import cv2 import os import time from pathlib import Path import re from tqdm import tqdm import cv2 # 导入您提供的拼接器类和拼接顺序生成器 from fry_project_classes.stitch_img_template_match import ImageStitcherTemplateMatch from fry_project_classes.get_full_stitch_order import get_full_stitch_order # 导入您提供的拼接器类 from fry_project_classes.stitch_img_template_match import ImageStitcherTemplateMatch def natural_sort_key(s): """ 提供自然排序的键，例如 '2.jpg' 会排在 '10.jpg' 之前。 """ return [int(text) if text.isdigit() else text.lower() for text in re.split(r'(\d+)', str(s))] def stitch_img(IMAGE_DIR, OUTPUT_DIR, NUM_COLS: int, NUM_ROWS: int, ESTIMATE_OVERLAP_HORIZONTAL_PIXELS: int, ESTIMATE_OVERLAP_VERTICAL_PIXELS: int, BLEND_TYPE: str, LIGHT_COMPENSATION: bool, DEBUG_MODE: bool, BLEND_RATIO: float, LIGHT_COMPENSATION_WIDTH: int): OUTPUT_DIR.mkdir(exist_ok=True) print("--- 图像拼接开始 ---") print(f"配置: {NUM_ROWS}行 x {NUM_COLS}列") print(f"图片目录: {IMAGE_DIR}") print(f"输出目录: {OUTPUT_DIR}") print(f"水平重叠预估: {ESTIMATE_OVERLAP_HORIZONTAL_PIXELS}px, 垂直重叠预估: {ESTIMATE_OVERLAP_VERTICAL_PIXELS}px") print(f"融合模式: {BLEND_TYPE}, 权重: {BLEND_RATIO}") print(f"光照补偿: {'启用' if LIGHT_COMPENSATION else '禁用'}, 补偿宽度: {LIGHT_COMPENSATION_WIDTH}px") # --- 1. 加载并排序所有图片 --- image_paths = sorted(list(IMAGE_DIR.glob("*.jpg")), key=natural_sort_key) if len(image_paths) != NUM_COLS * NUM_ROWS: print(f"错误: 找到 {len(image_paths)} 张图片, 但预期需要 {NUM_COLS * NUM_ROWS} 张。") return # 将所有图片读入内存，并用一个字典存储 images_dict = {} for i, path in enumerate(image_paths): img = cv2.imread(str(path)) if img is None: print(f"错误: 无法读取图片 {path}") return # 使用从 '1' 开始的字符串作为键，模仿 stitch_worker.py 的行为 images_dict[str(i + 1)] = img # --- 2. 获取拼接顺序 --- full_stitch_order_dict = get_full_stitch_order(NUM_ROWS, NUM_COLS) print(f"\n--- 获取到 {len(full_stitch_order_dict)} 步拼接指令 ---") # --- 3. 按照指令集执行拼接 --- final_image = None progress_bar = tqdm(full_stitch_order_dict.items(), desc="执行拼接") for step, (round_num, img1_name, img2_name, direction, result_name) in progress_bar: progress_bar.set_description(f"步骤 {step}: {img1_name} + {img2_name} -> {result_name}") img1 = images_dict[img1_name] img2 = images_dict[img2_name] # 根据方向选择重叠像素 overlap_pixels = 0 if direction == 'horizontal': overlap_pixels = ESTIMATE_OVERLAP_HORIZONTAL_PIXELS elif direction == 'vertical': overlap_pixels = ESTIMATE_OVERLAP_VERTICAL_PIXELS else: raise ValueError(f"未知的拼接方向: {direction}") # 每次都创建一个新的拼接器实例 stitcher = ImageStitcherTemplateMatch( estimate_overlap_pixels=overlap_pixels, stitch_type=direction, blend_type=BLEND_TYPE, blend_ratio=BLEND_RATIO, light_uniformity_compensation_enabled=LIGHT_COMPENSATION, light_uniformity_compensation_width=LIGHT_COMPENSATION_WIDTH, debug=DEBUG_MODE, debug_dir=str(OUTPUT_DIR / f'debug_{result_name}') ) # 执行拼接 stitched_image = stitcher.stitch_main(img1, img2) # 将新生成的图片存入字典，用于下一步拼接 images_dict[result_name] = stitched_image final_image = stitched_image # 始终保留最新的拼接结果 if DEBUG_MODE: # 保存每一步的中间结果 intermediate_path = OUTPUT_DIR / f"intermediate_{result_name}.jpg" cv2.imwrite(str(intermediate_path), stitched_image) # --- 4. 保存最终结果 --- if final_image is not None: final_output_path = OUTPUT_DIR / "final_stitched_image.jpg" cv2.imwrite(str(final_output_path), final_image) print("\n--- 所有拼接任务完成！---") print(f"最终的全景图已保存至: {final_output_path}") else: print("\n--- 拼接失败，没有生成最终图像 ---") def main(): """ 主执行函数 """ # --- 1. 配置参数 --- # 图片和输出目录设置 IMAGE_DIR = Path(r"C:\Code\ML\Project\StitchImageServer\temp\input\front_0_1") # OUTPUT_DIR = Path(r"C:\Code\ML\Project\StitchImageServer\temp\output") # 拼图网格设置 NUM_COLS = 4 NUM_ROWS = 6 # ！！！关键拼接参数，您可能需要根据实际图片进行调整！！！ # 预估水平方向重叠的像素数。如果您的图片宽1920像素，重叠25%，则该值为 1920 * 0.25 ≈ 480 # 预估垂直方向重叠的像素数。如果您的图片高1080像素，重叠25%，则该值为 1080 * 0.25 ≈ 270 # estimate_overlap_ratio = 0.45 ESTIMATE_OVERLAP_HORIZONTAL_PIXELS = 405 ESTIMATE_OVERLAP_VERTICAL_PIXELS = 440 # 选择融合模式。'blend_half_importance_partial_HSV' 是效果最好但最慢的模式之一 ''' 前五个都不行 half_importance,right_first,left_first 0星 ⭐half_importance_add_weight 2星, 49秒 half_importance_global_brightness 0星, 49秒 half_importance_partial_brightness 还行, 4星, 速度适中 ,99秒 blend_half_importance_partial_HV 不错 5星, 慢, 107秒 blend_half_importance_partial_SV 不错, 5星, 慢, 108秒 blend_half_importance_partial_HSV 很不错, 5星, 很慢, 120秒 ⭐blend_half_importance_partial_brightness_add_weight: 5星, 106秒 ''' blend_type_list = ["half_importance_add_weight", # "half_importance_global_brightness", "half_importance_partial_brightness", # "blend_half_importance_partial_HV", "blend_half_importance_partial_SV", # "blend_half_importance_partial_HSV", "blend_half_importance_partial_brightness_add_weight" ] # BLEND_TYPE = 'blend_half_importance_partial_HSV' # 是否开启光照补偿（推荐开启以获得更好效果） LIGHT_COMPENSATION = True # 是否开启调试模式（会生成大量中间过程图片，用于分析问题） DEBUG_MODE = False for i, BLEND_TYPE in enumerate(blend_type_list): base_dir_path = r"C:\Code\ML\Project\StitchImageServer\temp\output" img_dir_name = f"{i}_{BLEND_TYPE}" OUTPUT_DIR = Path(os.path.join(base_dir_path, img_dir_name)) one_img_time = time.time() stitch_img(IMAGE_DIR=IMAGE_DIR, OUTPUT_DIR=OUTPUT_DIR, NUM_COLS=NUM_COLS, NUM_ROWS=NUM_ROWS, ESTIMATE_OVERLAP_HORIZONTAL_PIXELS=ESTIMATE_OVERLAP_HORIZONTAL_PIXELS, ESTIMATE_OVERLAP_VERTICAL_PIXELS=ESTIMATE_OVERLAP_VERTICAL_PIXELS, BLEND_TYPE=BLEND_TYPE, BLEND_RATIO=0.5, LIGHT_COMPENSATION=LIGHT_COMPENSATION, LIGHT_COMPENSATION_WIDTH=15, DEBUG_MODE=DEBUG_MODE) print() print("_" * 20) print(f"单个用时: {BLEND_TYPE}: {time.time() - one_img_time}") print("_" * 20) if __name__ == '__main__': start_time = time.time() main() end_time = time.time() print(f"\n总耗时: {end_time - start_time:.2f} 秒")