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https://github.com/opencv/opencv.git
synced 2026-07-31 00:03:03 +04:00
FIx misc. source and comment typos
Found via `codespell -q 3 -S ./3rdparty,./modules -L amin,ang,atleast,dof,endwhile,hist,uint`
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@@ -622,7 +622,7 @@ int main(int argc, char* argv[])
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vector<Size> sizes(num_images);
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vector<UMat> masks(num_images);
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// Preapre images masks
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// Prepare images masks
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for (int i = 0; i < num_images; ++i)
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{
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masks[i].create(images[i].size(), CV_8U);
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@@ -41,7 +41,7 @@ const int MAX_FOCUS_STEP = 32767;
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const int FOCUS_DIRECTION_INFTY = 1;
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const int DEFAULT_BREAK_LIMIT = 5;
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const int DEFAULT_OUTPUT_FPS = 20;
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const double epsylon = 0.0005; // compression, noice, etc.
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const double epsylon = 0.0005; // compression, noise, etc.
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struct Args_t
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{
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@@ -83,7 +83,7 @@ public:
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r = m_pD3D11SwapChain->GetBuffer(0, __uuidof(ID3D11Texture2D), (LPVOID*)&m_pBackBuffer);
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if (FAILED(r))
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{
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throw std::runtime_error("GetBufer() failed!");
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throw std::runtime_error("GetBuffer() failed!");
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}
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r = m_pD3D11Dev->CreateRenderTargetView(m_pBackBuffer, NULL, &m_pRenderTarget);
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@@ -67,7 +67,7 @@ You need to prepare 2 LMDB databases: one for training images, one for validatio
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3. Train your detector
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For training you need to have 3 files: train.prototxt, test.prototxt and solver.prototxt. You can find these files in the same directory as for this readme.
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Also you need to edit train.prototxt and test.prototxt to replace paths for your LMDB databases to actual databases you've crated in step 2.
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Also you need to edit train.prototxt and test.prototxt to replace paths for your LMDB databases to actual databases you've created in step 2.
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Now all is done for launch training process.
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Execute next lines in Terminal:
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@@ -88,7 +88,7 @@ while cv.waitKey(1) < 0:
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points = []
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for i in range(len(BODY_PARTS)):
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# Slice heatmap of corresponging body's part.
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# Slice heatmap of corresponding body's part.
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heatMap = out[0, i, :, :]
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# Originally, we try to find all the local maximums. To simplify a sample
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@@ -703,7 +703,7 @@ int App::process_frame_with_open_cl(cv::Mat& frame, bool use_buffer, cl_mem* mem
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if (0 == mem || 0 == m_img_src)
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{
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// allocate/delete cl memory objects every frame for the simplicity.
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// in real applicaton more efficient pipeline can be built.
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// in real application more efficient pipeline can be built.
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if (use_buffer)
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{
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@@ -66,7 +66,7 @@ def on_high_V_thresh_trackbar(val):
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cv.setTrackbarPos(high_V_name, window_detection_name, high_V)
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parser = argparse.ArgumentParser(description='Code for Thresholding Operations using inRange tutorial.')
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parser.add_argument('--camera', help='Camera devide number.', default=0, type=int)
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parser.add_argument('--camera', help='Camera divide number.', default=0, type=int)
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args = parser.parse_args()
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## [cap]
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@@ -25,7 +25,7 @@ def detectAndDisplay(frame):
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parser = argparse.ArgumentParser(description='Code for Cascade Classifier tutorial.')
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parser.add_argument('--face_cascade', help='Path to face cascade.', default='data/haarcascades/haarcascade_frontalface_alt.xml')
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parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
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parser.add_argument('--camera', help='Camera devide number.', type=int, default=0)
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parser.add_argument('--camera', help='Camera divide number.', type=int, default=0)
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args = parser.parse_args()
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face_cascade_name = args.face_cascade
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