diff --git a/samples/dnn/models.yml b/samples/dnn/models.yml index 53b1604654c3..96550403c642 100644 --- a/samples/dnn/models.yml +++ b/samples/dnn/models.yml @@ -227,14 +227,13 @@ googlenet: # Semantic segmentation models. ################################################################################ -fcn8s: +fcnresnet50: load_info: - url: "http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel" - sha1: "c449ea74dd7d83751d1357d6a8c323fcf4038962" - model: "fcn8s-heavy-pascal.caffemodel" - config: "fcn8s-heavy-pascal.prototxt" - mean: [0, 0, 0] - scale: 1.0 + url: "https://github.com/onnx/models/raw/491ce05590abb7551d7fae43c067c060eeb575a6/validated/vision/object_detection_segmentation/fcn/model/fcn-resnet50-12.onnx" + sha1: "1bb0c7e0034038969aecc6251166f1612a139230" + model: "fcn-resnet50-12.onnx" + mean: [103.5, 116.2, 123.6] + scale: 0.019 width: 500 height: 500 rgb: false @@ -251,3 +250,15 @@ fcnresnet101: height: 500 rgb: false sample: "segmentation" + +u2netp: + load_info: + url: "https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2netp.onnx" + sha1: "0a99236f0d5c1916a99a8c401b23e5ef32038606" + model: "u2netp.onnx" + mean: [123.6, 116.2, 103.5] + scale: 0.019 + width: 320 + height: 320 + rgb: true + sample: "segmentation" \ No newline at end of file diff --git a/samples/dnn/segmentation.cpp b/samples/dnn/segmentation.cpp index b86569961e04..4534bfd97fe4 100644 --- a/samples/dnn/segmentation.cpp +++ b/samples/dnn/segmentation.cpp @@ -1,5 +1,6 @@ #include #include +#include #include #include @@ -7,50 +8,54 @@ #include "common.hpp" -std::string param_keys = - "{ help h | | Print help message. }" - "{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }" - "{ zoo | models.yml | An optional path to file with preprocessing parameters }" - "{ device | 0 | camera device number. }" - "{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }" - "{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }" - "{ classes | | Optional path to a text file with names of classes. }" - "{ colors | | Optional path to a text file with colors for an every class. " - "An every color is represented with three values from 0 to 255 in BGR channels order. }"; -std::string backend_keys = cv::format( - "{ backend | 0 | Choose one of computation backends: " - "%d: automatically (by default), " - "%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), " - "%d: OpenCV implementation, " - "%d: VKCOM, " - "%d: CUDA }", cv::dnn::DNN_BACKEND_DEFAULT, cv::dnn::DNN_BACKEND_INFERENCE_ENGINE, cv::dnn::DNN_BACKEND_OPENCV, cv::dnn::DNN_BACKEND_VKCOM, cv::dnn::DNN_BACKEND_CUDA); -std::string target_keys = cv::format( - "{ target | 0 | Choose one of target computation devices: " - "%d: CPU target (by default), " - "%d: OpenCL, " - "%d: OpenCL fp16 (half-float precision), " - "%d: VPU, " - "%d: Vulkan, " - "%d: CUDA, " - "%d: CUDA fp16 (half-float preprocess) }", cv::dnn::DNN_TARGET_CPU, cv::dnn::DNN_TARGET_OPENCL, cv::dnn::DNN_TARGET_OPENCL_FP16, cv::dnn::DNN_TARGET_MYRIAD, cv::dnn::DNN_TARGET_VULKAN, cv::dnn::DNN_TARGET_CUDA, cv::dnn::DNN_TARGET_CUDA_FP16); -std::string keys = param_keys + backend_keys + target_keys; - using namespace cv; +using namespace std; using namespace dnn; -std::vector classes; -std::vector colors; +const string param_keys = + "{ help h | | Print help message. }" + "{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }" + "{ zoo | models.yml | An optional path to file with preprocessing parameters }" + "{ device | 0 | camera device number. }" + "{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }" + "{ classes | | Optional path to a text file with names of classes. }" + "{ colors | | Optional path to a text file with colors for an every class. " + "Every color is represented with three values from 0 to 255 in BGR channels order. }"; + +const string backend_keys = format( + "{ backend | 0 | Choose one of computation backends: " + "%d: automatically (by default), " + "%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), " + "%d: OpenCV implementation, " + "%d: VKCOM, " + "%d: CUDA }", + DNN_BACKEND_DEFAULT, DNN_BACKEND_INFERENCE_ENGINE, DNN_BACKEND_OPENCV, DNN_BACKEND_VKCOM, DNN_BACKEND_CUDA); + +const string target_keys = format( + "{ target | 0 | Choose one of target computation devices: " + "%d: CPU target (by default), " + "%d: OpenCL, " + "%d: OpenCL fp16 (half-float precision), " + "%d: VPU, " + "%d: Vulkan, " + "%d: CUDA, " + "%d: CUDA fp16 (half-float preprocess) }", + DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16, DNN_TARGET_MYRIAD, DNN_TARGET_VULKAN, DNN_TARGET_CUDA, DNN_TARGET_CUDA_FP16); + +string keys = param_keys + backend_keys + target_keys; +vector classes; +vector colors; void showLegend(); void colorizeSegmentation(const Mat &score, Mat &segm); -int main(int argc, char** argv) +int main(int argc, char **argv) { CommandLineParser parser(argc, argv, keys); - const std::string modelName = parser.get("@alias"); - const std::string zooFile = parser.get("zoo"); + const string modelName = parser.get("@alias"); + const string zooFile = parser.get("zoo"); keys += genPreprocArguments(modelName, zooFile); @@ -68,36 +73,33 @@ int main(int argc, char** argv) int inpWidth = parser.get("width"); int inpHeight = parser.get("height"); String model = findFile(parser.get("model")); - String config = findFile(parser.get("config")); - String framework = parser.get("framework"); int backendId = parser.get("backend"); int targetId = parser.get("target"); // Open file with classes names. if (parser.has("classes")) { - std::string file = parser.get("classes"); - std::ifstream ifs(file.c_str()); + string file = parser.get("classes"); + ifstream ifs(file.c_str()); if (!ifs.is_open()) CV_Error(Error::StsError, "File " + file + " not found"); - std::string line; - while (std::getline(ifs, line)) + string line; + while (getline(ifs, line)) { classes.push_back(line); } } - // Open file with colors. if (parser.has("colors")) { - std::string file = parser.get("colors"); - std::ifstream ifs(file.c_str()); + string file = parser.get("colors"); + ifstream ifs(file.c_str()); if (!ifs.is_open()) CV_Error(Error::StsError, "File " + file + " not found"); - std::string line; - while (std::getline(ifs, line)) + string line; + while (getline(ifs, line)) { - std::istringstream colorStr(line.c_str()); + istringstream colorStr(line.c_str()); Vec3b color; for (int i = 0; i < 3 && !colorStr.eof(); ++i) @@ -114,23 +116,21 @@ int main(int argc, char** argv) CV_Assert(!model.empty()); //! [Read and initialize network] - Net net = readNet(model, config, framework); + Net net = readNetFromONNX(model); net.setPreferableBackend(backendId); net.setPreferableTarget(targetId); //! [Read and initialize network] - // Create a window - static const std::string kWinName = "Deep learning semantic segmentation in OpenCV"; + static const string kWinName = "Deep learning semantic segmentation in OpenCV"; namedWindow(kWinName, WINDOW_NORMAL); //! [Open a video file or an image file or a camera stream] VideoCapture cap; if (parser.has("input")) - cap.open(parser.get("input")); + cap.open(findFile(parser.get("input"))); else cap.open(parser.get("device")); //! [Open a video file or an image file or a camera stream] - // Process frames. Mat frame, blob; while (waitKey(1) < 0) @@ -141,29 +141,45 @@ int main(int argc, char** argv) waitKey(); break; } - + imshow("Original Image", frame); //! [Create a 4D blob from a frame] blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false); - //! [Create a 4D blob from a frame] - //! [Set input blob] net.setInput(blob); - //! [Set input blob] //! [Make forward pass] Mat score = net.forward(); - //! [Make forward pass] - - Mat segm; - colorizeSegmentation(score, segm); - - resize(segm, segm, frame.size(), 0, 0, INTER_NEAREST); - addWeighted(frame, 0.1, segm, 0.9, 0.0, frame); + if (modelName == "u2netp") + { + Mat mask, thresholded_mask, foreground_overlay, background_overlay, foreground_segmented; + mask = cv::Mat(score.size[2], score.size[3], CV_32F, score.ptr(0, 0)); + mask.convertTo(mask, CV_8U, 255); + threshold(mask, thresholded_mask, 0, 255, THRESH_BINARY + THRESH_OTSU); + resize(thresholded_mask, thresholded_mask, Size(frame.cols, frame.rows), 0, 0, INTER_AREA); + // Create overlays for foreground and background + foreground_overlay = Mat::zeros(frame.size(), frame.type()); + background_overlay = Mat::zeros(frame.size(), frame.type()); + // Set foreground (object) to red and background to blue + foreground_overlay.setTo(Scalar(0, 0, 255), thresholded_mask); + Mat inverted_mask; + bitwise_not(thresholded_mask, inverted_mask); + background_overlay.setTo(Scalar(255, 0, 0), inverted_mask); + // Blend the overlays with the original frame + addWeighted(frame, 1, foreground_overlay, 0.5, 0, foreground_segmented); + addWeighted(foreground_segmented, 1, background_overlay, 0.5, 0, frame); + } + else + { + Mat segm; + colorizeSegmentation(score, segm); + resize(segm, segm, frame.size(), 0, 0, INTER_NEAREST); + addWeighted(frame, 0.1, segm, 0.9, 0.0, frame); + } // Put efficiency information. - std::vector layersTimes; + vector layersTimes; double freq = getTickFrequency() / 1000; double t = net.getPerfProfile(layersTimes) / freq; - std::string label = format("Inference time: %.2f ms", t); + string label = format("Inference time: %.2f ms", t); putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0)); imshow(kWinName, frame); @@ -194,7 +210,8 @@ void colorizeSegmentation(const Mat &score, Mat &segm) else if (chns != (int)colors.size()) { CV_Error(Error::StsError, format("Number of output classes does not match " - "number of colors (%d != %zu)", chns, colors.size())); + "number of colors (%d != %zu)", + chns, colors.size())); } Mat maxCl = Mat::zeros(rows, cols, CV_8UC1); @@ -216,7 +233,6 @@ void colorizeSegmentation(const Mat &score, Mat &segm) } } } - segm.create(rows, cols, CV_8UC3); for (int row = 0; row < rows; row++) { @@ -239,7 +255,8 @@ void showLegend() if ((int)colors.size() != numClasses) { CV_Error(Error::StsError, format("Number of output classes does not match " - "number of labels (%zu != %zu)", colors.size(), classes.size())); + "number of labels (%zu != %zu)", + colors.size(), classes.size())); } legend.create(kBlockHeight * numClasses, 200, CV_8UC3); for (int i = 0; i < numClasses; i++) diff --git a/samples/dnn/segmentation.py b/samples/dnn/segmentation.py index 9f9e7693e04b..f46281f729d0 100644 --- a/samples/dnn/segmentation.py +++ b/samples/dnn/segmentation.py @@ -14,9 +14,6 @@ parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'), help='An optional path to file with preprocessing parameters.') parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.') -parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'darknet', 'onnx'], - help='Optional name of an origin framework of the model. ' - 'Detect it automatically if it does not set.') parser.add_argument('--colors', help='Optional path to a text file with colors for an every class. ' 'An every color is represented with three values from 0 to 255 in BGR channels order.') parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int, @@ -44,7 +41,6 @@ args = parser.parse_args() args.model = findFile(args.model) -args.config = findFile(args.config) args.classes = findFile(args.classes) np.random.seed(324) @@ -79,7 +75,7 @@ def showLegend(classes): classes = None # Load a network -net = cv.dnn.readNet(args.model, args.config, args.framework) +net = cv.dnn.readNet(args.model) net.setPreferableBackend(args.backend) net.setPreferableTarget(args.target) @@ -94,41 +90,53 @@ def showLegend(classes): cv.waitKey() break + cv.imshow("Original Image", frame) frameHeight = frame.shape[0] frameWidth = frame.shape[1] - # Create a 4D blob from a frame. inpWidth = args.width if args.width else frameWidth inpHeight = args.height if args.height else frameHeight blob = cv.dnn.blobFromImage(frame, args.scale, (inpWidth, inpHeight), args.mean, args.rgb, crop=False) - # Run a model net.setInput(blob) score = net.forward() - numClasses = score.shape[1] - height = score.shape[2] - width = score.shape[3] - - # Draw segmentation - if not colors: - # Generate colors - colors = [np.array([0, 0, 0], np.uint8)] - for i in range(1, numClasses): - colors.append((colors[i - 1] + np.random.randint(0, 256, [3], np.uint8)) / 2) - - classIds = np.argmax(score[0], axis=0) - segm = np.stack([colors[idx] for idx in classIds.flatten()]) - segm = segm.reshape(height, width, 3) - - segm = cv.resize(segm, (frameWidth, frameHeight), interpolation=cv.INTER_NEAREST) - frame = (0.1 * frame + 0.9 * segm).astype(np.uint8) + if args.alias == 'u2netp': + mask = score[0][0] + mask = mask.astype(np.uint8) + _, mask = cv.threshold(mask, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU) + mask = cv.resize(mask, (frame.shape[1], frame.shape[0]), interpolation=cv.INTER_AREA) + # Create overlays for foreground and background + foreground_overlay = np.zeros_like(frame, dtype=np.uint8) + background_overlay = np.zeros_like(frame, dtype=np.uint8) + # Set foreground (object) to red and background to blue + foreground_overlay[mask == 255] = [0, 0, 255] # Red foreground + background_overlay[mask == 0] = [255, 0, 0] # Blue background + # Blend the overlays with the original frame + foreground_segmented = cv.addWeighted(frame, 1, foreground_overlay, 0.5, 0) + frame = cv.addWeighted(foreground_segmented, 1, background_overlay, 0.5, 0) + else: + numClasses = score.shape[1] + height = score.shape[2] + width = score.shape[3] + # Draw segmentation + if not colors: + # Generate colors + colors = [np.array([0, 0, 0], np.uint8)] + for i in range(1, numClasses): + colors.append((colors[i - 1] + np.random.randint(0, 256, [3], np.uint8)) / 2) + classIds = np.argmax(score[0], axis=0) + segm = np.stack([colors[idx] for idx in classIds.flatten()]) + segm = segm.reshape(height, width, 3) + + segm = cv.resize(segm, (frameWidth, frameHeight), interpolation=cv.INTER_NEAREST) + frame = (0.1 * frame + 0.9 * segm).astype(np.uint8) + + showLegend(classes) # Put efficiency information. t, _ = net.getPerfProfile() label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency()) cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0)) - showLegend(classes) - - cv.imshow(winName, frame) + cv.imshow(winName, frame) \ No newline at end of file