Use validBitIdThreshold for Aruco refineDetectedMarkers #29252
The goal of this PR is to solve the issue raised by @vrabaud in https://github.com/opencv/opencv/pull/28289 (comment: https://github.com/opencv/opencv/pull/28289#discussion_r3355812646).
**Issue:**
`refineDetectedMarkers()` converted the extracted cell ratios with `convertTo(CV_8UC1)` (an implicit 0.5 threshold) before computing the code distance, ignoring `detectorParams.validBitIdThreshold`.
**Solution:**
Make the refine path consistent with the main detection path `Dictionary::identify`.
**Changes:**
- Add a `Dictionary::getDistanceToId()` overload that takes the float cell pixel ratio matrix and `validBitIdThreshold` (similar to how it's done for the `identify()` overload.
- Move the per cell distance computation into a private `getDistanceToIdImpl` helper used by both `identify()` and the new overload of `getDistanceToId()` to avoid repetitions.
- `refineDetectedMarkers()` now calls the new overload.
**Tests:**
- `CV_ArucoRefine.validBitIdThreshold`: a marker with one degraded cell is recovered at threshold 0.7 but not at 0.49.
- `CV_ArucoDictionary.getDistanceToIdCellPixelRatio`: unit-tests both `getDistanceToId` overloads.
All passed
Update ArUco doc #28824
See https://github.com/opencv/opencv/pull/28823
Update of the ArUco doc but targeted for OpenCV 4.
### Pull Request Readiness Checklist
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Identify ArUco markers based on threshold to reduce false positives #28289
**Goal:** parametrize the current marker identification process (pixel-based majority count) to reduce the number of false positives while maintaining high recall. Useful in high risk scenarios in which false positives are not acceptable.
**Context:** This PR builds on top of https://github.com/opencv/opencv/pull/23190 in which we've introduced a pixel-based confidence in the marker detection.
**Solution:** Include a new parameter: `validBitIdThreshold` used to identify markers based on the pixel count of each cell. Set the parameter default either to 50% which is equivalent to the current majority count implementation or to 49% which already singnificantly reduces the number of false positives (see details below).
**Test coverage:**
- Unit tests: `CV_ArucoDetectionThreshold`, `CV_InvertedArucoDetectionThreshold`
- The impact of `validBitIdThreshold` on false positives was also tested using the benchmark dataset: `MIRFLICKR-25k` https://www.kaggle.com/datasets/skfrost19/mirflickr25k which contains random images without any markers. Every marker detection is a false positive.
Example of images in the dataset:
**Results:** A threshold of 49% already allows to significantly reduce the number of false positives for the dict `DICT_4X4_1000`:
- `5942` false positives for `validBitIdThreshold = 0.5`
- `629` false positives for `validBitIdThreshold = 0.49` and `0.46`
- number of false positives divided by `9.5` when compared to `validBitIdThreshold = 0.5`
- `139` false positives for `validBitIdThreshold = 0.43` and `0.4`
- number of false positives divided by `42` when compared to `validBitIdThreshold = 0.5`
Dicts with a higher number of cells are not as impacted since it's much harder to obtain false positives. However, the less cells in a marker the further away it can be reliably detected, so the dict `DICT_4X4_1000` is commonly used.
<img width="1280" height="800" alt="false_positive_image_rate" src="https://github.com/user-attachments/assets/1a0ee16a-221d-443e-835b-022ed6dea6b0" />
In the image attached, the values of `validBitIdThreshold` tested are: `0.10f, 0.20f, 0.30f, 0.40f, 0.43f, 0.46f, 0.49f, 0.50f, 0.53f, 0.56f, 0.60f, 0.70f, 0.80f, 0.90f`
Summary of the results: [summary.csv](https://github.com/user-attachments/files/24315662/summary.csv)
Note that we can also analyse the number of false positives per marker `id`. For example, here's the histogram for the dict `DICT_4X4_1000`. (The CSV attached contains all the results)
<img width="1440" height="640" alt="false_positive_ids_DICT_4X4_1000_thr0 50" src="https://github.com/user-attachments/assets/af4f3ff8-9b8f-4682-9d51-a090c2610d8c" />
For example, the marker id 17 is detected 252 times with `validBitIdThreshold = 0.5` and only 34 times with `validBitIdThreshold = 0.49`. Looking at marker 17 (see below), we understand that this simple pattern randomly occurs in images.
<img width="447" height="441" alt="Marker17" src="https://github.com/user-attachments/assets/f5d09227-b39b-4598-94f9-b529f8300703" />
Results for every dict and every `validBitIdThreshold` [per_id.csv](https://github.com/user-attachments/files/24315667/per_id.csv)
**Missing coverage:** there is no labeled dataset with images containing markers to analyse the impact of on the recall (i.e. look at the true positive rate). For my specific use case (drones) any threshold above `0.4` allows to maintain a high recall in all conditions.
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docs: fix spelling errors in documentation and code #28301
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### Description
Fixed multiple spelling errors across documentation, comments, and code:
- 'colummn' → 'column' (cublas.hpp, 3 occurrences)
- 'points_per_colum' → 'points_per_column' (calib3d.hpp, 3 occurrences)
- 'Asignee' → 'Assignee' (sift files, 2 occurrences)
- 'compability' → 'compatibility' (face.hpp, 2 occurrences)
- 'orignal' → 'original' (aruco_detector.cpp)
- 'refrence' → 'reference' (chessboard.cpp)
- 'indeces' → 'indices' (stitching.hpp)
- 'OutputPrecison' → 'OutputPrecision' (test)
- 'tranform' → 'transform' (slice_layer.cpp, 3 occurrences)
Total: 24 fixes across 14 files. Documentation and comment changes only, no functional impact.
Include pixel-based confidence in ArUco marker detection #23190
The aim of this pull request is to compute a **pixel-based confidence** of the marker detection. The confidence [0;1] is defined as the percentage of correctly detected pixels, with 1 describing a pixel perfect detection. Currently it is possible to get the normalized Hamming distance between the detected marker and the dictionary ground truth [Dictionary::getDistanceToId()](https://github.com/opencv/opencv/blob/4.x/modules/objdetect/src/aruco/aruco_dictionary.cpp#L114) However, this distance is based on the extracted bits and we lose information in the [majority count step](https://github.com/opencv/opencv/blob/4.x/modules/objdetect/src/aruco/aruco_detector.cpp#L487). For example, even if each cell has 49% incorrect pixels, we still obtain a perfect Hamming distance.
**Implementation tests**: Generate 36 synthetic images containing 4 markers each (with different ids) so a total of 144 markers. Invert a given percentage of pixels in each cell of the marker to simulate uncertain detection. Assuming a perfect detection, define the ground truth uncertainty as the percentage of inverted pixels. The test is passed if `abs(computedConfidece - groundTruthConfidence) < 0.05` where `0.05` accounts for minor detection inaccuracies.
- Performed for both regular and inverted markers
- Included perspective-distorted markers
- Markers in all 4 possible rotations [0, 90, 180, 270]
- Different set of detection params:
- `perspectiveRemovePixelPerCell`
- `perspectiveRemoveIgnoredMarginPerCell`
- `markerBorderBits`
The code properly builds locally and `opencv_test_objdetect` and `opencv_test_core` passed. Please let me know if there are any further modifications needed.
Thanks!
I've also pushed minor unrelated improvement (let me know if you want a separate PR) in the [bit extraction method](https://github.com/opencv/opencv/blob/4.x/modules/objdetect/src/aruco/aruco_detector.cpp#L435). `CV_Assert(perspectiveRemoveIgnoredMarginPerCell <=1)` should be `< 0.5`. Since there are margins on both sides of the cell, the margins must be smaller than half of the cell. When setting `perspectiveRemoveIgnoredMarginPerCell >= 0.5`, `opencv_test_objdetect` fails. Note: 0.499 is ok because `int()` will floor the result, thus `cellMarginPixels = int(cellMarginRate * cellSize)` will be smaller than `cellSize / 2`
### Pull Request Readiness Checklist
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Consider QRCode ECI encoding #24426
### Pull Request Readiness Checklist
related: https://github.com/opencv/opencv/pull/24350#pullrequestreview-1661658421
1. Add `getEncoding` method to obtain ECI number
2. Add `detectAndDecodeBytes`, `decodeBytes`, `decodeBytesMulti`, `detectAndDecodeBytesMulti` methods in Python (return `bytes`) and Java (return `byte[]`)
3. Allow Python bytes to std::string conversion in general and add `encode(byte[] encoded_info, Mat qrcode)` in Java
Python example with Kanji encoding:
```python
img = cv.imread("test.png")
detect = cv.QRCodeDetector()
data, points, straight_qrcode = detect.detectAndDecodeBytes(img)
print(data)
print(detect.getEncoding(), cv.QRCodeEncoder_ECI_SHIFT_JIS)
print(data.decode("shift-jis"))
```
```
b'\x82\xb1\x82\xf1\x82\xc9\x82\xbf\x82\xcd\x90\xa2\x8aE'
20 20
こんにちは世界
```
source: https://github.com/opencv/opencv/blob/ba4d6c859d21536f84e0328c16f4cc3e96bf3065/modules/objdetect/test/test_qrcode_encode.cpp#L332
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Explicitly specify enum type scopes to improve Java wrapper generation #27228
Changed DataLayout and ImagePaddingMode to dnn::DataLayout and dnn::ImagePaddingMode to explicitly specify their scopes. This allows gen_java.py to correctly register disc_type, preventing constructors and methods using these enum types from being skipped during Java wrapper generation.
Similarly updated QRCodeEncoder::CorrectionLevel and QRCodeEncoder::EncodeMode with explicit scope declarations.
Also added a new Java test class `DnnBlobFromImageWithParamsTest` based on: https://github.com/opencv/opencv/blob/4.x/modules/dnn/test/test_misc.cpp#L133-L243
Related issues
#23753
### Pull Request Readiness Checklist
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- Implemented a new `create` method in `FaceRecognizerSF` to allow model and configuration loading from memory buffers (std::vector<uchar>), similar to the existing functionality in `FaceDetectorYN`.
- Updated `face_recognize.cpp` with a new constructor in `FaceRecognizerSFImpl` that supports buffer-based loading for both model weights and network configuration.
- Ensured compatibility with both file-based and buffer-based model loading by maintaining consistent backend and target settings across both constructors.
- This change improves flexibility, allowing FaceRecognizerSF to be instantiated from memory buffers, which is useful for dynamic model loading scenarios such as embedded systems or applications where models are loaded in-memory.
Feature barcode detector parameters #24903
Attempt to solve #24902 without changing the default detector behaviour.
Megre with extra: https://github.com/opencv/opencv_extra/pull/1150
**Introduces new parameters and methods to `cv::barcode::BarcodeDetector`**.
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Documentation transition to fresh Doxygen #25042
* current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584)
* Doxyfile adapted to new version
* MathJax updated to 3.x
* `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings)
* refactored matx.hpp - extracted matx.inl.hpp
* opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
Move Aruco tutorials and samples to main repo #23018
merge with https://github.com/opencv/opencv_contrib/pull/3401
merge with https://github.com/opencv/opencv_extra/pull/1143
### Pull Request Readiness Checklist
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---------
Co-authored-by: AleksandrPanov <alexander.panov@xperience.ai>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
QR codes Structured Append decoding mode #24548
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23245
Merge after https://github.com/opencv/opencv/pull/24299
Current proposal is to use `detectAndDecodeMulti` or `decodeMulti` for structured append mode decoding. 0-th QR code in a sequence gets a full message while the rest of codes will correspond to empty strings.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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python: accept path-like objects wherever file names are expected #24773
Merry Christmas, all 🎄
Implements #15731
Support is enabled for all arguments named `filename` or `filepath` (case-insensitive), or annotated with `CV_WRAP_FILE_PATH`.
Support is based on `PyOS_FSPath`, which is available in Python 3.6+. When running on older Python versions the arguments must have a `str` value as before.
### Pull Request Readiness Checklist
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Fix detect diamonds api #23848
`detectDiamonds` cannot be called from python, reproducer:
```
import numpy as np
import cv2 as cv
detector = cv.aruco.CharucoDetector(
cv.aruco.CharucoBoard(
(3, 3), 200.0, 100.0,
cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250)
)
)
image = np.zeros((640, 480, 1), dtype=np.uint8)
res = detector.detectDiamonds(image)
print(res)
```
The error in `detectDiamonds` API fixed by replacing `InputOutputArrayOfArrays markerIds` with `InputOutputArray markerIds`.
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added Aruco MIP dictionaries #23785
added Aruco MIP dictionaries: DICT_ARUCO_MIP_16h3, DICT_ARUCO_MIP_25h7, DICT_ARUCO_MIP_36h12 from [Aruco.js](https://github.com/damianofalcioni/js-aruco2), converted in opencv format using https://github.com/damianofalcioni/js-aruco2/blob/master/src/dictionaries/utils/dic2opencv.js
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Fix python bindings for setCharucoParameters #23436
setCharucoParameters fails in python
Fixes: https://github.com/opencv/opencv/issues/23440
### Pull Request Readiness Checklist
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Add notes for the output format of FaceDetectorYN.detect()
Resolves https://github.com/opencv/opencv/pull/23020#issuecomment-1499010015
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merge with https://github.com/opencv/opencv_contrib/pull/3394
move Charuco API from contrib to main repo:
- add CharucoDetector:
```
CharucoDetector::detectBoard(InputArray image, InputOutputArrayOfArrays markerCorners, InputOutputArray markerIds,
OutputArray charucoCorners, OutputArray charucoIds) const // detect charucoCorners and/or markerCorners
CharucoDetector::detectDiamonds(InputArray image, InputOutputArrayOfArrays _markerCorners,
InputOutputArrayOfArrays _markerIds, OutputArrayOfArrays _diamondCorners,
OutputArray _diamondIds) const
```
- add `matchImagePoints()` for `CharucoBoard`
- remove contrib aruco dependencies from interactive-calibration tool
- move almost all aruco tests to objdetect
### Pull Request Readiness Checklist
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Megre together with https://github.com/opencv/opencv_contrib/pull/3325
1. Move aruco_detector, aruco_board, aruco_dictionary, aruco_utils to objdetect
1.1 add virtual Board::draw(), virtual ~Board()
1.2 move `testCharucoCornersCollinear` to Board classes (and rename to `checkCharucoCornersCollinear`)
1.3 add wrappers to keep the old api working
3. Reduce inludes
4. Fix java tests (add objdetect import)
5. Refactoring
### Pull Request Readiness Checklist
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```
**WIP**
force_builders=linux,win64,docs,Linux x64 Debug,Custom
Xbuild_contrib:Docs=OFF
build_image:Custom=ubuntu:22.04
build_worker:Custom=linux-1
```
Jonas Perolini
s-trinh
Murat Raimbekov
Sergei Nikiforov
Dmitry Kurtaev
utibenkei
Benjamin Knecht
quantizs
Patrick Keane
Cristian Dobre
Maksim Shabunin
Sergei Shutov
Alex
lpanaf
Stefan Dragnev
Alexander Smorkalov
Alexander Smorkalov
Sean McBride
Damiano Falcioni
zihaomu
Stefan Becker
Alex
Milan van Wouden
keith siilats
Yuantao Feng
Alexander Alekhin
Alexander Alekhin
