问Abaqus Python getByBoundingBox命令EN本文介绍了如何在abaqus中编写Python脚本，包括创建脚本的三种方法、...
'''xgo图形化python库'''importcv2importcv2 as cvimportcopyimportargparseimportnumpy as npimportmediapipe as mpimportshutil,requestsimporturllib.requestimportmathimportos,sys,time,loggingimportspidev as SPIimportLCD_2inchimportonnxruntimeimportRPi.GPIO as GPIOfromPILimportImage,ImageDraw,ImageFontfromctypes...
Source File: text.py From GraphicDesignPatternByPython with MIT License 6 votes def get_window_extent(self, renderer=None): ''' Return a :class:`~matplotlib.transforms.Bbox` object bounding the text, in display units. In addition to being used internally, this is useful for specifying ...
The parameters 'bbox_heads_weight' and 'class_heads_weight' are weighting factors for the calculation of the total loss. This means, when the losses of the individual networks are summed up, the contributions from the bounding box regression heads are weighted by a factor 'bbox_heads_weight...
L3 Python bindings 1. Introduction 1.1 Set Python Environment 1.2 Build the Shared Library 2. Using the Vitis BLAS L3 Python API 2.1 General Description 2.1.1 Vitis BLAS Initialization 2.2 Vitis BLAS Helper Function Reference 2.3 Using Python APIs Python Environment Setup Guide Bench...
obj_ids = np.unique(mask) # first id is the background, so remove it obj_ids = obj_ids[1:] # split the color-encoded mask into a set # of binary masks masks = mask == obj_ids[:, None, None] # get bounding box coordinates for each mask ...
any(0).cumsum() xmask = (xmask != 0) & (xmask != xmask[-1]) dst = dst[ymask][:, xmask] return dst.astype(bool) Example #17Source File: util.py From SlowFast-Network-pytorch with MIT License 6 votes def draw_bboxes(img, bbox, identities=None, distance=None, speed = ...
INT8 models are generated byIntel® Neural Compressor.Intel® Neural Compressoris an open-source Python library which supports automatic accuracy-driven tuning strategies to help user quickly find out the best quantized model. It implements dynamic and static quantization for ONNX models and can ...
getPerspectiveTransform(box0, box1) image = cv2.warpPerspective(image, mat, (width, height), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101, borderValue=(0, 0, 0,)) mask = cv2.warpPerspective(mask, mat, (width, height), flags=cv2.INTER_NEAREST, borderMode=cv2.BORDER_REFLECT...
valid_params={ "Normal":{"masking_spread":None,"inverse_roi":False,"bounding_box":False}, "NoLung":{"masking_spread":0,"inverse_roi":False,"bounding_box":False}, "NoLungBB":{"masking_spread":0,"inverse_roi":False,"bounding_box":True}, "OnlyLung":{"masking_spread":0,"inverse_...