Alfonso Blanco.'s repositories
LicensePlate_Yolov8_Filters_PaddleOCR
Recognition of license plate numbers, in any format, by automatic detection with Yolov8, pipeline of filters and paddleocr as OCR
LicensePlate_Yolov8_MaxFilters
LicensePlate_Yolov8_MaxFilters: recognition of car license plates that are detected by Yolov8 and recognized with pytesseract after processing with a pipeline of filters choosing the most repeated car license plate. In a test with 21 images, 18 hits are achieved
ablanco1950-LicensePlate_FindContours_And_Haarcascade
Using together cv2's findcontours and Haarcascade license plate detection together with the application of an extensive set of filters
CarsBrands_Inceptionv3
Project that detects the brand of a car, between 1 and 49 brands, that appears in a photograph, with a success rate of more than 70% (using a test file that has not been involved in the training as a valid or training file, "unseen data") and can be implemented on a personal computer
DetectSpeedLicensePlate_Yolov8_Filters_PaddleOCR
This work is an extension of the project https://github.com/ablanco1950/LicensePlate_Yolov8_Filters_PaddleOCR adding the possibility to detect the speed
LicensePlate_RoboflowAPI_Filters_PaddleOCR
This project detects the car license plate through a free Roboflow API, submits the detected car license plate image to a battery of filters and obtains the car license plate number using paddleOcr
LicensePlate_Yolov8_FilterCNN_PaddleOCR
Project that uses Yolov8 as license plate detector, followed by a filter that is got selecting from a filters collection with a code assigned to each filter and predicting what filter with a CNN process
LicenSePlate_Yolov8_FilterSVM_PaddleOCR
Project that uses Yolov8 as license plate detector, followed by a filter that is got selecting from a filters collection with a code assigned to each filter and predicting what filter with a SVM process
LicensePlateImage_ThresholdFiltered
From some files of images and labels obtained by applying the project presented at https://github.com/ashok426/Vehicle-number-plate-recognition-YOLOv5, the images of license plates are filtered through a threshold that allows a better recognition of the license plate numbers by pytesseract. On 05/23/2022, a new version is introduced. On 07/04/2022 an ML version es added
PointOutWristPositiveFracture_on_xray
Indicates the location of wrist fractures in x-rays through training with yolo v8 of roboflow images downloaded from https://www.kaggle.com/datasets/pkdarabi/bone-fracture-detection-computer-vision-project/code
DetectSpeedLicensePlate_RoboflowAPI_Filters_PaddleOCR
This work is an extension of the project https://github.com/ablanco1950/LicensePlate_RoboflowAPI_Filters_PaddleOCR adding the possibility to detect the speed
CarsBrands_Resnet_Pytorch
Project that detects the brand of a car, between 1 and 49 brands ( the 49 brands of Stanford car file), that appears in a photograph with a success rate of more than 80% (using a test file that has not been involved in the training as a valid or training file, "unseen data") and can be implemented on a personal computer.
CarsModels_Resnet_Pytorch
Project that detects the model of a car, between 1 and 196 models ( the 196 modelss of Stanford car file), that appears in a photograph with a success rate of more than 70% (using a test file that has not been involved in the training as a valid or training file, "unseen data") and can be implemented on a personal computer.
DetectCarDistanceAndRoadLane
Project that estimates the distance a car is on a road based on the relationship between the real size of the car and the size it appears in the video obtained. It also estimates the lane the car are traveling in at any given time based on the angle between the position of the car and camera, even guess lane change intentions
DetectTrafficSign
Creation of a model based on yolov8 that uses the file downloaded from https://www.kaggle.com/datasets/valentynsichkar/traffic-signs-dataset-in-yolo-format/data as a custom dataset to detect traffic signs. The detected signals can be recognized using the project https://github.com/ablanco1950/RecognizeTrafficSign.
Directs_Object_Following_Lane
Project that positions an object in a video following a road lane.
GuessImagesLFW_VGG16
Simple application of VGG16 for the recognition of images, obtained from LFW, of a limited number of famous(15) with good performance (greater than 80%)
LaneDetection_Template
Lane detection using cv.matchTemplate function, a simpler system than the one usually used to process the image and detect contours. Furthermore, it does not require establishing a region of interest.
LFW_SVM_facecascade
An image recognition process contained in the LFW database http://vis-www.cs.umass.edu/lfw/#download is carried out with extreme simplicity, taking advantage of the ease of sklearn to implement the SVM model. Cascading face recognition is also used to refine the images, obtaining accuracy greater than 70% in the test with images that do not appear in the training.
LFW_TwoModels
A recognition process of images contained in the LFW database http://vis-www.cs.umass.edu/lfw/#download is carried out using two models, one based on the minimum distance between training image records and test and another that is an adaptation of the CNN KERAS model https://keras.io/examples/vision/mnist_convnet/. Both models are complementary. A module is also incorporated that takes advantage of the facility of sklearn to implement the SVM model with great simplicity.
LicensePlate_CLAHE
Through the use of Contrast Limited Adaptive Histogram Equalization (CLAHE) filters, completed with otsu filters, a direct reading of car license plates with success rates above 70% and an acceptable time is achieved
LicensePlate_FindContours
A recognition licenses plates based in FindContours
LicensePlate_Labeled_MaxFilters
From images of cars in which their license plates have been labeled, and passing filters, their recognition is attempted by pytesseract . As there is not a single filter that works for all the licensess, it is tried with several filters and The license plate number that has been detected the most times is assigned.
LicensePlate_Wpod-net_MaxFilters
It's a Wpod-net demo, downloaded from https://github.com/quangnhat185/Plate_detect_and_recognize, for the recognition of car license plates, the use of labeled images is avoided, with lower accuracy
OCRFromScratch_Chars74K_SpanishLicensePlate
OCR from scratch using Chars74 Dataset: http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/ applied to the case of Spanish car license plates or any other with format NNNNAAA. The hit rate is lower than that achieved by pytesseract: in a test with 21 images, 12 hits are reached while with pytesseract the hits are 17.
OCRFromScratch_KaggleDataset_SpanishLicensePlate
OCR from scratch using Kaggle dataset dwonloaded from https://www.kaggle.com/code/preatcher/ocr-training applied to the case of Spanish car license plates or any other with format NNNNAAA. The hit rate is lower than that achieved by pytesseract: in a test with 21 images, 16 hits are reached while with pytesseract the hits are 17
RecognitionTrafficLight
Traffic light recognition
RecognizeTrafficSign
From traffic sign database downloaded from https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign It is produced using a CNN based on resnet and pytorch, a model to recognize traffic signs previously detected using the model produced at https://github.com/ablanco1950/DetectTrafficSign
SkinLesionDetection_Resnet_Pytorch
Detection of skin lesions (among 7 classes) using the file https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T and using the pytorch resnet model. The success rate for the specific test file (unseen data) that comes with the download file is 81.13%.