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If there are N results in total, the value range of `index` is [-N, N - 1], where negative numbers are converted to N - index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.
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- `method`: *int*
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Template matching algorithm, equivalent to cv::TemplateMatchModes. Optional, default is 5.
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Only supports 1, 3, and 5, with higher values providing greater accuracy but also taking more time.
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-`count`:*int*
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The number of required matching feature points (threshold), default is 4.
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-`order_by`:*string*
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How the results are sorted. Optional, default is `Horizontal`
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Possible values:`Horizontal`|`Vertical`|`Score`|`Area`|`Random`
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You can use it with the `index` field.
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-`index`:*int*
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Index to hit. Optional, default is `0`.
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If there are N results in total, the value range of`index` is [-N, N-1], where negative numbers are converted to N- index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.
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-`green_mask`:*bool*
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Whether to apply a green mask. Optional, default is false.
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If set to true, you can paint the unwanted parts in the image green withRGB: (0, 255, 0), and those green parts won't be matched.
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- `count`: *int*
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The threshold for the number of matching points required. Optional, default is 1.
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- `order_by`: *string*
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How the results are sorted. Optional, default is `Horizontal`
If there are N results in total, the value range of `index` is [-N, N - 1], where negative numbers are converted to N - index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.
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- `connected`: *bool*
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Whether to count only connected points. Optional, default is false.
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If set to true, after applying color filtering, it will only count the maximum connected block of pixels.
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Some text recognition results may not be accurate, so replacements are performed. Optional.
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-`order_by`:*string*
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How the results are sorted. Optional, default is `Horizontal`
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Possible values:`Horizontal`|`Vertical`|`Area`|`Length`|`Random`
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You can use it with the `index` field.
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-`index`:*int*
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Index to hit. Optional, default is `0`.
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If there are N results in total, the value range of`index` is [-N, N-1], where negative numbers are converted to N- index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.
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-`only_rec`:*bool*
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Whether to recognize only (without detection, requires precise `roi`). Optional, default is false.
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@@ -350,6 +386,15 @@ This task property requires additional fields:
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-`expected`:*int*|*list<int, >*
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The expected category index.
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-`order_by`:*string*
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How the results are sorted. Optional, default is `Horizontal`
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Possible values:`Horizontal`|`Vertical`|`Area`|`Random`
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You can use it with the `index` field.
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-`index`:*int*
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Index to hit. Optional, default is `0`.
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If there are N results in total, the value range of`index` is [-N, N-1], where negative numbers are converted to N- index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.
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For example, if you want to recognize whether a cat or a mouse appears in a **fixed position**in the image, and you've trained a model that supports this three-category classification, and you want to click when it recognizes a cat or a mouse but not when it recognizes a dog, the relevant fields would be:
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```jsonc
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Model confidence threshold. Optional, default is 0.3.
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If it's an array, its length should match the length of the `expected` array.
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-`order_by`:*string*
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How the results are sorted. Optional, default is `Horizontal`
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Possible values:`Horizontal`|`Vertical`|`Area`|`Random`
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You can use it with the `index` field.
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-`index`:*int*
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Index to hit. Optional, default is `0`.
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If there are N results in total, the value range of`index` is [-N, N-1], where negative numbers are converted to N- index using Python-like rules. If it exceeds the range, it is considered that there is no result in the current identification.
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For example, if you want to detect cats, dogs, and mice in an image and only click when a cat or a mouse is detected but not when a dog is detected, the relevant fields would be:
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