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PICO-8游戏开发项目:Mirror_Image详解

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【标题】与【描述】均为 "Mirror_Image",表明此文件或项目名称为“Mirror_Image”。结合标签信息“game game-development pico-8 pico8 PICO-8”,我们可以推断这可能是与游戏开发有关的项目,特别是与PICO-8平台相关的内容。 PICO-8 是一种虚构的游戏开发平台,它模拟了一个小型的8位计算机环境,专门为游戏爱好者和独立开发者设计。PICO-8 提供了一个简单的图形界面,能够创建游戏、编写代码和分享作品。它拥有自己的脚本语言,类似Lua,但为游戏开发做了优化。PICO-8 旨在鼓励简化的游戏设计和创意表达,同时也能快速产出完整的游戏。 从文件名称列表“Mirror_Image-master”可以推测这是一个项目文件夹,其中的“master”可能意味着这是项目的主版本或者主要分支。这表明项目可能包含了游戏的源代码、资源文件、文档以及可能的开发工具。 关于“Mirror_Image”这个标题,我们可以联想到其可能涉及的游戏概念。"Mirror"(镜子)通常在游戏设计中用于创造对称、反射或者其他视觉效果。例如,在游戏中,玩家可能需要操作角色或物体与镜面或镜像进行互动。通过反射,游戏场景可以被复制并以不同的视觉方式展现给玩家,从而创造出独一无二的谜题或挑战。 由于这个文件没有提供更具体的游戏设计、规则描述或者玩法介绍,我们只能做出一些假设。例如,如果玩家需要在PICO-8平台上开发一个名为“Mirror_Image”的游戏,那么游戏中可能会涉及以下内容或知识点: 1. 图形和渲染技术:在PICO-8上,开发者需要熟悉其图形渲染引擎,包括如何在8位环境中渲染像素图形以及如何实现动画效果。 2. 游戏逻辑和脚本编程:开发者需要掌握PICO-8内置的Lua脚本语言,以及如何使用它来编写游戏逻辑,比如玩家控制、游戏状态管理、碰撞检测等。 3. 镜像和对称算法:为了在游戏中实现镜像效果,可能需要学习和编写关于图像翻转、对称和反射的算法,使得游戏场景能够以预期的方式展现。 4. 谜题和关卡设计:如果“Mirror_Image”包含了解谜元素,开发者需要设计富有挑战性且有趣的关卡,这将涉及到创造性的思维以及对游戏平衡性的把握。 5. 用户界面和体验:为了提升玩家体验,游戏可能需要具有直观易用的用户界面,包括菜单、得分系统和控制系统等。在PICO-8有限的空间和资源下,这部分设计尤为考验开发者。 6. 音效和音乐:PICO-8 也支持音乐和音效的集成,开发者需要了解如何为游戏创作或整合背景音乐,以及如何在游戏进程中触发不同的音效,增强游戏氛围。 7. 发布和分享:在游戏开发完成后,开发者还需学会如何将游戏打包并发布到PICO-8社区,这可能涉及使用特定工具将游戏内容压缩到一个文件中,方便分享和分发。 综上所述,虽然没有具体的文件内容,但通过标题、描述、标签和文件名的组合,我们可以推测“Mirror_Image”可能是一个与PICO-8平台相关的游戏项目,涉及图形渲染、编程逻辑、对称算法、谜题设计、用户界面、音效以及分享发布等方面的知识点。

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* 单件快递单据一维码识别程序 read_image(Image, 'D:/study/hal_pro/data/barcodes/2.png') get_image_size(Image, Width, Height) * 1. 图像预处理 rgb1_to_gray(Image, GrayImage) * 自适应阈值处理光照不均 mean_image(GrayImage, MeanImage, 50, 50) sub_image(GrayImage, MeanImage, ImageSub, 1, 128) * 噪声过滤与边缘增强 median_image(ImageSub, ImageMedian, 'circle', 3, 'mirror') emphasize(ImageMedian, ImageEmphasize, 3, 3, 10) * 2. 倾斜校正(处理任意放置情况) * 使用边缘检测定位单据轮廓 edges_sub_pix(ImageEmphasize, Edges, 'canny', 1, 20, 40) dilation_circle(Edges, EdgesDilation, 3) connection(EdgesDilation, ConnectedRegions) select_shape(ConnectedRegions, LargestRegions, 'area', 'and', 1000, 999999) * 拟合矩形获取倾斜角度 fit_rectangle2_contour_xld (LargestRegions, 'tukey', -1, 0, 5, 2, RowBegin, ColBegin, RowEnd, ColEnd, Nr, Nc, Dist1) * 旋转图像校正倾斜 rotate_image(Image, ImageRotate, -Phi,'constant') rgb1_to_gray(ImageRotate, GrayCorrected) * 3. 一维码定位与识别 * 定义条码搜索ROI(根据校正后的图像调整) gen_rectangle1(ROI_0, Row-50, Column-50, Row+50, Column+50) reduce_domain(GrayCorrected, ROI_0, ImageReduced) * 使用Halcon条码识别算子 find_bar_code (Image, SymbolRegions, BarCodeHandle, 'auto', DecodedDataStrings) if (|DecodedDataStrings| > 0) * 提取条码数据 get_bar_code_result(DecodedDataStrings, 0, '解码文本', BarCodeText) dev_set_color('red') dev_display(ImageRotate) dev_display(SymbolRegions) disp_message (WindowHandle, DecodedDataStrings, 'image', Row, Column, 'black', 'true')

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def test_flip_mirror_impl(cam, props, fmt, chart, debug, log_path): 42 43 """Return if image is flipped or mirrored. 44 45 Args: 46 cam : An open its session. 47 props : Properties of cam. 48 fmt : dict,Capture format. 49 chart: Object with chart properties. 50 debug: boolean,whether to run test in debug mode or not. 51 log_path: log_path to save the captured image. 52 53 Returns: 54 boolean: True if flipped, False if not 55 """ 56 57 # determine if monochrome camera 58 mono_camera = camera_properties_utils.mono_camera(props) 59 60 # get a local copy of the chart template 61 template = cv2.imread(opencv_processing_utils.CHART_FILE, cv2.IMREAD_ANYDEPTH) 62 63 # take img, crop chart, scale and prep for cv2 template match 64 cam.do_3a() 65 req = capture_request_utils.auto_capture_request() 66 cap = cam.do_capture(req, fmt) 67 y, _, _ = image_processing_utils.convert_capture_to_planes(cap, props) 68 y = image_processing_utils.rotate_img_per_argv(y) 69 patch = image_processing_utils.get_image_patch(y, chart.xnorm, chart.ynorm, 70 chart.wnorm, chart.hnorm) 71 patch = 255 * opencv_processing_utils.gray_scale_img(patch) 72 patch = opencv_processing_utils.scale_img( 73 patch.astype(np.uint8), chart.scale) 74 75 # check image has content 76 if np.max(patch)-np.min(patch) < 255/8: 77 raise AssertionError('Image patch has no content! Check setup.') 78 79 # save full images if in debug 80 if debug: 81 image_processing_utils.write_image( 82 template[:, :, np.newaxis] / 255.0, 83 '%s_template.jpg' % os.path.join(log_path, NAME)) 84 85 # save patch 86 image_processing_utils.write_image( 87 patch[:, :, np.newaxis] / 255.0, 88 '%s_scene_patch.jpg' % os.path.join(log_path, NAME)) 89 90 # crop center areas and strip off any extra rows/columns 91 template = image_processing_utils.get_image_patch( 92 template, PATCH_X, PATCH_Y, PATCH_W, PATCH_H) 93 patch = image_processing_utils.get_image_patch( 94 patch, PATCH_X, PATCH_Y, PATCH_W, PATCH_H) 95 patch = patch[0:min(patch.shape[0], template.shape[0]), 96 0:min(patch.shape[1], template.shape[1])] 97 comp_chart = patch 98 99 # determine optimum orientation 100 opts = [] 101 for orientation in CHART_ORIENTATIONS: 102 if orientation == 'flip': 103 comp_chart = np.flipud(patch) 104 elif orientation == 'mirror': 105 comp_chart = np.fliplr(patch) 106 elif orientation == 'rotate': 107 comp_chart = np.flipud(np.fliplr(patch)) 108 correlation = cv2.matchTemplate(comp_chart, template, cv2.TM_CCOEFF) 109 _, opt_val, _, _ = cv2.minMaxLoc(correlation) 110 if debug: 111 cv2.imwrite('%s_%s.jpg' % (os.path.join(log_path, NAME), orientation), 112 comp_chart) 113 logging.debug('%s correlation value: %d', orientation, opt_val) 114 opts.append(opt_val) 115 116 # determine if 'nominal' or 'rotated' is best orientation 117 if not (opts[0] == max(opts) or opts[3] == max(opts)): 118 raise AssertionError( 119 f'Optimum orientation is {CHART_ORIENTATIONS[np.argmax(opts)]}') 120 # print warning if rotated 121 if opts[3] == max(opts): 122 logging.warning('Image is rotated 180 degrees. Tablet might be rotated.') 实际的函数是这样的,应该如何去修改

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import os import sys import urllib.request import zipfile import h5py import json import numpy as np import shutil # 目标文件路径 TARGET_DIR = r"D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning" TARGET_FILE = os.path.join(TARGET_DIR, "coco2014_captions.h5") def handle_long_paths(path): """处理Windows长路径问题""" if len(path) > 260 and sys.platform == "win32": if not path.startswith(r"\\?\\"): return r"\\?\\" + os.path.abspath(path) return path def fix_path_issues(file_path): """修复路径相关的问题""" file_path = handle_long_paths(file_path) os.makedirs(os.path.dirname(file_path), exist_ok=True) if os.path.exists(file_path): print(f"✅ 文件已存在: {file_path}") return True, file_path print(f"❌ 文件不存在: {file_path}") return False, file_path def download_coco_dataset(file_path): """下载并解压COCO数据集""" # 国内镜像源 MIRROR_SOURCES = [ "https://hf-mirror.com/datasets/sentence-transformers/coco-captions/resolve/main/coco2014_captions.h5", "https://storage.googleapis.com/ai-studio-online/coco2014_captions.h5", "https://bj.bcebos.com/paddlex/datasets/coco2014_captions.h5" ] # 尝试直接下载预处理好的HDF5文件 for idx, url in enumerate(MIRROR_SOURCES): print(f"尝试下载源 {idx+1}/{len(MIRROR_SOURCES)}: {url}") try: urllib.request.urlretrieve(url, file_path) print("✅ 直接下载成功!") return True, file_path except Exception as e: print(f"下载失败: {str(e)}") # 如果直接下载失败,尝试下载原始数据并转换 print("尝试下载原始数据并转换...") try: # 下载原始标注文件 annotations_url = "http://images.cocodataset.org/annotations/annotations_trainval2014.zip" zip_path = os.path.join(TARGET_DIR, "annotations.zip") urllib.request.urlretrieve(annotations_url, zip_path) # 解压文件 with zipfile.ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(TARGET_DIR) # 转换格式 json_path = os.path.join(TARGET_DIR, "annotations", "captions_trainval2014.json") convert_to_hdf5(json_path, file_path) # 清理临时文件 os.remove(zip_path) shutil.rmtree(os.path.join(TARGET_DIR, "annotations")) return True, file_path except Exception as e: print(f"下载和转换失败: {str(e)}") return False, file_path def convert_to_hdf5(json_path, hdf5_path): """将JSON标注转换为课程需要的HDF5格式""" print(f"转换标注格式: {json_path} → {hdf5_path}") try: # 读取JSON文件 with open(json_path, 'r') as f: data = json.load(f) # 提取图像ID和标题 image_ids = [] captions = [] for ann in data['annotations']: image_ids.append(ann['image_id']) captions.append(ann['caption'].encode('utf-8')) # 转换为字节串 # 转换为NumPy数组 image_ids = np.array(image_ids, dtype=np.int32) captions = np.array(captions, dtype=h5py.special_dtype(vlen=str)) # 创建HDF5文件 with h5py.File(hdf5_path, 'w') as hf: hf.create_dataset("train_image_idxs", data=image_ids) hf.create_dataset("train_captions", data=captions) print(f"✅ 成功转换 {len(captions)} 条标注") except Exception as e: print(f"❌ 格式转换失败: {str(e)}") raise def validate_hdf5_file(file_path): """验证HDF5文件完整性""" try: with h5py.File(file_path, 'r') as f: if "train_captions" in f and "train_image_idxs" in f: captions = f["train_captions"][:] image_ids = f["train_image_idxs"][:] print(f"✅ 文件验证成功! 包含 {len(captions)} 条标注") print(f"示例标注: {captions[0].decode('utf-8')}") print(f"对应图像ID: {image_ids[0]}") return True print("❌ 文件结构不符合要求") return False except Exception as e: print(f"❌ 文件验证失败: {str(e)}") return False # 主执行流程 if __name__ == "__main__": # 处理路径问题 exists, current_path = fix_path_issues(TARGET_FILE) if not exists: # 下载数据集 downloaded, current_path = download_coco_dataset(current_path) if downloaded: print(f"✅ 数据集成功部署: {current_path}") else: print("❌ 数据集部署失败,请手动下载") sys.exit(1) # 验证文件 if not validate_hdf5_file(current_path): print("❌ 文件验证失败,数据集可能损坏") sys.exit(1) print("✨ 所有操作成功完成!") ❌ 文件不存在: D:\cs231n.github.io-master\assignments\2021\assignment3_colab\assignment3\cs231n\datasets\coco_captioning\coco2014_captions.h5 尝试下载源 1/3: https://hf-mirror.com/datasets/sentence-transformers/coco-captions/resolve/main/coco2014_captions.h5 下载失败: HTTP Error 404: Not Found 尝试下载源 2/3: https://storage.googleapis.com/ai-studio-online/coco2014_captions.h5 下载失败: HTTP Error 404: Not Found 尝试下载源 3/3: https://bj.bcebos.com/paddlex/datasets/coco2014_captions.h5 下载失败: HTTP Error 404: Not Found 尝试下载原始数据并转换... 原始数据下载太慢了

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Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`. Traceback (most recent call last): File "C:\Users\T_Rain\Desktop\毕业设计-基于AI基础模型的图像语义通信关键技术研究\源代码\SAM_text.py", line 12, in <module> processor = CLIPProcessor.from_pretrained("ViT-B32") File "C:\Users\T_Rain\anaconda3\envs\transformer\lib\site-packages\transformers\processing_utils.py", line 1185, in from_pretrained args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs) File "C:\Users\T_Rain\anaconda3\envs\transformer\lib\site-packages\transformers\processing_utils.py", line 1248, in _get_arguments_from_pretrained args.append(attribute_class.from_pretrained(pretrained_model_name_or_path, **kwargs)) File "C:\Users\T_Rain\anaconda3\envs\transformer\lib\site-packages\transformers\image_processing_base.py", line 208, in from_pretrained image_processor_dict, kwargs = cls.get_image_processor_dict(pretrained_model_name_or_path, **kwargs) File "C:\Users\T_Rain\anaconda3\envs\transformer\lib\site-packages\transformers\image_processing_base.py", line 340, in get_image_processor_dict resolved_image_processor_file = cached_file( File "C:\Users\T_Rain\anaconda3\envs\transformer\lib\site-packages\transformers\utils\hub.py", line 312, in cached_file file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs) File "C:\Users\T_Rain\anaconda3\envs\transformer\lib\site-packages\transformers\utils\hub.py", line 427, in cached_files raise OSError( OSError: ViT-B32 does not appear to have a file named preprocessor_config.json. Checkout 'https://huggingface.co/ViT-B32/tree/main' for available files.

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