328 lines
12 KiB
Python
328 lines
12 KiB
Python
import glob
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import os
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import random
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import cv2
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# --- CONFIGURATION ---
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OUTPUT_DIRECTORY = "dataset_v3"
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FRAMES_TO_EXTRACT = 20 # Number of random anchor frames to find targets
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SCALE_FACTOR = 0.5 # Resize video to 1/2 size for easier viewing
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# Define the EXACT size of the bounding box around your click point
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BOX_WIDTH = 64
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BOX_HEIGHT = 64
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# Defined classification options (YOLO maps these to IDs: 0, 1, 2, 3...)
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CLASSIFICATION_OPTIONS = [
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"guardian",
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"interceptor",
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"sentinel",
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"rocket",
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"bomber",
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"background",
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"asteroid",
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]
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# ---------------------
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def get_dataset_counts():
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"""Counts the number of existing bounding boxes by parsing YOLO text files."""
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counts = {option: 0 for option in CLASSIFICATION_OPTIONS}
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labels_dir = os.path.join(OUTPUT_DIRECTORY, "labels")
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if os.path.exists(labels_dir):
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# Scan all text annotation files in the YOLO labels directory
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for txt_file in glob.glob(os.path.join(labels_dir, "*.txt")):
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try:
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with open(txt_file, "r") as f:
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for line in f:
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parts = line.strip().split()
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if parts:
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class_id = int(parts[0])
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if 0 <= class_id < len(CLASSIFICATION_OPTIONS):
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option = CLASSIFICATION_OPTIONS[class_id]
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counts[option] += 1
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except Exception:
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pass # Skip corrupted or unreadable text files safely
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return counts
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def mouse_click_callback(event, x, y, flags, param):
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"""Handles mouse clicks.
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Calculates YOLO coordinates, saves the full frame, and appends a line to
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the text file.
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"""
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if event == cv2.EVENT_LBUTTONDOWN:
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# Ignore clicks if the user is in "background" capture mode
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if param["session_label"] == "background":
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print(
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" Info: In background mode, do not click targets. Just press SPACE to save frame."
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)
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return
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frame = param["frame"]
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display_frame = param["display_frame"]
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frame_idx = param["frame_idx"]
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window_name = param["window_name"]
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# Calculate top-left and bottom-right corners
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x1 = x - BOX_WIDTH // 2
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y1 = y - BOX_HEIGHT // 2
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x2 = x1 + BOX_WIDTH
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y2 = y1 + BOX_HEIGHT
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# --- Edge Case Handling ---
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if x1 < 0:
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x1 = 0
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x2 = BOX_WIDTH
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if y1 < 0:
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y1 = 0
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y2 = BOX_HEIGHT
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if x2 > frame.shape[1]:
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x2 = frame.shape[1]
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x1 = x2 - BOX_WIDTH
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if y2 > frame.shape[0]:
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y2 = frame.shape[0]
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y1 = y2 - BOX_HEIGHT
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# --- YOLO Normalized Coordinate Conversion ---
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img_h, img_w = frame.shape[0], frame.shape[1]
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true_center_x = (x1 + x2) / 2.0
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true_center_y = (y1 + y2) / 2.0
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x_center_norm = true_center_x / img_w
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y_center_norm = true_center_y / img_h
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width_norm = BOX_WIDTH / img_w
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height_norm = BOX_HEIGHT / img_h
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# Clean video name to ensure a unique, safe filename
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video_base = os.path.splitext(os.path.basename(param["video_name"]))[0]
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filename_base = f"{video_base}_frame_{frame_idx}"
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image_save_path = os.path.join(
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param["images_dir"], f"{filename_base}.jpg"
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)
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label_save_path = os.path.join(
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param["labels_dir"], f"{filename_base}.txt"
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)
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# 1. Save the full frame image (overwriting is fine if clicking multiple items)
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cv2.imwrite(image_save_path, frame)
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# 2. Append the target annotation to the YOLO text file
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class_id = param["class_id"]
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with open(label_save_path, "a") as f:
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f.write(
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f"{class_id} {x_center_norm:.6f} {y_center_norm:.6f} {width_norm:.6f} {height_norm:.6f}\n"
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)
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print(
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f" Logged {param['session_label']} box coordinate to {label_save_path}"
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)
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param["box_drawn"] = True
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# Draw visual feedback box and short class label on screen
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cv2.rectangle(display_frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.putText(
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display_frame,
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param["session_label"],
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(x1, y1 - 5),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.4,
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(0, 255, 0),
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1,
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)
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cv2.imshow(window_name, display_frame)
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def label_video_session():
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print("=========================================")
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print(" AI YOLO DATASET LABELER ")
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print("=========================================\n")
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# 1. Automatically find and select a random MP4 video
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mp4_files = glob.glob("*.mp4")
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if not mp4_files:
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print(
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"Error: No .mp4 files found in the current directory.\n"
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"Please place this script in the same folder as your video files."
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)
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return
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selected_video = random.choice(mp4_files)
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print(f"-> Selected Video File: '{selected_video}'")
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# 2. Fetch and display current dataset balance dashboard
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counts = get_dataset_counts()
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print("\n-----------------------------------------")
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print(" CURRENT DATASET BALANCE STATUS ")
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print("-----------------------------------------")
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for option, count in counts.items():
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print(f" • {option.capitalize():<12} : {count} boxes logged")
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print("-----------------------------------------")
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print(
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"💡 Tip: Try to choose options with lower counts to keep data balanced!"
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)
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# 3. Force selection of a valid label
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print("\nAvailable Classification Targets:")
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for idx, option in enumerate(CLASSIFICATION_OPTIONS, start=1):
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print(f" [{idx}] {option} (Current Boxes: {counts[option]})")
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while True:
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try:
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choice = input(
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f"\nEnter the number (1-{len(CLASSIFICATION_OPTIONS)}) of what you are labeling: "
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).strip()
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choice_idx = int(choice) - 1
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if 0 <= choice_idx < len(CLASSIFICATION_OPTIONS):
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session_label = CLASSIFICATION_OPTIONS[choice_idx]
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session_class_id = choice_idx
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break
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else:
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print("Invalid selection. Please choose a number from the list.")
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except ValueError:
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print("Invalid input. Please enter a number.")
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# Open video and check validity
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cap = cv2.VideoCapture(selected_video)
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if not cap.isOpened():
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print(f"Error: Could not open video file {selected_video}")
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return
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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if total_frames == 0:
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print("Error: Video has 0 frames.")
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return
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# Select random unique anchor frames to jump between
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num_frames = min(FRAMES_TO_EXTRACT, total_frames)
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frame_indices = random.sample(range(total_frames), num_frames)
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frame_indices.sort()
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# Set up standard YOLO dataset directory branches
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images_dir = os.path.join(OUTPUT_DIRECTORY, "images")
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labels_dir = os.path.join(OUTPUT_DIRECTORY, "labels")
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os.makedirs(images_dir, exist_ok=True)
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os.makedirs(labels_dir, exist_ok=True)
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# Comprehensive User Instructions
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print("\n" + "=" * 50)
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print(" HOW TO LABEL")
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print("=" * 50)
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print(f"• TARGET OBJECT: {session_label.upper()}")
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print(f"• SESSION SCOPE: {num_frames} random search zones in '{selected_video}'")
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print(f"• BOUNDING BOX : {BOX_WIDTH}x{BOX_HEIGHT} pixels scaled to YOLO layout")
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print("-" * 50)
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print("► CONTROLS:")
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print(" [Left-Click] - Click directly on target to tag position (auto-saves frame).")
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print(" [Right Arrow] - Move FORWARD 1 individual frame (sequential tracking, no save).")
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print(" [Left Arrow] - Move BACKWARD 1 individual frame (sequential tracking, no save).")
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print(" [SPACE / ENTER] - Jump completely to the NEXT random video search spot.")
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print(" [Q Key] - Quit and save all progress up to this point.")
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print("=" * 50)
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input("\nPress ENTER when you are ready to begin...")
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# Cross-platform arrow key mappings for waitKeyEx()
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LEFT_KEYS = [81, 2, 2424832, 65361, 63234]
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RIGHT_KEYS = [83, 3, 2490368, 65363, 63235]
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i = 0
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current_frame = frame_indices[i]
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while i < len(frame_indices):
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cap.set(cv2.CAP_PROP_POS_FRAMES, current_frame)
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ret, frame = cap.read()
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if not ret:
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print(f" Warning: Could not read frame {current_frame}. Moving to next random anchor.")
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i += 1
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if i < len(frame_indices):
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current_frame = frame_indices[i]
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continue
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# Resize the frame by 1/2 for laptop viewability
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frame_resized = cv2.resize(
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frame, (0, 0), fx=SCALE_FACTOR, fy=SCALE_FACTOR
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)
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frame_display = frame_resized.copy()
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window_name = f"Labeling: {session_label} | Spot {i+1}/{num_frames} (Frame {current_frame})"
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cv2.namedWindow(window_name)
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session_state = {
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"frame": frame_resized,
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"display_frame": frame_display,
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"images_dir": images_dir,
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"labels_dir": labels_dir,
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"frame_idx": current_frame,
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"window_name": window_name,
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"video_name": selected_video,
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"class_id": session_class_id,
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"session_label": session_label,
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"box_drawn": False,
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}
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cv2.setMouseCallback(window_name, mouse_click_callback, session_state)
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# Keep window open until user acts
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cv2.imshow(window_name, frame_display)
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while True:
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cv2.moveWindow(window_name, 0, 0)
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key = cv2.waitKeyEx(1)
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# Enter (13) or Space (32) -> Jump to the next random anchor spot
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if key == 13 or key == 32:
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if session_label == "background":
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video_base = os.path.splitext(
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os.path.basename(selected_video)
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)[0]
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filename_base = f"{video_base}_frame_{current_frame}"
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image_save_path = os.path.join(
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images_dir, f"{filename_base}.jpg"
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)
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label_save_path = os.path.join(
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labels_dir, f"{filename_base}.txt"
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)
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cv2.imwrite(image_save_path, frame_resized)
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open(label_save_path, "a").close() # Creates empty file
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print(f" Registered negative background frame: {filename_base}")
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i += 1
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if i < len(frame_indices):
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current_frame = frame_indices[i]
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break
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# Right Arrow -> Move forward exactly ONE individual sequential frame
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elif key in RIGHT_KEYS:
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if current_frame < total_frames - 10:
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current_frame += 10
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else:
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print(" Already at the very last frame of the video!")
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continue
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break
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# Left Arrow -> Move backward exactly ONE individual sequential frame
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elif key in LEFT_KEYS:
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if current_frame > 0:
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current_frame -= 10
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else:
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print(" Already at the first frame of the video!")
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continue
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break
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# Quit
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elif key == ord("q") or key == ord("Q") or (key & 0xFF) in [ord("q"), ord("Q")]:
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print("\nQuitting session early... Saving progress.")
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cap.release()
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cv2.destroyAllWindows()
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return
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cv2.destroyWindow(window_name)
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cap.release()
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cv2.destroyAllWindows()
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print(f"\nSession finished! Dataset updated successfully inside '{OUTPUT_DIRECTORY}'.")
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if __name__ == "__main__":
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label_video_session()
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