = landmarks[0:16] left_side_face = landmarks[17:32] right_eye = landmarks[33:42] right_eye_brow = landmarks[43:51] left_eye = landmarks[87:96] left_eye_brow = landmarks[97:105] # Calculate forehead extension right_eyebrow_top = np.min(right_eye_brow[:, 1]) left_eyebrow_top = np.min(left_eye_brow[:, 1]) eyebrow_top = min(right_eyebrow_top, left_eyebrow_top) face_top = np.min([right_side_face[0, 1], left_side_face[-1, 1]]) forehead_height = face_top - eyebrow_top extended_forehead_height = int(forehead_height * 5.0) # Extend by 50% # Create forehead points forehead_left = right_side_face[0].copy() forehead_right = left_side_face[-1].copy() forehead_left[1] -= extended_forehead_height forehead_right[1] -= extended_forehead_height # Combine all points to create the face outline face_outline = np.vstack( [ [forehead_left], right_side_face, left_side_face[ ::-1 ], # Reverse left side to create a continuous outline [forehead_right], ] ) # Calculate padding padding = int( np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05 ) # 5% of face width # Create a slightly larger convex hull for padding hull = cv2.convexHull(face_outline) hull_padded = [] for point in hull: x, y = point[0] center = np.mean(face_outline, axis=0) direction = np.array([x, y]) - center direction = direction / np.linalg.norm(direction) padded_point = np.array([x, y]) + direction * padding hull_padded.append(padded_point) hull_padded = np.array(hull_padded, dtype=np.int32) # Fill the padded convex hull cv2.fillConvexPoly(mask, hull_padded, 255) # Smooth the mask edges mask = cv2.GaussianBlur(mask, (5, 5), 3) return mask def apply_color_transfer(source, target): """ Apply color transfer from target to source image """ source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32") target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32") source_mean, source_std = cv2.meanStdDev(source) target_mean, target_std = cv2.meanStdDev(target) # Reshape mean and std to be broadcastable source_mean = source_mean.reshape(1, 1, 3) source_std = source_std.reshape(1, 1, 3) target_mean = target_mean.reshape(1, 1, 3) target_std = target_std.reshape(1, 1, 3) # Perform the color transfer source = (source - source_mean) * (target_std / source_std) + target_mean return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR) def apply_histogram_matching_color_correction(source_img: Frame, target_img: Frame) -> Frame: """ Applies color correction to the source image to match the target image's color distribution using histogram matching on each color channel. """ corrected_img = np.zeros_like(source_img) for i in range(source_img.shape[2]): # Iterate over color channels (B, G, R) source_hist, _ = np.histogram(source_img[:, :, i].flatten(), 256, [0, 256]) target_hist, _ = np.histogram(target_img[:, :, i].flatten(), 256, [0, 256]) # Compute cumulative distribution functions (CDFs) source_cdf = source_hist.cumsum() source_cdf_normalized = source_cdf * source_hist.max() / source_cdf.max() # Normalize target_cdf = target_hist.cumsum() target_cdf_normalized = target_cdf * target_hist.max() / target_cdf.max() # Normalize # Create lookup table lookup_table = np.zeros(256, 'uint8') gj = 0 for gi in range(256): while gj < 256 and target_cdf_normalized[gj] < source_cdf_normalized[gi]: gj += 1 if gj == 256: # If we reach end of target_cdf, map remaining to max value lookup_table[gi] = 255 else: lookup_table[gi] = gj corrected_img[:, :, i] = cv2.LUT(source_img[:, :, i], lookup_table) return corrected_img