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add foo_points_priv

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Scott Register
2024-10-19 14:34:17 -07:00
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commit 999c6660e9
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# 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()