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JeVoisBase
1.20
JeVois Smart Embedded Machine Vision Toolkit Base Modules
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JeVoisBase
1.20
JeVois Smart Embedded Machine Vision Toolkit Base Modules
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Functions | |
| def | str2bool (v) |
| def | visualize (image, results, box_color=(0, 255, 0), text_color=(0, 0, 255), fps=None) |
| def | get_color_map_list (num_classes) |
| def | visualize (image, result, weight=0.6, fps=None) |
| def | visualize (image, bbox, score, isLocated, fps=None, box_color=(0, 255, 0), text_color=(0, 255, 0), fontScale=1, fontSize=1) |
| def | readImageFromDirectory (img_dir, w=128, h=256) |
| def | visualize (image, res, points, points_color=(0, 255, 0), text_color=(0, 255, 0), fps=None) |
| def | visualize (image, results, box_color=(0, 255, 0), text_color=(0, 0, 255), isClosed=True, thickness=2, fps=None) |
Variables | |
| list | backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA] |
| list | targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16] |
| string | help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA" |
| string | help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16" |
| parser = argparse.ArgumentParser(description='YuNet: A Fast and Accurate CNN-based Face Detector (https://github.com/ShiqiYu/libfacedetection).') | |
| type | |
| str | |
| help | |
| default | |
| int | |
| float | |
| False | |
| str2bool | |
| True | |
| args = parser.parse_args() | |
| model | |
| image = cv.imread(args.input) | |
| h = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT)) | |
| w = int(cap.get(cv.CAP_PROP_FRAME_WIDTH)) | |
| results = model.infer(image) | |
| int | deviceId = 0 |
| cap = cv.VideoCapture(deviceId) | |
| tm = cv.TickMeter() | |
| hasFrame | |
| frame = visualize(frame, results, fps=tm.getFPS()) | |
| choices | |
| recognizer = SFace(modelPath=args.model, disType=args.dis_type, backendId=args.backend, targetId=args.target) | |
| detector | |
| img1 = cv.imread(args.input1) | |
| img2 = cv.imread(args.input2) | |
| face1 = detector.infer(img1) | |
| face2 = detector.infer(img2) | |
| result = recognizer.match(img1, face1[0][:-1], img2, face2[0][:-1]) | |
| dictionary | models |
| int | device_id = 0 |
| video = cv.VideoCapture(_input) | |
| has_frame | |
| first_frame | |
| first_frame_copy = first_frame.copy() | |
| roi = cv.selectROI('DaSiamRPN Demo', first_frame_copy) | |
| isLocated | |
| bbox | |
| score | |
| palm_box | |
| palm_landmarks | |
| net = YoutuReID(modelPath=args.model, backendId=args.backend, targetId=args.target) | |
| query_img_list | |
| query_file_list | |
| gallery_img_list | |
| gallery_file_list | |
| topk_indices = net.query(query_img_list, gallery_img_list, args.topk) | |
| list | topk_matches = [] |
| def | results_vis = visualize(results, args.query_dir, args.gallery_dir) |
| res | |
| points | |
| fps = tm.getFPS() | |
| float64 | |
| list | texts = [] |
| def demo.get_color_map_list | ( | num_classes | ) |
Returns the color map for visualizing the segmentation mask,
which can support arbitrary number of classes.
Args:
num_classes (int): Number of classes.
Returns:
(list). The color map.
Definition at line 43 of file demo.py.
Referenced by visualize().
| def demo.readImageFromDirectory | ( | img_dir, | |
w = 128, |
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h = 256 |
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| ) |
Definition at line 47 of file demo.py.
References visualize().
| def demo.visualize | ( | image, | |
| bbox, | |||
| score, | |||
| isLocated, | |||
fps = None, |
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box_color = (0, 255, 0), |
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text_color = (0, 255, 0), |
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fontScale = 1, |
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fontSize = 1 |
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| ) |
Definition at line 32 of file demo.py.
References int, and visualize().
| def demo.visualize | ( | image, | |
| res, | |||
| points, | |||
points_color = (0, 255, 0), |
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text_color = (0, 255, 0), |
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fps = None |
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| ) |
Definition at line 33 of file demo.py.
References int, and visualize().
| def demo.visualize | ( | image, | |
| result, | |||
weight = 0.6, |
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fps = None |
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| ) |
Convert predict result to color image, and save added image.
Args:
image (str): The input image.
result (np.ndarray): The predict result of image.
weight (float): The image weight of visual image, and the result weight is (1 - weight). Default: 0.6
fps (str): The FPS to be drawn on the input image.
Returns:
vis_result (np.ndarray): The visualized result.
Definition at line 69 of file demo.py.
References get_color_map_list(), int, and visualize().
| def demo.visualize | ( | image, | |
| results, | |||
box_color = (0, 255, 0), |
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text_color = (0, 0, 255), |
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fps = None |
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| ) |
Definition at line 46 of file demo.py.
Referenced by readImageFromDirectory(), and visualize().
| def demo.visualize | ( | image, | |
| results, | |||
box_color = (0, 255, 0), |
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text_color = (0, 0, 255), |
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isClosed = True, |
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thickness = 2, |
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fps = None |
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| ) |
Definition at line 51 of file demo.py.
References visualize().
| list demo.backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA] |
| demo.detector |
| demo.float |
Definition at line 39 of file demo.py.
Referenced by RoadFinder.computeRoadCenterPoint(), PyCoralDetect.PyCoralDetect.get_objects(), PyDetectionDNN.PyDetectionDNN.postprocess(), Yolo.predict(), Darknet.predict(), PyPostDAMOyolo.PyPostDAMOyolo.process(), DarknetSaliency.process(), TensorFlowSaliency.process(), PyLicensePlate.PyLicensePlate.processGUI(), and PySceneText.PySceneText.processGUI().
Definition at line 120 of file demo.py.
Referenced by DetectionDNN.postprocess(), BufferedVideoReader.run(), SurpriseRecorder.run(), and SaveVideo.run().
| string demo.help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA" |
| string demo.help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16" |
| def demo.image = cv.imread(args.input) |
Definition at line 84 of file demo.py.
Referenced by ARtoolkit.detectMarkers(), ArUco.detectMarkers(), Yolo.predict(), Darknet.predict(), QRcode.process(), and DiceCounter.process().
| demo.img1 = cv.imread(args.input1) |
Definition at line 62 of file demo.py.
Referenced by env_img_swap().
| demo.img2 = cv.imread(args.input2) |
Definition at line 63 of file demo.py.
Referenced by env_img_swap().
| demo.int |
Definition at line 37 of file demo.py.
Referenced by transform.CenterCrop.__call__(), utils.metrics.base_metric.BaseMetric.__init__(), utils.dataloaders.classification.ClassificationImageLoader.__init__(), PyPoseDetector.calc_bounding_rect(), PyFaceMesh.PyFaceMesh.calc_bounding_rect(), PyHandDetector.PyHandDetector.calc_bounding_rect(), PyHandDetector.PyHandDetector.calc_palm_moment(), RoadFinder.computeVanishingLines(), FirstVision.detect(), PyFaceMesh.PyFaceMesh.draw_landmarks(), PyPoseDetector.draw_landmarks(), PyHandDetector.PyHandDetector.draw_landmarks(), ArUco.drawDetections(), PythonObject6D.PythonObject6D.drawDetections(), FirstPython.FirstPython.drawDetections(), FirstVision.drawDetections(), FirstVision.estimatePose(), PyCoralDetect.PyCoralDetect.get_objects(), utils.dataloaders.base_dataloader._VideoStream.getFrameSize(), RoadFinder.getLineFitness(), DarknetSaliency.getSalROI(), TensorFlowSaliency.getSalROI(), lpd_yunet.LPD_YuNet.infer(), PyDetectionDNN.PyDetectionDNN.postprocess(), DetectionDNN.postprocess(), download_data.Downloader.printRequest(), PyPreBlob.PyPreBlob.process(), PyNetOpenCV.PyNetOpenCV.process(), DenseSift.process(), DemoSaliency.process(), BurnTest.process(), RoadFinder.process(), ObjectTracker.process(), ArUcoBlob.process(), PyPostDepth.PyPostDepth.report(), PyPostURetinex.PyPostURetinex.report(), and visualize().
| dictionary demo.model |
| dictionary demo.models |
| demo.net = YoutuReID(modelPath=args.model, backendId=args.backend, targetId=args.target) |
| demo.parser = argparse.ArgumentParser(description='YuNet: A Fast and Accurate CNN-based Face Detector (https://github.com/ShiqiYu/libfacedetection).') |
| demo.points |
Definition at line 68 of file demo.py.
Referenced by RoadFinder.combine(), RoadFinder.findLine2(), RoadFinder.fitLine(), RoadFinder.getLineFitness(), RoadFinder.getPixels(), RoadFinder.getPixelsQuick(), RoadFinder.trackVanishingLines(), and RoadFinder.updateLine().
| demo.recognizer = SFace(modelPath=args.model, disType=args.dis_type, backendId=args.backend, targetId=args.target) |
| demo.res |
Definition at line 68 of file demo.py.
Referenced by SuperPixel.process(), ObjectRecognitionMNIST.train(), ObjectRecognitionILAB.train(), and ObjectRecognitionCIFAR.train().
Definition at line 74 of file demo.py.
Referenced by ARtoolkit.detectInternal(), env_chan_color(), env_chan_intensity(), env_chan_orientation(), env_chan_steerable(), env_dec_x(), env_dec_xy(), env_dec_y(), env_lowpass_5_x_dec_x(), env_lowpass_5_y_dec_y(), env_motion_channel_input_and_consume_pyr(), env_rescale(), ObjectRecognitionMNIST.train(), ObjectRecognitionILAB.train(), and ObjectRecognitionCIFAR.train().
| demo.results = model.infer(image) |
Definition at line 89 of file demo.py.
Referenced by TensorFlow.predict(), Darknet.predict(), and QRcode.process().
| demo.roi = cv.selectROI('DaSiamRPN Demo', first_frame_copy) |
Definition at line 75 of file demo.py.
Referenced by SaliencySURF.process().
| demo.score |
Definition at line 90 of file demo.py.
Referenced by RoadFinder.computeVanishingLines(), RoadFinder.findLine2(), RoadFinder.getLineFitness(), Darknet.predict(), RoadFinder.trackVanishingLines(), and RoadFinder.updateLine().
| demo.str |
Definition at line 35 of file demo.py.
Referenced by FirstVision.detect(), PyPoseDetector.draw_landmarks(), ObjectDetect.parseSerial(), SaveVideo.parseSerial(), ArUcoBlob.process(), and benchmark.Benchmark.run().
| def demo.str2bool |
Definition at line 43 of file demo.py.
Referenced by str2bool().
| list demo.targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16] |
| demo.topk_indices = net.query(query_img_list, gallery_img_list, args.topk) |
Definition at line 85 of file demo.py.
Referenced by QRcode.drawDetections(), FirstVision.drawDetections(), drawMap(), env_c_lowpass_5_x_dec_x_fewbits_optim(), env_c_lowpass_5_y_dec_y_fewbits_optim(), env_c_lowpass_9_x_fewbits_optim(), env_c_lowpass_9_y_fewbits_optim(), env_lowpass_5_x_dec_x(), env_lowpass_5_y_dec_y(), TensorFlow.getInDims(), Darknet.getInDims(), Yolo.getInDims(), RoadFinder.getPixels(), RoadFinder.getPixelsQuick(), DarknetSaliency.getSalROI(), TensorFlowSaliency.getSalROI(), FirstVision.learnHSV(), FirstVision.loadCameraCalibration(), ARtoolkit.manualinit(), Yolo.predict(), Darknet.predict(), SuperPixelSeg.process(), OpticalFlow.process(), DiceCounter.process(), DenseSift.process(), DemoBackgroundSubtract.process(), SaliencyGist.process(), SalientRegions.process(), DemoSaliency.process(), DemoQRcode.process(), DemoCPUGPU.process(), DemoIMU.process(), BurnTest.process(), RoadNavigation.process(), DemoARtoolkit.process(), MarkersCombo.process(), DNN.process(), ColorFiltering.process(), DemoNeon.process(), DarknetYOLO.process(), DarknetSingle.process(), SaliencySURF.process(), DemoDMP.process(), DemoArUco.process(), ObjectTracker.process(), SurpriseRecorder.process(), TensorFlowEasy.process(), TensorFlowSingle.process(), DarknetSaliency.process(), TensorFlowSaliency.process(), ObjectDetect.process(), SaveVideo.process(), ArUcoBlob.process(), DetectionDNN.process(), FirstVision.process(), Darknet.resizeInDims(), Yolo.resizeInDims(), FirstVision.sendAllSerial(), ArUcoBlob.sendBlobs(), and ArUco.sendSerial().
1.8.17