import cv2 import os import time from pathlib import Path import re from tqdm import tqdm # 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): OUTPUT_DIR.mkdir(exist_ok=True) # 创建输出文件夹 # --- 2. 加载并排序图片 --- 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}, 光照补偿: {'启用' if LIGHT_COMPENSATION else '禁用'}") # --- 2. 加载并排序图片 --- 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 # --- 3. 阶段一：水平拼接每一行 --- stitched_rows = [] print("\n--- 阶段一: 水平拼接每一行 ---") for i in tqdm(range(NUM_ROWS), desc="处理行"): row_start_index = i * NUM_COLS row_image_paths = image_paths[row_start_index: row_start_index + NUM_COLS] # 加载行的第一张图片 current_row_image = cv2.imread(str(row_image_paths[0])) if current_row_image is None: print(f"错误: 无法读取图片 {row_image_paths[0]}") continue # 依次将该行的后续图片拼接到右侧 for j in range(1, NUM_COLS): # 为每次拼接实例化一个新的Stitcher对象，以隔离调试文件夹 stitcher_h = ImageStitcherTemplateMatch( estimate_overlap_pixels=ESTIMATE_OVERLAP_HORIZONTAL_PIXELS, stitch_type="horizontal", blend_type=BLEND_TYPE, light_uniformity_compensation_enabled=LIGHT_COMPENSATION, light_uniformity_compensation_width=30, # 光照补偿的计算宽度 debug=DEBUG_MODE, debug_dir=str(OUTPUT_DIR / f'debug_h_row{i + 1}_col{j}vs{j + 1}') ) next_image = cv2.imread(str(row_image_paths[j])) if next_image is None: print(f"错误: 无法读取图片 {row_image_paths[j]}") break current_row_image = stitcher_h.stitch_main(current_row_image, next_image) # 保存拼接好的行 row_output_path = OUTPUT_DIR / f"stitched_row_{i + 1}.jpg" cv2.imwrite(str(row_output_path), current_row_image) stitched_rows.append(current_row_image) tqdm.write(f"第 {i + 1} 行拼接完成, 已保存至 {row_output_path}") # --- 4. 阶段二：垂直拼接所有行 --- print("\n--- 阶段二: 垂直拼接所有行 ---") if not stitched_rows: print("错误: 没有成功拼接的行，无法进行垂直拼接。") return final_image = stitched_rows[0] for i in tqdm(range(1, NUM_ROWS), desc="拼接行"): # 实例化垂直拼接器 stitcher_v = ImageStitcherTemplateMatch( estimate_overlap_pixels=ESTIMATE_OVERLAP_VERTICAL_PIXELS, stitch_type="vertical", blend_type=BLEND_TYPE, light_uniformity_compensation_enabled=LIGHT_COMPENSATION, light_uniformity_compensation_width=30, debug=DEBUG_MODE, debug_dir=str(OUTPUT_DIR / f'debug_v_row{i}vs{i + 1}') ) next_row_image = stitched_rows[i] final_image = stitcher_v.stitch_main(final_image, next_row_image) # --- 5. 保存最终结果 --- 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}") 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\_250801_1142_0030") # 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 = int(round(1024 * estimate_overlap_ratio)) ESTIMATE_OVERLAP_VERTICAL_PIXELS = int(round(1024 * estimate_overlap_ratio)) # 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, LIGHT_COMPENSATION=LIGHT_COMPENSATION, 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")