args.checkpoint, device=args.device)# test a single imageresult =inference_detector(model, args.img)# show the resultsshow_result_pyplot(model, args.img, result, score_thr=args.score_thr)
创建人脸检测的Model对象,其中model文件表示模型结构文件,params表示模型参数文件。 face_detector = Model('./pyramidbox_lite/model', './pyramidbox_lite/params') 人脸检测图像预处理 importcv2importnumpyasnpfromPILimportImageimportmathdefface_detect_preprocess(img,shrink=1.0):# BGRimg_shape=img.shape img=...
this_res = inference_detector(model, img) File "/Users/aya/miniforge3/envs/torch-gpu/lib/python3.8/site-packages/mmdet/apis/inference.py", line 151, in inference_detector results = model(return_loss=False, rescale=True, **data) File "/Users/aya/miniforge3/envs/torch-gpu/lib/python3.8/...
CHienSFcommentedApr 19, 2021 print("Before mmdet verification") try: from mmdet.apis import inference_detector, init_detector has_mmdet = True except (ImportError, ModuleNotFoundError): has_mmdet = False print("after mmdet verification")
saver.restore( sess, model_path )# Fast forward images & save themforidx,imgPinenumerate(imgPath): img = Image.open(imgP) img = img.resize([224,224]) img = np.array(img) img = img.reshape(1,224,224,3) 開發者ID:marc-moreaux,項目名稱:Weakly_detector,代碼行數:32,代碼來源:forward...
在我们OCR模型部署过程中,需要使用到两个模型,一个为文件检测模型,一个为文字识别模型,这两个模型都会通过Model类来创建推理实例。 文字检测 1) OCR模型部署过程中,首要做的一件事是对图像中的文字进行检测,我们先通过Model类来创建文字检测模型实例 In [3] ocr_detector = Model('./ocr_model/text_detector/...
ocr_detector = Model('./ocr_model/text_detector/__model__', './ocr_model/text_detector/__params__') 2) 读取事例图片并进行图像预处理 In [15] import cv2 import numpy as np import sys import math image_file = './work/11.jpg' ori_img = cv2.imread(image_file) print (ori_img.sha...
MLModelDownloadListener Class Summary MLLocalModelManager MLModelDownloadStrategy MLModelDownloadStrategy.Factory MLLocalModel MLRemoteModel 错误码 com.huawei.hms.mlsdk.langdetect Overview Class Summary MLDetectedLang MLLangDetectorFactory com.huawei.hms.mlsdk.langdetect.local Overview ...
This method involvesslidingafixed-sized windowacross the image at various positions and scales. At each window position, the object detector applies a classification model to determine if an object is present. By considering different window sizes and positions, the detector can effectively search for...
import cv2 import matplotlib.pyplot as plt import numpy as np import os from paddleocr import PaddleOCR, draw_ocr ocr = PaddleOCR(use_gpu=False) # need to run only once to download and load model into memory img_path = './images/006.jpg' result = ocr.ocr(img_path, rec=False) for ...