[{"data":1,"prerenderedAt":535},["ShallowReactive",2],{"node-red-\u002Fnode-red\u002Fflowfuse\u002Fai\u002Fdepth-estimation":3},{"id":4,"title":5,"body":6,"description":527,"extension":528,"meta":529,"navigation":530,"path":531,"seo":532,"stem":533,"__hash__":534},"nodeRed\u002Fnode-red\u002Fflowfuse\u002Fai\u002Fdepth-estimation.md","Depth Estimation",{"type":7,"value":8,"toc":515},"minimark",[9,17,25,30,35,75,80,83,110,114,117,216,222,228,232,235,269,272,328,332,336,339,399,402,405,408,478,481,484,487,503,507,512],[10,11,13],"h1",{"id":12},"",[14,15],"binding",{"value":16},"meta.title",[18,19,20,21,24],"p",{},"The ",[22,23,5],"strong",{}," node allows you to estimate the relative distance of objects within an image using an ONNX model. It generates a depth map that represents how far each pixel is from the camera and can optionally create a visual image of the depth map using different color styles.",[26,27,29],"h2",{"id":28},"inputs","Inputs",[31,32,34],"h3",{"id":33},"general","General",[36,37,38,49,65],"ul",{},[39,40,41,44,45],"li",{},[22,42,43],{},"Property:"," ",[46,47,48],"code",{},"input",[39,50,51,44,54,57,58,57,61,64],{},[22,52,53],{},"Type:",[46,55,56],{},"object",", ",[46,59,60],{},"buffer",[46,62,63],{},"string"," or tensor.",[39,66,67,70,71,74],{},[22,68,69],{},"Description:"," The input image or tensor to classify. See the ",[22,72,73],{},"Details"," section for supported input formats.",[76,77,79],"h5",{"id":78},"supported-input-formats","Supported Input Formats",[18,81,82],{},"Typically, the input would be an image which could be:",[36,84,85,100,103],{},[39,86,87,88,91,92,95,96,99],{},"A ",[46,89,90],{},"Buffer"," object containing the binary image data (e.g. from a ",[46,93,94],{},"file"," node or ",[46,97,98],{},"http request"," node)",[39,101,102],{},"A base64-encoded string.",[39,104,105,106,109],{},"A Jimp image object (e.g, output from ",[46,107,108],{},"node-red-contrib-image-tools",").",[76,111,113],{"id":112},"tensor-input","Tensor input",[18,115,116],{},"Alternatively, you can supply a pre-processed tensor in the following format:",[118,119,123],"pre",{"className":120,"code":121,"language":122,"meta":12,"style":12},"language-json shiki shiki-themes github-light github-dark","{\n  \"data\": [0.0, 0.1, 0.2, ...],\n  \"type\": \"float32\",\n  \"dim\": [1, 3, 224, 224]\n}\n","json",[46,124,125,134,166,182,210],{"__ignoreMap":12},[126,127,130],"span",{"class":128,"line":129},"line",1,[126,131,133],{"class":132},"sVt8B","{\n",[126,135,137,141,144,147,149,152,154,157,159,163],{"class":128,"line":136},2,[126,138,140],{"class":139},"sj4cs","  \"data\"",[126,142,143],{"class":132},": [",[126,145,146],{"class":139},"0.0",[126,148,57],{"class":132},[126,150,151],{"class":139},"0.1",[126,153,57],{"class":132},[126,155,156],{"class":139},"0.2",[126,158,57],{"class":132},[126,160,162],{"class":161},"s7hpK","...",[126,164,165],{"class":132},"],\n",[126,167,169,172,175,179],{"class":128,"line":168},3,[126,170,171],{"class":139},"  \"type\"",[126,173,174],{"class":132},": ",[126,176,178],{"class":177},"sZZnC","\"float32\"",[126,180,181],{"class":132},",\n",[126,183,185,188,190,193,195,198,200,203,205,207],{"class":128,"line":184},4,[126,186,187],{"class":139},"  \"dim\"",[126,189,143],{"class":132},[126,191,192],{"class":139},"1",[126,194,57],{"class":132},[126,196,197],{"class":139},"3",[126,199,57],{"class":132},[126,201,202],{"class":139},"224",[126,204,57],{"class":132},[126,206,202],{"class":139},[126,208,209],{"class":132},"]\n",[126,211,213],{"class":128,"line":212},5,[126,214,215],{"class":132},"}\n",[18,217,218,219,109],{},"This represents a flat array of pixel values, the data type of the tensor, and its dimensions (for example, ",[46,220,221],{},"[batch_size, channels, height, width]",[223,224,225],"blockquote",{},[18,226,227],{},"TIP: If the model supports batching, the input can be an array of images in one of the supported formats.",[26,229,231],{"id":230},"model-selection","Model Selection",[18,233,234],{},"You can specify the model in two ways:",[36,236,237,248],{},[39,238,239,240,243,244,247],{},"Provide a ",[22,241,242],{},"local path"," (for example, ",[46,245,246],{},"\u002Fdata\u002Fmodels\u002Fresnet50.onnx","), or",[39,249,250,251,254,255,243,264,109],{},"Specify a ",[22,252,253],{},"model name"," available on ",[22,256,257],{},[258,259,263],"a",{"href":260,"rel":261},"https:\u002F\u002Fhuggingface.co\u002Fmodels?pipeline_tag=depth-estimation&library=transformers.js,onnx&sort=trending",[262],"nofollow","Hugging Face",[258,265,268],{"href":266,"rel":267},"https:\u002F\u002Fhuggingface.co\u002FXenova\u002Fdepth-anything-small-hf",[262],"Xenova\u002Fdepth-anything-small-hf",[18,270,271],{},"When specifying a model by name, you can define the data type to use when loading it. Supported types include:",[36,273,274,280,286,292,298,304,310,316,322],{},[39,275,276,279],{},[46,277,278],{},"auto"," — Automatically selects the most suitable type.",[39,281,282,285],{},[46,283,284],{},"fp32"," — Standard 32-bit floating-point model.",[39,287,288,291],{},[46,289,290],{},"fp16"," — Half-precision 16-bit floating-point model.",[39,293,294,297],{},[46,295,296],{},"int8"," — 8-bit integer quantized model.",[39,299,300,303],{},[46,301,302],{},"uint8"," — 8-bit unsigned integer model.",[39,305,306,309],{},[46,307,308],{},"q8"," — Quantized Int8 model (default).",[39,311,312,315],{},[46,313,314],{},"q4"," — Quantized Int4 model.",[39,317,318,321],{},[46,319,320],{},"q4f16"," — Quantized Int4 with Float16 model.",[39,323,324,327],{},[46,325,326],{},"bnb4"," — BNB4 quantized model.",[26,329,331],{"id":330},"configuration","Configuration",[31,333,335],{"id":334},"output-image","Output Image",[18,337,338],{},"If enabled, the node generates a visual representation of the depth map based on the selected style and alpha values.\nThe output will include both the raw depth data and a generated image:",[118,340,342],{"className":120,"code":341,"language":122,"meta":12,"style":12},"{\n  \"data\": { ... },\n  \"image\": \"Buffer\",\n  \"width\": 640,\n  \"height\": 480\n}\n",[46,343,344,348,360,372,384,394],{"__ignoreMap":12},[126,345,346],{"class":128,"line":129},[126,347,133],{"class":132},[126,349,350,352,355,357],{"class":128,"line":136},[126,351,140],{"class":139},[126,353,354],{"class":132},": { ",[126,356,162],{"class":161},[126,358,359],{"class":132}," },\n",[126,361,362,365,367,370],{"class":128,"line":168},[126,363,364],{"class":139},"  \"image\"",[126,366,174],{"class":132},[126,368,369],{"class":177},"\"Buffer\"",[126,371,181],{"class":132},[126,373,374,377,379,382],{"class":128,"line":184},[126,375,376],{"class":139},"  \"width\"",[126,378,174],{"class":132},[126,380,381],{"class":139},"640",[126,383,181],{"class":132},[126,385,386,389,391],{"class":128,"line":212},[126,387,388],{"class":139},"  \"height\"",[126,390,174],{"class":132},[126,392,393],{"class":139},"480\n",[126,395,397],{"class":128,"line":396},6,[126,398,215],{"class":132},[18,400,401],{},"If disabled, only the raw depth data will be included in the output.",[31,403,404],{"id":404},"style",[18,406,407],{},"Specifies the color map used when creating the depth visualization.",[18,409,410,411,57,414,57,417,57,420,57,423,57,426,57,429,57,432,57,435,57,438,57,441,57,444,57,447,57,450,57,453,57,456,57,459,57,462,57,465,57,468,57,471,57,474,477],{},"Available options include:\n",[46,412,413],{},"grayscale",[46,415,416],{},"jet",[46,418,419],{},"hot",[46,421,422],{},"hsv",[46,424,425],{},"spring",[46,427,428],{},"summer",[46,430,431],{},"autumn",[46,433,434],{},"winter",[46,436,437],{},"bone",[46,439,440],{},"copper",[46,442,443],{},"viridis",[46,445,446],{},"inferno",[46,448,449],{},"magma",[46,451,452],{},"plasma",[46,454,455],{},"rainbow",[46,457,458],{},"cool",[46,460,461],{},"warm",[46,463,464],{},"earth",[46,466,467],{},"blackbody",[46,469,470],{},"electric",[46,472,473],{},"velocity-blue",[46,475,476],{},"velocity-green",", and many more.",[18,479,480],{},"These styles correspond to common colormaps used in computer vision to represent depth or heat data.",[31,482,483],{"id":483},"alpha",[18,485,486],{},"Defines the transparency of the generated depth image.\nYou can use either a single value or an array of two values:",[36,488,489,496],{},[39,490,491,492,495],{},"A single value (e.g., ",[46,493,494],{},"0.5",") applies a uniform transparency.",[39,497,498,499,502],{},"An array ",[46,500,501],{},"[0.3, 0.8]"," defines a transparency range from the nearest (0.3) to farthest (0.8) objects.",[26,504,506],{"id":505},"example-flow","Example 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