defcnn_text_detect():image=cv.imread("D:/images/cover_01.jpg")cv.imshow("input",image)result=image.copy()detector=cv.text.TextDetectorCNN_create("textbox.prototxt","TextBoxes_icdar13.caffemodel")boxes,scores=detector.detect(image);threshold=0.5forrinrange(np.shape(boxes)[0]):ifscores[r...
FaceMesh(static_image, max_faces, refine, detect_conf, track_conf) def detect_mesh(self, img, disp=True): results = self.face_track.process(img) detected_landmarks = results.multi_face_landmarks if detected_landmarks: if disp: for f_landmarks in detected_landmarks: self.draw_utils...
self._frame = self._capture.retrieve( self._frame, self.channel) return self._frame @property def isWritingImage(self): return self._imageFilename is not None @property def isWritingVideo(self): return self._videoFilename is not None ...
Note:To use your GPU for neural network inference, you need to have OpenCV’sdnnmodule compiled with NVIDIA CUDA support. OpenCV’sdnnmodule does not have NVIDIA support via a pip install. Instead, you need to compile OpenCV with GPU support explicitly. We cover how to do that in thetutoria...
looking at datasets for robot arm manipulations. camera stabilization functionality (as well as other nice image/video processing algorithms for robotics) could be useful to have Alexander S (opencv.ai): Done: Abduragim Shtanchaev: Working on GPT2 inference sample to make it dynamic ...
# export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH import cv2 as cv import numpy as np import argparse parser = argparse.ArgumentParser() parser.add_argument('--input', help='Path to image or video. Skip to capture fra...
Many computer vision algorithms that reside in dedicated modules are discarded, such as face detection etc.You could try deep-learning based algorithms with neural network inference library optimized for mobile. Image IO functions in highgui module, likecv::imreadandcv::imwrite, are re-implemented ...
我们用于人脸识别的网络架构基于 He 等人的 Deep Residual Learning for Image Recognition 论文中的ResNet-34,但层数更少,过滤器的数量减少了一半。 网络本身由 Davis King 在≈300 万张图像的数据集上进行训练。在野外标记人脸 (LFW) 数据集上,该网络与其他最先进的方法进行比较,准确率达到 99.38%。Davis King...
= cv2.face.LBPHFaceRecognizer_create() # 此处要修改,文件夹名称与我们自行创建的要一致 detector = cv2.CascadeClassifier("cascades/haarcascade_frontalface_default.xml"); # function to get the images and label data def getImagesAndLabels(path): imagePaths = [os.path.join(path,f) for f in os....
(mBitmap); mTv_faction.setText("Facecount:" + facenum); } }); mBtn_choose.setOnClickListener(new OnClickListener() { @Override public void onClick(View v) { Intent intent = new Intent(); intent.setAction(Intent.ACTION_PICK); intent.setType("image/*"); startActivityForResult(intent...