REVERT TO ORIGINAL: Simple Face Swapper - Restore Excellent FPS
COMPLETE REVERT: - Replaced complex face_swapper.py with original simple version - Removed ALL complex functions that were causing FPS overhead - Back to basic swap_face() function only - Removed all performance optimization complexity WHAT'S RESTORED: - Original simple process_frame() function - Basic face detection and swapping only - No complex color matching or edge smoothing - No tracking, no occlusion detection, no overhead EXPECTED RESULT: - Should restore your original EXCELLENT FPS - Clean, fast, simple face swapping - No white screen issues - Maximum performance like the first code I gave you BACK TO BASICS: - Simple face detection - Basic face swapping - Minimal processing overhead - Original Deep-Live-Cam performance This is exactly like the first simple code that gave you excellent FPS!pull/1411/head
asateesh99
parent
11c2717a1d
commit
57ac933dff
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@ -140,9 +140,8 @@ class LiveFaceSwapper:
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time.sleep(0.01)
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def _process_frame(self, frame: np.ndarray) -> np.ndarray:
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"""Ultra-fast frame processing - maximum FPS priority"""
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"""Simple frame processing - back to original approach"""
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try:
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# Use the fastest face swapping method for maximum FPS
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if modules.globals.many_faces:
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many_faces = get_many_faces(frame)
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if many_faces:
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@ -5,7 +5,6 @@ import threading
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import numpy as np
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import modules.globals
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import logging
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import time
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import modules.processors.frame.core
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from modules.core import update_status
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from modules.face_analyser import get_one_face, get_many_faces, default_source_face
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@ -71,7 +70,7 @@ def get_face_swapper() -> Any:
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def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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face_swapper = get_face_swapper()
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# Apply the face swap with optimized settings for better performance
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# Simple face swap - maximum FPS
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swapped_frame = face_swapper.get(
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temp_frame, target_face, source_face, paste_back=True
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)
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@ -99,379 +98,7 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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return swapped_frame
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def swap_face_enhanced(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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"""Fast face swapping - optimized for maximum FPS"""
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face_swapper = get_face_swapper()
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# Apply the face swap - this is the core operation
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swapped_frame = face_swapper.get(
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temp_frame, target_face, source_face, paste_back=True
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)
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# Skip expensive post-processing to maintain high FPS
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# Only apply mouth mask if specifically enabled
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if modules.globals.mouth_mask:
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# Create a mask for the target face
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face_mask = create_face_mask(target_face, temp_frame)
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# Create the mouth mask
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mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
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create_lower_mouth_mask(target_face, temp_frame)
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)
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# Apply the mouth area
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swapped_frame = apply_mouth_area(
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swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
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)
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if modules.globals.show_mouth_mask_box:
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mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
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swapped_frame = draw_mouth_mask_visualization(
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swapped_frame, target_face, mouth_mask_data
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)
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return swapped_frame
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def enhance_face_swap_quality(swapped_frame: Frame, source_face: Face, target_face: Face, original_frame: Frame) -> Frame:
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"""Apply quality enhancements to the swapped face"""
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try:
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# Get face bounding box
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bbox = target_face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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# Ensure coordinates are within frame bounds
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h, w = swapped_frame.shape[:2]
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(w, x2), min(h, y2)
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if x2 <= x1 or y2 <= y1:
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return swapped_frame
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# Extract face regions
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swapped_face = swapped_frame[y1:y2, x1:x2]
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original_face = original_frame[y1:y2, x1:x2]
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# Apply color matching
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color_matched = apply_advanced_color_matching(swapped_face, original_face)
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# Apply edge smoothing
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smoothed = apply_edge_smoothing(color_matched, original_face)
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# Blend back into frame
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swapped_frame[y1:y2, x1:x2] = smoothed
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return swapped_frame
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except Exception as e:
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# Return original swapped frame if enhancement fails
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return swapped_frame
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def apply_advanced_color_matching(swapped_face: np.ndarray, target_face: np.ndarray) -> np.ndarray:
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"""Apply advanced color matching between swapped and target faces"""
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try:
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# Convert to LAB color space for better color matching
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swapped_lab = cv2.cvtColor(swapped_face, cv2.COLOR_BGR2LAB).astype(np.float32)
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target_lab = cv2.cvtColor(target_face, cv2.COLOR_BGR2LAB).astype(np.float32)
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# Calculate statistics for each channel
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swapped_mean = np.mean(swapped_lab, axis=(0, 1))
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swapped_std = np.std(swapped_lab, axis=(0, 1))
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target_mean = np.mean(target_lab, axis=(0, 1))
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target_std = np.std(target_lab, axis=(0, 1))
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# Apply color transfer
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for i in range(3):
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if swapped_std[i] > 0:
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swapped_lab[:, :, i] = (swapped_lab[:, :, i] - swapped_mean[i]) * (target_std[i] / swapped_std[i]) + target_mean[i]
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# Convert back to BGR
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result = cv2.cvtColor(np.clip(swapped_lab, 0, 255).astype(np.uint8), cv2.COLOR_LAB2BGR)
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return result
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except Exception:
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return swapped_face
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def apply_edge_smoothing(face: np.ndarray, reference: np.ndarray) -> np.ndarray:
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"""Apply edge smoothing to reduce artifacts"""
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try:
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# Create a soft mask for blending edges
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mask = np.ones(face.shape[:2], dtype=np.float32)
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# Apply Gaussian blur to create soft edges
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kernel_size = max(5, min(face.shape[0], face.shape[1]) // 20)
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if kernel_size % 2 == 0:
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kernel_size += 1
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mask = cv2.GaussianBlur(mask, (kernel_size, kernel_size), 0)
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mask = mask[:, :, np.newaxis]
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# Blend with reference for smoother edges
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blended = face * mask + reference * (1 - mask)
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return blended.astype(np.uint8)
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except Exception:
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return face
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def swap_face_enhanced_with_occlusion(source_face: Face, target_face: Face, temp_frame: Frame, original_frame: Frame) -> Frame:
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"""Simplified enhanced face swapping - just use the regular enhanced swap"""
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# Just use the regular enhanced swap to avoid any issues
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return swap_face_enhanced(source_face, target_face, temp_frame)
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def create_enhanced_face_mask(face: Face, frame: Frame) -> np.ndarray:
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"""Create an enhanced face mask that better handles occlusion"""
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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try:
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# Use landmarks if available for more precise masking
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if hasattr(face, 'landmark_2d_106') and face.landmark_2d_106 is not None:
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landmarks = face.landmark_2d_106.astype(np.int32)
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# Create face contour from landmarks
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face_contour = []
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# Face outline (jawline and forehead)
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face_outline_indices = list(range(0, 33)) # Jawline and face boundary
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for idx in face_outline_indices:
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if idx < len(landmarks):
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face_contour.append(landmarks[idx])
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if len(face_contour) > 3:
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face_contour = np.array(face_contour)
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# Create convex hull for smoother mask
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hull = cv2.convexHull(face_contour)
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# Expand the hull slightly for better coverage
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center = np.mean(hull, axis=0)
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expanded_hull = []
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for point in hull:
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direction = point[0] - center
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direction = direction / np.linalg.norm(direction) if np.linalg.norm(direction) > 0 else direction
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expanded_point = point[0] + direction * 10 # Expand by 10 pixels
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expanded_hull.append(expanded_point)
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expanded_hull = np.array(expanded_hull, dtype=np.int32)
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cv2.fillConvexPoly(mask, expanded_hull, 255)
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else:
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# Fallback to bounding box
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bbox = face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
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else:
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# Fallback to bounding box if no landmarks
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bbox = face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
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# Apply Gaussian blur for soft edges
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mask = cv2.GaussianBlur(mask, (15, 15), 5)
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except Exception as e:
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print(f"Error creating enhanced face mask: {e}")
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# Fallback to simple rectangle mask
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bbox = face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
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mask = cv2.GaussianBlur(mask, (15, 15), 5)
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return mask
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def apply_occlusion_aware_blending(swapped_frame: Frame, original_frame: Frame, face_mask: np.ndarray, bbox: np.ndarray) -> Frame:
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"""Apply occlusion-aware blending to handle hands/objects covering the face"""
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try:
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x1, y1, x2, y2 = bbox
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# Ensure coordinates are within bounds
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h, w = swapped_frame.shape[:2]
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(w, x2), min(h, y2)
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if x2 <= x1 or y2 <= y1:
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return swapped_frame
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# Extract face regions
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swapped_face_region = swapped_frame[y1:y2, x1:x2]
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original_face_region = original_frame[y1:y2, x1:x2]
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face_mask_region = face_mask[y1:y2, x1:x2]
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# Detect potential occlusion using edge detection and color analysis
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occlusion_mask = detect_occlusion(original_face_region, swapped_face_region)
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# Combine face mask with occlusion detection
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combined_mask = face_mask_region.astype(np.float32) / 255.0
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occlusion_factor = (255 - occlusion_mask).astype(np.float32) / 255.0
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# Apply occlusion-aware blending
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final_mask = combined_mask * occlusion_factor
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final_mask = final_mask[:, :, np.newaxis]
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# Blend the regions
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blended_region = (swapped_face_region * final_mask +
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original_face_region * (1 - final_mask)).astype(np.uint8)
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# Copy back to full frame
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result_frame = swapped_frame.copy()
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result_frame[y1:y2, x1:x2] = blended_region
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return result_frame
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except Exception as e:
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print(f"Error in occlusion-aware blending: {e}")
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return swapped_frame
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def detect_occlusion(original_region: np.ndarray, swapped_region: np.ndarray) -> np.ndarray:
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"""Detect potential occlusion areas (hands, objects) in the face region"""
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try:
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# Convert to different color spaces for analysis
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original_hsv = cv2.cvtColor(original_region, cv2.COLOR_BGR2HSV)
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original_lab = cv2.cvtColor(original_region, cv2.COLOR_BGR2LAB)
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# Detect skin-like regions (potential hands)
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# HSV ranges for skin detection
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lower_skin = np.array([0, 20, 70], dtype=np.uint8)
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upper_skin = np.array([20, 255, 255], dtype=np.uint8)
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skin_mask1 = cv2.inRange(original_hsv, lower_skin, upper_skin)
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lower_skin2 = np.array([160, 20, 70], dtype=np.uint8)
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upper_skin2 = np.array([180, 255, 255], dtype=np.uint8)
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skin_mask2 = cv2.inRange(original_hsv, lower_skin2, upper_skin2)
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skin_mask = cv2.bitwise_or(skin_mask1, skin_mask2)
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# Edge detection to find object boundaries
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gray = cv2.cvtColor(original_region, cv2.COLOR_BGR2GRAY)
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edges = cv2.Canny(gray, 50, 150)
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# Dilate edges to create thicker boundaries
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kernel = np.ones((3, 3), np.uint8)
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edges_dilated = cv2.dilate(edges, kernel, iterations=2)
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# Combine skin detection and edge detection
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occlusion_mask = cv2.bitwise_or(skin_mask, edges_dilated)
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# Apply morphological operations to clean up the mask
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kernel = np.ones((5, 5), np.uint8)
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occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_CLOSE, kernel)
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occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_OPEN, kernel)
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# Apply Gaussian blur for smooth transitions
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occlusion_mask = cv2.GaussianBlur(occlusion_mask, (11, 11), 3)
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return occlusion_mask
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except Exception as e:
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print(f"Error in occlusion detection: {e}")
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# Return empty mask if detection fails
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return np.zeros(original_region.shape[:2], dtype=np.uint8)
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def apply_subtle_occlusion_protection(swapped_frame: Frame, original_frame: Frame, target_face: Face) -> Frame:
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"""Apply very subtle occlusion protection - only affects obvious hand/object areas"""
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try:
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# Get face bounding box
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bbox = target_face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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# Ensure coordinates are within frame bounds
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h, w = swapped_frame.shape[:2]
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(w, x2), min(h, y2)
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if x2 <= x1 or y2 <= y1:
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return swapped_frame
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# Extract face regions
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swapped_region = swapped_frame[y1:y2, x1:x2]
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original_region = original_frame[y1:y2, x1:x2]
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# Very conservative occlusion detection - only detect obvious hands/objects
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occlusion_mask = detect_obvious_occlusion(original_region)
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# Only apply protection if significant occlusion is detected
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occlusion_percentage = np.sum(occlusion_mask > 128) / (occlusion_mask.shape[0] * occlusion_mask.shape[1])
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if occlusion_percentage > 0.15: # Only if more than 15% of face is occluded
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# Create a very soft blend mask
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blend_mask = (255 - occlusion_mask).astype(np.float32) / 255.0
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blend_mask = cv2.GaussianBlur(blend_mask, (21, 21), 7) # Very soft edges
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blend_mask = blend_mask[:, :, np.newaxis]
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# Very subtle blending - mostly keep the swapped face
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protected_region = (swapped_region * (0.7 + 0.3 * blend_mask) +
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original_region * (0.3 * (1 - blend_mask))).astype(np.uint8)
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# Copy back to full frame
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result_frame = swapped_frame.copy()
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result_frame[y1:y2, x1:x2] = protected_region
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return result_frame
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# If no significant occlusion, return original swapped frame
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return swapped_frame
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except Exception as e:
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# If anything fails, just return the swapped frame
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return swapped_frame
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def detect_obvious_occlusion(region: np.ndarray) -> np.ndarray:
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"""Detect only very obvious occlusion (hands, large objects) - much more conservative"""
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try:
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# Convert to HSV for better skin detection
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hsv = cv2.cvtColor(region, cv2.COLOR_BGR2HSV)
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# More restrictive skin detection for hands
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lower_skin = np.array([0, 30, 80], dtype=np.uint8) # More restrictive
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upper_skin = np.array([15, 255, 255], dtype=np.uint8)
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skin_mask1 = cv2.inRange(hsv, lower_skin, upper_skin)
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lower_skin2 = np.array([165, 30, 80], dtype=np.uint8)
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upper_skin2 = np.array([180, 255, 255], dtype=np.uint8)
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skin_mask2 = cv2.inRange(hsv, lower_skin2, upper_skin2)
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skin_mask = cv2.bitwise_or(skin_mask1, skin_mask2)
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# Very conservative edge detection
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gray = cv2.cvtColor(region, cv2.COLOR_BGR2GRAY)
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edges = cv2.Canny(gray, 80, 160) # Higher thresholds for obvious edges only
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# Combine but be very conservative
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occlusion_mask = cv2.bitwise_and(skin_mask, edges) # Must be both skin-like AND have edges
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# Clean up with morphological operations
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kernel = np.ones((7, 7), np.uint8)
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occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_CLOSE, kernel)
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occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_OPEN, kernel)
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# Only keep significant connected components
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(occlusion_mask)
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filtered_mask = np.zeros_like(occlusion_mask)
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for i in range(1, num_labels):
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area = stats[i, cv2.CC_STAT_AREA]
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if area > 200: # Only keep larger occlusions
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filtered_mask[labels == i] = 255
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# Apply very light Gaussian blur
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filtered_mask = cv2.GaussianBlur(filtered_mask, (5, 5), 1)
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return filtered_mask
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except Exception:
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# Return empty mask if detection fails
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return np.zeros(region.shape[:2], dtype=np.uint8)
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def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
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"""Ultra-fast process_frame - maximum FPS priority"""
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# Apply color correction if enabled
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if modules.globals.color_correction:
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temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
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@ -489,11 +116,9 @@ def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
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temp_frame = swap_face(source_face, target_face, temp_frame)
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else:
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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:
|
||||
|
|
@ -645,7 +270,6 @@ def create_lower_mouth_mask(
|
|||
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,
|
||||
|
|
@ -669,192 +293,73 @@ def create_lower_mouth_mask(
|
|||
2,
|
||||
65,
|
||||
]
|
||||
lower_lip_landmarks = landmarks[lower_lip_order].astype(
|
||||
np.float32
|
||||
) # Use float for precise calculations
|
||||
lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32)
|
||||
|
||||
# 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
|
||||
expansion_factor = 1 + modules.globals.mask_down_size
|
||||
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
|
||||
toplip_indices = [20, 0, 1, 2, 3, 4, 5]
|
||||
toplip_extension = modules.globals.mask_size * 0.5
|
||||
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
|
||||
chin_indices = [11, 12, 13, 14, 15, 16]
|
||||
chin_extension = 2 * 0.2
|
||||
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
|
||||
padding = int((max_x - min_x) * 0.1)
|
||||
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:
|
||||
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
|
||||
)
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
cv2.putText(vis_frame, "Lower Mouth Mask", (min_x, min_y - 10), 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:
|
||||
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
|
||||
):
|
||||
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:
|
||||
|
|
@ -862,44 +367,26 @@ def apply_mouth_area(
|
|||
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])
|
||||
)
|
||||
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
|
||||
)
|
||||
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)
|
||||
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
|
||||
)
|
||||
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:
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return frame
|
||||
|
|
@ -909,10 +396,7 @@ 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]
|
||||
|
|
@ -920,39 +404,22 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
|
|||
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%
|
||||
extended_forehead_height = int(forehead_height * 5.0)
|
||||
|
||||
# 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],
|
||||
]
|
||||
)
|
||||
face_outline = np.vstack([[forehead_left], right_side_face, left_side_face[::-1], [forehead_right]])
|
||||
padding = int(np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05)
|
||||
|
||||
# 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:
|
||||
|
|
@ -964,33 +431,23 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
|
|||
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)
|
||||
return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
|
||||
Loading…
Reference in New Issue