分割和背景去除
我为什么这么做:
我正在研究这个项目,并开发了一堆工具来完成重型数据工程组件的发布,因为其中一些是巧妙的,但大多数是,这样它们就会被下一个 gemini 模型突袭并并入愚蠢的 google colab gemini 建议引擎。 - 蒂姆
说明和解释
指示:
- 设置检测输出目录,其中存储检测到的对象的帧。
- 定义将保存分段帧的segmentation_output_dir。
- 使用 yolo 分割模型初始化egmentation_model。
- 运行脚本对帧进行分割并保存结果。
说明:
代码:
import osimport shutilfrom ultralytics import YOLOimport cv2import numpy as npfrom rembg import remove# Paths to the base directoriesdetection_output_dir = '/workspace/stage2.frame.detection'segmentation_output_dir = '/workspace/stage3.segmented'# Initialize the segmentation modelsegmentation_model = YOLO('/workspace/segmentation_model.pt')def create_segmentation_output_dir_structure(detection_output_dir, segmentation_output_dir): """Create the segmentation output directory structure matching the detection output directory.""" for root, dirs, files in os.walk(detection_output_dir): for dir_name in dirs: new_dir_path = os.path.join(segmentation_output_dir, os.path.relpath(os.path.join(root, dir_name), detection_output_dir)) os.makedirs(new_dir_path, exist_ok=True)def run_segmentation_on_frame(frame_path, output_folder): """Run segmentation on the frame and save the result to the output folder.""" os.makedirs(output_folder, exist_ok=True) frame_filename = os.path.basename(frame_path) output_path = os.path.join(output_folder, frame_filename) try: results = segmentation_model.predict(frame_path, save=False) for result in results: mask = result.masks.xy[0] if result.masks.xy else None if mask is not None: original_img_rgb = cv2.imread(frame_path) original_img_rgb = cv2.cvtColor(original_img_rgb, cv2.COLOR_BGR2RGB) image_height, image_width, _ = original_img_rgb.shape mask_img = np.zeros((image_height, image_width), dtype=np.uint8) cv2.fillPoly(mask_img, [np.array(mask, dtype=np.int32)], (255)) masked_img = cv2.bitwise_and(original_img_rgb, original_img_rgb, mask=mask_img) cv2.imwrite(output_path, cv2.cvtColor(masked_img, cv2.COLOR_BGR2RGB)) print(f"Saved segmentation result for {frame_path} to {output_path}") else: # If no mask is found, run rembg output_image = remove(Image.open(frame_path)) output_image.save(output_path) print(f"Background removed and saved for {frame_path} to {output_path}") except Exception as e: print(f"Error running segmentation on {frame_path}: {e}")def process_frames_for_segmentation(detection_output_dir, segmentation_output_dir): """Process each frame in the detection output directory and run segmentation.""" for root, dirs, files in os.walk(detection_output_dir): for file_name in files: if file_name.endswith('.jpg'): frame_path = os.path.join(root, file_name) relative_path = os.path.relpath(root, detection_output_dir) output_folder = os.path.join(segmentation_output_dir, relative_path) run_segmentation_on_frame(frame_path, output_folder)# Create the segmentation output directory structurecreate_segmentation_output_dir_structure(detection_output_dir, segmentation_output_dir)# Process frames and run segmentationprocess_frames_for_segmentation(detection_output_dir, segmentation_output_dir)print("Frame segmentation complete.")
关键词和标签
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由来自加拿大中西部的 tim 创建。
2024.
本文档已获得 gpl 许可。