# this script will process multiple video segments (5 second clips) # of a long (1 hour total video) and will greenscreen/key out everything # except tracked objects. # # To specify which items are tracked, the user will call the script with # --segments-collect-points 1 5 10 ... Which are segments to be treated # as "keyframes", for these we will ask the user to select the point # we want to track by hand, this is done for large changes in the objects # position in the video. # # For other segments, since the object is mostly static in the frame, # We will use the previous segments final frame (an image # with the object we want to track visible, and everything else green) # to determine an input mask and use add_new_mask() instead of selecting # points. # # When the script finishes, each segment should have an output directory # with the same object tracked throughout the every frame in all the segment directories # # I will then turn these back into a video using ffmpeg but that is outside the scope # of this program import os import cv2 import numpy as np import torch import sys from sam2.build_sam import build_sam2_video_predictor import argparse # Variables for input and output directories SAM2_CHECKPOINT = "../checkpoints/sam2.1_hiera_large.pt" MODEL_CFG = "configs/sam2.1/sam2.1_hiera_l.yaml" def load_previous_segment_mask(prev_segment_dir): mask_path = os.path.join(prev_segment_dir, "mask.jpg") mask_image = cv2.imread(mask_path) # Extract Object A and Object B masks mask_a = (mask_image[:, :, 1] == 255) # Green channel mask_b = (mask_image[:, :, 0] == 254) # Blue channel # show an image of mask a and mask b, resize the window to 300 pixels #cv2.namedWindow('Mask A', cv2.WINDOW_NORMAL) #cv2.resizeWindow('Select Points', int(mask_image.shape[1] * (500 / mask_image.shape[0])), 500) ##cv2.imshow('Mask A', mask_a.astype(np.uint8) * 255) #cv2.imshow('Mask A', mask_image) per_obj_input_mask = {1: mask_a, 2: mask_b} input_palette = None # No palette needed for binary mask return per_obj_input_mask, input_palette def convert_green_screen_to_mask(frame): lower_green = np.array([0, 255, 0]) upper_green = np.array([0, 255, 0]) mask = cv2.inRange(frame, lower_green, upper_green) mask = cv2.bitwise_not(mask) return mask > 0 def get_per_obj_mask(mask): object_ids = np.unique(mask) object_ids = object_ids[object_ids > 0].tolist() per_obj_mask = {object_id: (mask == object_id) for object_id in object_ids} return per_obj_mask def load_masks_from_dir(input_mask_dir, video_name, frame_name, per_obj_png_file, allow_missing=False): if not per_obj_png_file: input_mask_path = os.path.join(input_mask_dir, video_name, f"{frame_name}.png") if allow_missing and not os.path.exists(input_mask_path): return {}, None input_mask, input_palette = load_ann_png(input_mask_path) per_obj_input_mask = get_per_obj_mask(input_mask) else: per_obj_input_mask = {} input_palette = None for object_name in os.listdir(os.path.join(input_mask_dir, video_name)): object_id = int(object_name) input_mask_path = os.path.join(input_mask_dir, video_name, object_name, f"{frame_name}.png") if allow_missing and not os.path.exists(input_mask_path): continue input_mask, input_palette = load_ann_png(input_mask_path) per_obj_input_mask[object_id] = input_mask > 0 if not per_obj_input_mask: frame_path = os.path.join(input_mask_dir, video_name, f"{frame_name}.jpg") if os.path.exists(frame_path): frame = cv2.imread(frame_path) mask = convert_green_screen_to_mask(frame) per_obj_input_mask = {1: mask} return per_obj_input_mask, input_palette def apply_green_mask(frame, masks): green_mask = np.zeros_like(frame) green_mask[:, :] = [0, 255, 0] combined_mask = np.zeros(frame.shape[:2], dtype=bool) for mask in masks: mask = mask.squeeze() if mask.shape != frame.shape[:2]: mask = cv2.resize(mask, (frame.shape[1], frame.shape[0]), interpolation=cv2.INTER_NEAREST) combined_mask = np.logical_or(combined_mask, mask) inverted_mask = np.logical_not(combined_mask) frame[inverted_mask] = green_mask[inverted_mask] return frame def initialize_predictor(): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") predictor = build_sam2_video_predictor(MODEL_CFG, SAM2_CHECKPOINT, device=device) return predictor def load_first_frame(input_frames_dir): frame_names = sorted([p for p in os.listdir(input_frames_dir) if p.endswith(('.jpg', '.jpeg', '.png'))]) first_frame_path = os.path.join(input_frames_dir, frame_names[0]) first_frame = cv2.imread(first_frame_path) return first_frame, frame_names def select_points(first_frame): points_a = [] points_b = [] current_object = 'A' point_count = 0 selection_complete = False def mouse_callback(event, x, y, flags, param): nonlocal points_a, points_b, current_object, point_count, selection_complete if event == cv2.EVENT_LBUTTONDOWN: if current_object == 'A': points_a.append((x, y)) point_count += 1 print(f"Selected point {point_count} for Object A: ({x}, {y})") if len(points_a) == 4: # Collect 4 points for Object A current_object = 'B' point_count = 0 print("Select point 1 for Object B") elif current_object == 'B': points_b.append((x, y)) point_count += 1 print(f"Selected point {point_count} for Object B: ({x}, {y})") if len(points_b) == 4: # Collect 4 points for Object B selection_complete = True print("Select point 1 for Object A") cv2.namedWindow('Select Points', cv2.WINDOW_NORMAL) cv2.resizeWindow('Select Points', int(first_frame.shape[1] * (500 / first_frame.shape[0])), 500) cv2.imshow('Select Points', first_frame) cv2.setMouseCallback('Select Points', mouse_callback) while not selection_complete: cv2.waitKey(1) cv2.destroyAllWindows() return np.array(points_a, dtype=np.float32), np.array(points_b, dtype=np.float32) def add_points_to_predictor(predictor, inference_state, points, obj_id): labels = np.array([1, 1, 1, 1], np.int32) # Update labels to match 4 points points = np.array(points, dtype=np.float32) # Ensure points have shape (4, 2) try: print(f"Adding points for Object {obj_id}: {points}") _, out_obj_ids, out_mask_logits = predictor.add_new_points_or_box( inference_state=inference_state, frame_idx=0, obj_id=obj_id, points=points, labels=labels, ) print(f"Object {obj_id} added successfully: {out_obj_ids}") return out_mask_logits except Exception as e: print(f"Error adding points for Object {obj_id}: {e}") exit() def show_mask_on_frame(frame, masks): combined_mask = np.zeros(frame.shape[:2], dtype=bool) for mask in masks: mask = mask.squeeze() if mask.shape != frame.shape[:2]: mask = cv2.resize(mask, (frame.shape[1], frame.shape[0]), interpolation=cv2.INTER_NEAREST) combined_mask = np.logical_or(combined_mask, mask) color = (0, 255, 0) frame[combined_mask] = color return frame def confirm_masks(first_frame, masks_a, masks_b): first_frame_with_masks = show_mask_on_frame(first_frame.copy(), masks_a + masks_b) cv2.namedWindow('First Frame with Masks', cv2.WINDOW_NORMAL) cv2.resizeWindow('First Frame with Masks', int(first_frame.shape[1] * (500 / first_frame.shape[0])), 500) cv2.imshow('First Frame with Masks', first_frame_with_masks) cv2.waitKey(0) cv2.destroyAllWindows() confirmation = input("Are the masks correct? (yes/no): ").strip().lower() if confirmation != 'yes': print("Aborting process.") exit() def propagate_masks(predictor, inference_state): video_segments = {} for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(inference_state): video_segments[out_frame_idx] = { out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy() for i, out_obj_id in enumerate(out_obj_ids) } return video_segments def apply_colored_mask(frame, masks_a, masks_b): colored_mask = np.zeros_like(frame) # Apply colors to the masks for mask in masks_a: mask = mask.squeeze() if mask.shape != frame.shape[:2]: mask = cv2.resize(mask, (frame.shape[1], frame.shape[0]), interpolation=cv2.INTER_NEAREST) colored_mask[mask] = [0, 255, 0] # Green for Object A for mask in masks_b: mask = mask.squeeze() if mask.shape != frame.shape[:2]: mask = cv2.resize(mask, (frame.shape[1], frame.shape[0]), interpolation=cv2.INTER_NEAREST) colored_mask[mask] = [255, 0, 0] # Blue for Object B return colored_mask def process_and_save_frames(input_frames_dir, output_frames_dir, frame_names, video_segments, segment_dir): for out_frame_idx, frame_name in enumerate(frame_names): frame_path = os.path.join(input_frames_dir, frame_name) frame = cv2.imread(frame_path) masks = [video_segments[out_frame_idx][out_obj_id] for out_obj_id in video_segments[out_frame_idx]] frame = apply_green_mask(frame, masks) output_path = os.path.join(output_frames_dir, frame_name) cv2.imwrite(output_path, frame) # Create and save mask.jpg final_frame_path = os.path.join(input_frames_dir, frame_names[-1]) final_frame = cv2.imread(final_frame_path) masks_a = [video_segments[len(frame_names) - 1][1]] masks_b = [video_segments[len(frame_names) - 1][2]] # Apply colored mask mask_image = apply_colored_mask(final_frame, masks_a, masks_b) mask_output_path = os.path.join(segment_dir, "mask.jpg") cv2.imwrite(mask_output_path, mask_image) print("Processing complete. Frames saved in:", output_frames_dir) def main(): parser = argparse.ArgumentParser(description="Process video segments.") parser.add_argument("--segments-collect-points", nargs='+', type=int, help="Segments for which to collect points.") args = parser.parse_args() base_dir = "./spirit_2min_segments" segments = [d for d in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir, d)) and d.startswith("segment_")] segments.sort(key=lambda x: int(x.split("_")[1])) collect_points_segments = args.segments_collect_points if args.segments_collect_points else [] for i, segment in enumerate(segments): print("Processing segment", segment) segment_index = int(segment.split("_")[1]) segment_dir = os.path.join(base_dir, segment) points_file = os.path.join(segment_dir, "segment_points") if segment_index in collect_points_segments and not os.path.exists(points_file): input_frames_dir = os.path.join(segment_dir, "frames") first_frame, _ = load_first_frame(input_frames_dir) points_a, points_b = select_points(first_frame) with open(points_file, 'w') as f: np.savetxt(f, points_a, header="Object A Points") np.savetxt(f, points_b, header="Object B Points") for i, segment in enumerate(segments): segment_index = int(segment.split("_")[1]) segment_dir = os.path.join(base_dir, segment) output_done_file = os.path.join(segment_dir, "output_frames_done") if not os.path.exists(output_done_file): print(f"Processing segment {segment}") points_file = os.path.join(segment_dir, "segment_points") if os.path.exists(points_file): points = np.loadtxt(points_file, comments="#") points_a = points[:4] points_b = points[4:] input_frames_dir = os.path.join(segment_dir, "frames") output_frames_dir = os.path.join(segment_dir, "output_frames") os.makedirs(output_frames_dir, exist_ok=True) first_frame, frame_names = load_first_frame(input_frames_dir) predictor = initialize_predictor() inference_state = predictor.init_state(video_path=input_frames_dir, async_loading_frames=True) if i > 0 and segment_index not in collect_points_segments and not os.path.exists(points_file): prev_segment_dir = os.path.join(base_dir, segments[i-1]) print(f"Loading previous segment masks from {prev_segment_dir}") per_obj_input_mask, input_palette = load_previous_segment_mask(prev_segment_dir) for obj_id, mask in per_obj_input_mask.items(): predictor.add_new_mask(inference_state, 0, obj_id, mask) else: print("Using points for segment") out_mask_logits_a = add_points_to_predictor(predictor, inference_state, points_a, obj_id=1) out_mask_logits_b = add_points_to_predictor(predictor, inference_state, points_b, obj_id=2) masks_a = [(out_mask_logits_a[i] > 0.0).cpu().numpy() for i in range(len(out_mask_logits_a))] masks_b = [(out_mask_logits_b[i] > 0.0).cpu().numpy() for i in range(len(out_mask_logits_b))] video_segments = propagate_masks(predictor, inference_state) predictor.reset_state(inference_state) process_and_save_frames(input_frames_dir, output_frames_dir, frame_names, video_segments, segment_dir) del inference_state del video_segments del predictor import gc gc.collect() open(output_done_file, 'a').close() if __name__ == "__main__": main()