[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KF2SZFQCFK44ZFP3P05WJ9":3,"skill-same:01M6KF2SZFQCFK44ZFP3P05WJ9":50},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":23,"linkOnly":24,"markdownContent":25,"markdownBody":26,"frontMatter":27,"refName":28,"commitSha":29,"skillDirectory":30,"forksCount":31,"repositoryHtmlUrl":32,"repositoryHomepageUrl":33,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":35,"install":36,"resources":49},"01M6KF2SZFQCFK44ZFP3P05WJ9","opencv-template-matching",null,"Count occurrences of a template in a target image.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",93,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fvideo-object-counting\u002Fopencv-template-matching\u002FSKILL.md",[],[18,19,20,21,22],"opencv","template","matching","opencv template matching","image","2026-11-10 10:12:30",false,"---\nname: opencv-template-matching\ndescription: Count occurrences of a template in a target image.\n---\n\n# OpenCV Template Matching\n\nUse `cv2.matchTemplate` to find objects matching a template image in the main image. Multiple detections of the same object can be filtered via Non-Maximum Suppression (NMS).\n\n```python\nimport cv2\nimport numpy as np\n\ndef count_objects(main_img_path, template_path, threshold=0.8):\n    img_gray = cv2.imread(main_img_path, 0)\n    template = cv2.imread(template_path, 0)\n    w, h = template.shape[::-1]\n    res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)\n    loc = np.where(res >= threshold)\n    \n    rects = []\n    for pt in zip(*loc[::-1]):\n        rects.append([int(pt[0]), int(pt[1]), int(w), int(h)])\n    \n    # Apply non-maximum suppression (requires imutils.object_detection or cv2.dnn.NMSBoxes)\n    rects, weights = cv2.groupRectangles(rects, 1, 0.2)\n    return len(rects)\n```\n","# OpenCV Template Matching\n\nUse `cv2.matchTemplate` to find objects matching a template image in the main image. Multiple detections of the same object can be filtered via Non-Maximum Suppression (NMS).\n\n```python\nimport cv2\nimport numpy as np\n\ndef count_objects(main_img_path, template_path, threshold=0.8):\n    img_gray = cv2.imread(main_img_path, 0)\n    template = cv2.imread(template_path, 0)\n    w, h = template.shape[::-1]\n    res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)\n    loc = np.where(res >= threshold)\n    \n    rects = []\n    for pt in zip(*loc[::-1]):\n        rects.append([int(pt[0]), int(pt[1]), int(w), int(h)])\n    \n    # Apply non-maximum suppression (requires imutils.object_detection or cv2.dnn.NMSBoxes)\n    rects, weights = cv2.groupRectangles(rects, 1, 0.2)\n    return len(rects)\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fvideo-object-counting\u002Fopencv-template-matching",7,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench","https:\u002F\u002Fcxcscmu.github.io\u002FSkillLearnBench","https:\u002F\u002Fgithub.com\u002Fcxcscmu","2026-10-02 22:55:36",{"source":8,"skillName":5,"commands":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill opencv-template-matching",{"tool":42,"command":43},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill opencv-template-matching",{"tool":45,"command":46},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill opencv-template-matching","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fvideo-object-counting\u002Fopencv-template-matching\u002FSKILL.md",true,[],[51,68,93,111,130,142],{"skillNo":52,"name":53,"nameZh":6,"description":54,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":55,"categories":56,"keywords":62,"publishedAt":23},"01M6KM5KH175P5ABP5JMX2SBDQ","object_counter","Count occurrences of an object in the image using computer vision algorithm.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Fvideo-object-counting\u002Fobject_counter\u002FSKILL.md",[57],{"categoryNo":58,"categoryName":59,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[63,64,65,66,67,22],"object counter","object","vision","computer","Computer Vision",{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":77,"publishedAt":23},"01M6KM51TMVXG79MMPCS6Y8G86","travel-planning-workflow","Invoke this skill when performing travel planning. It defines a structured workflow for generating itineraries. Following this workflow is essential to ensure the final plan satisfies user requirements.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Ftravel-planning-workflow\u002FSKILL.md",[74],{"categoryNo":75,"categoryName":76,"taxonomyType":60,"taxonomyTypeDesc":61},"01JBSEEDCAT000000000000023","Skill与智能体开发",[78,79,80,81,82,83,84,85,86,87,88,89,90,91,92],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":23},"01M6KM4DY820XHS9YTSK5QP5HD","bundled-dataset-search-tool","invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002FSKILL.md",[],[100,101,102,103,104,105,106,81,107,108,82,109,87,110,79],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":23},"01M6KM465BB9PQ0B548HQSVAWZ","glm-output","Output-processing guidance for GLM tasks. Especially useful after glm-basics and glm-calibration when you need verifier-matching metrics from output.nc and a final \u002Froot\u002Fmetrics.json.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-output\u002FSKILL.md",[],[118,119,120,87,121,122,123,124,125,20,126,127,109,128,129],"glm output","output","glm","tasks","metrics","json","basics","guidance","verifier","processing","root","calibration",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":137,"publishedAt":23},"01M6KM40KY3QWDW5W97S39R2BC","glm-calibration","Calibration guidance for GLM tasks. Often most effective after glm-basics has clarified the setup; glm-output is the companion skill for exact final metric computation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-calibration\u002FSKILL.md",[136],{"categoryNo":75,"categoryName":76,"taxonomyType":60,"taxonomyTypeDesc":61},[138,129,120,119,125,124,121,139,140,87,141],"glm calibration","setup","companion","metric",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":148,"publishedAt":23},"01M6KM3VHHVSGMGHJQB3PPT6YG","glm-basics","A good starting point for GLM calibration tasks. Use to inspect glm3.nml, confirm how GLM runs, and identify the relevant files before moving on to calibration and output evaluation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-basics\u002FSKILL.md",[],[149,120,124,150,138,129,87,119,151,152,153,154,155,156,121],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]