Merge branch 'openvinotoolkit:master' into master

Author: ieliz

.gitattributes

@@ -41,3 +41,6 @@
 /tools/model_tools/** omz.ci.job-for-change.downloader
 
 *.md omz.ci.job-for-change.documentation -omz.ci.job-for-change.ac -omz.ci.job-for-change.demos -omz.ci.job-for-change.downloader
+
+/models/intel/device_support.md omz.ci.job-for-change.demos
+/models/public/device_support.md omz.ci.job-for-change.demos

README.md

@@ -1,5 +1,5 @@
 # [OpenVINO™ Toolkit](https://docs.openvino.ai/latest/index.html) - Open Model Zoo repository
-[![Stable release](https://img.shields.io/badge/version-2021.4-green.svg)](https://github.com/openvinotoolkit/open_model_zoo/releases/tag/2022.1)
+[![Stable release](https://img.shields.io/badge/version-2022.1-green.svg)](https://github.com/openvinotoolkit/open_model_zoo/releases/tag/2022.1)
 [![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/open_model_zoo/community)
 [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
 

ci/dependencies.yml

@@ -1,6 +1,6 @@
-opencv_linux: '20220210_0636-4.5.5_043'
-opencv_windows: '20220210_0636-4.5.5_043'
-openvino_linux: '2022.1.0.582'
-openvino_windows: '2022.1.0.582'
-wheel_linux: '2022.1.0.dev20220211-6610'
-wheel_windows: '2022.1.0.dev20220211-6610'
+opencv_linux: '20220215_0649-4.5.5_053'
+opencv_windows: '20220215_0649-4.5.5_053'
+openvino_linux: '2022.1.0.592'
+openvino_windows: '2022.1.0.592'
+wheel_linux: '2022.1.0.dev20220215-6682'
+wheel_windows: '2022.1.0.dev20220215-6682'

demos/common/cpp/models/include/models/detection_model_yolo.h

@@ -27,14 +27,14 @@ class ModelYolo : public DetectionModel {
     class Region {
     public:
         int num = 0;
-        int classes = 0;
+        size_t classes = 0;
         int coords = 0;
         std::vector<float> anchors;
-        int outputWidth = 0;
-        int outputHeight = 0;
+        size_t outputWidth = 0;
+        size_t outputHeight = 0;
 
         Region(const std::shared_ptr<ov::op::v0::RegionYolo>& regionYolo);
-        Region(int classes, int coords, const std::vector<float>& anchors, const std::vector<int64_t>& masks, int outputWidth, int outputHeight);
+        Region(size_t classes, int coords, const std::vector<float>& anchors, const std::vector<int64_t>& masks, size_t outputWidth, size_t outputHeight);
     };
 
 public:
@@ -75,7 +75,7 @@ class ModelYolo : public DetectionModel {
         const unsigned long resized_im_h, const unsigned long resized_im_w, const unsigned long original_im_h,
         const unsigned long original_im_w, std::vector<DetectedObject>& objects);
 
-    static int calculateEntryIndex(int entriesNum, int lcoords, int lclasses, int location, int entry);
+    static int calculateEntryIndex(int entriesNum, int lcoords, size_t lclasses, int location, int entry);
     static double intersectionOverUnion(const DetectedObject& o1, const DetectedObject& o2);
 
     std::map<std::string, Region> regions;

demos/common/cpp/models/src/detection_model_yolo.cpp

@@ -181,14 +181,15 @@ void ModelYolo::prepareInputsOutputs(std::shared_ptr<ov::Model>& model) {
 
         for (const auto& name : outputsNames) {
             const auto& shape = outShapes[name];
-            int classes = (int)shape[ov::layout::channels_idx(yoloRegionLayout)] / num - 4 - (isObjConf ? 1 : 0);
             if (shape[ov::layout::channels_idx(yoloRegionLayout)] % num != 0) {
                 throw std::logic_error(std::string("Output tenosor ") + name + " has wrong 2nd dimension");
             }
-            regions.emplace(name, Region(classes, 4,
+            regions.emplace(name, Region(
+                shape[ov::layout::channels_idx(yoloRegionLayout)] / num - 4 - (isObjConf ? 1 : 0),
+                4,
                 presetAnchors.size() ? presetAnchors : defaultAnchors[yoloVersion],
                 std::vector<int64_t>(chosenMasks.begin() + i * num, chosenMasks.begin() + (i + 1) * num),
-                (int)shape[ov::layout::width_idx(yoloRegionLayout)], (int)shape[ov::layout::height_idx(yoloRegionLayout)]));
+                shape[ov::layout::width_idx(yoloRegionLayout)], shape[ov::layout::height_idx(yoloRegionLayout)]));
             i++;
         }
     }
@@ -315,7 +316,7 @@ void ModelYolo::parseYOLOOutput(const std::string& output_name,
                 obj.width = clamp(width, 0.f, (float)original_im_w - obj.x);
                 obj.height = clamp(height, 0.f, (float)original_im_h - obj.y);
 
-                for (int j = 0; j < region.classes; ++j) {
+                for (size_t j = 0; j < region.classes; ++j) {
                     int class_index = calculateEntryIndex(entriesNum, region.coords, region.classes + isObjConf, n * entriesNum + i, region.coords + isObjConf + j);
                     float prob = scale * postprocessRawData(outData[class_index]);
 
@@ -332,7 +333,7 @@ void ModelYolo::parseYOLOOutput(const std::string& output_name,
     }
 }
 
-int ModelYolo::calculateEntryIndex(int totalCells, int lcoords, int lclasses, int location, int entry) {
+int ModelYolo::calculateEntryIndex(int totalCells, int lcoords, size_t lclasses, int location, int entry) {
     int n = location / totalCells;
     int loc = location % totalCells;
     return (n * (lcoords + lclasses) + entry) * totalCells + loc;
@@ -353,8 +354,8 @@ ModelYolo::Region::Region(const std::shared_ptr<ov::op::v0::RegionYolo>& regionY
     num = mask.size();
 
     auto shape = regionYolo->get_input_shape(0);
-    outputWidth = (int)shape[3];
-    outputHeight = (int)shape[2];
+    outputWidth = shape[3];
+    outputHeight = shape[2];
 
     if (num) {
 
@@ -377,7 +378,7 @@ ModelYolo::Region::Region(const std::shared_ptr<ov::op::v0::RegionYolo>& regionY
     }
 }
 
-ModelYolo::Region::Region(int classes, int coords, const std::vector<float>& anchors, const std::vector<int64_t>& masks, int outputWidth, int outputHeight) :
+ModelYolo::Region::Region(size_t classes, int coords, const std::vector<float>& anchors, const std::vector<int64_t>& masks, size_t outputWidth, size_t outputHeight) :
     classes(classes), coords(coords),
     outputWidth(outputWidth), outputHeight(outputHeight) {
     num = masks.size();

demos/common/cpp/utils/include/utils/kuhn_munkres.hpp

@@ -31,7 +31,7 @@ class KuhnMunkres {
     /// \return Optimal column index for each row. -1 means that there is no
     /// column for row.
     ///
-    std::vector<int> Solve(const cv::Mat &dissimilarity_matrix);
+    std::vector<size_t> Solve(const cv::Mat &dissimilarity_matrix);
 
 private:
     static constexpr int kStar = 1;

demos/common/cpp/utils/src/kuhn_munkres.cpp

@@ -10,7 +10,7 @@
 
 KuhnMunkres::KuhnMunkres(bool greedy) : n_(), greedy_(greedy) {}
 
-std::vector<int> KuhnMunkres::Solve(const cv::Mat& dissimilarity_matrix) {
+std::vector<size_t> KuhnMunkres::Solve(const cv::Mat& dissimilarity_matrix) {
     CV_Assert(dissimilarity_matrix.type() == CV_32F);
     double min_val;
     cv::minMaxLoc(dissimilarity_matrix, &min_val);
@@ -28,12 +28,12 @@ std::vector<int> KuhnMunkres::Solve(const cv::Mat& dissimilarity_matrix) {
 
     Run();
 
-    std::vector<int> results(dissimilarity_matrix.rows, -1);
+    std::vector<size_t> results(dissimilarity_matrix.rows, -1);
     for (int i = 0; i < dissimilarity_matrix.rows; i++) {
         const auto ptr = marked_.ptr<char>(i);
         for (int j = 0; j < dissimilarity_matrix.cols; j++) {
             if (ptr[j] == kStar) {
-                results[i] = j;
+                results[i] = (size_t)j;
             }
         }
     }

demos/common/python/openvino/model_zoo/model_api/models/instance_segmentation.py

@@ -1,5 +1,5 @@
 """
- Copyright (c) 2020 Intel Corporation
+ Copyright (c) 2020-2022 Intel Corporation
 
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
@@ -19,8 +19,8 @@
 
 from .image_model import ImageModel
 from .model import WrapperError
-from .types import NumericalValue
-from .utils import nms
+from .types import NumericalValue, ListValue, StringValue
+from .utils import nms, load_labels
 
 
 class MaskRCNNModel(ImageModel):
@@ -29,6 +29,8 @@ class MaskRCNNModel(ImageModel):
     def __init__(self, model_adapter, configuration, preload=False):
         super().__init__(model_adapter, configuration, preload)
         self._check_io_number((1, 2), (3, 4, 5, 8))
+        if self.path_to_labels:
+            self.labels = load_labels(self.path_to_labels)
         self.is_segmentoly = len(self.inputs) == 2
         self.output_blob_name = self._get_outputs()
 
@@ -40,6 +42,10 @@ def parameters(cls):
                 default_value=0.5,
                 description='Probability threshold for detections filtering'
             ),
+            'labels': ListValue(description="List of class labels"),
+            'path_to_labels': StringValue(
+                description="Path to file with labels. Overrides the labels"
+            ),
         })
         return parameters
 
@@ -150,6 +156,8 @@ class YolactModel(ImageModel):
 
     def __init__(self, model_adapter, configuration, preload=False):
         super().__init__(model_adapter, configuration, preload)
+        if self.path_to_labels:
+            self.labels = load_labels(self.path_to_labels)
         self._check_io_number(1, 4)
         self.output_blob_name = self._get_outputs()
 
@@ -161,6 +169,10 @@ def parameters(cls):
                 default_value=0.5,
                 description='Probability threshold for detections filtering'
             ),
+            'labels': ListValue(description="List of class labels"),
+            'path_to_labels': StringValue(
+                description="Path to file with labels. Overrides the labels"
+            ),
         })
         return parameters
 

demos/common/python/visualizers/__init__.py

@@ -0,0 +1,23 @@
+"""
+ Copyright (C) 2022 Intel Corporation
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+"""
+
+from .drawing_utils import ColorPalette
+from .instance_segmentation import InstanceSegmentationVisualizer
+
+__all__ = [
+    'ColorPalette',
+    'InstanceSegmentationVisualizer',
+]

demos/common/python/visualizers/drawing_utils.py

@@ -0,0 +1,61 @@
+"""
+ Copyright (c) 2022 Intel Corporation
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+"""
+
+import random
+import colorsys
+
+import numpy as np
+
+
+class ColorPalette:
+    def __init__(self, n, rng=None):
+        if n == 0:
+            raise ValueError('ColorPalette accepts only the positive number of colors')
+        if rng is None:
+            rng = random.Random(0xACE)  # nosec - disable B311:random check
+
+        candidates_num = 100
+        hsv_colors = [(1.0, 1.0, 1.0)]
+        for _ in range(1, n):
+            colors_candidates = [(rng.random(), rng.uniform(0.8, 1.0), rng.uniform(0.5, 1.0))
+                                 for _ in range(candidates_num)]
+            min_distances = [self.min_distance(hsv_colors, c) for c in colors_candidates]
+            arg_max = np.argmax(min_distances)
+            hsv_colors.append(colors_candidates[arg_max])
+
+        self.palette = [self.hsv2rgb(*hsv) for hsv in hsv_colors]
+
+    @staticmethod
+    def dist(c1, c2):
+        dh = min(abs(c1[0] - c2[0]), 1 - abs(c1[0] - c2[0])) * 2
+        ds = abs(c1[1] - c2[1])
+        dv = abs(c1[2] - c2[2])
+        return dh * dh + ds * ds + dv * dv
+
+    @classmethod
+    def min_distance(cls, colors_set, color_candidate):
+        distances = [cls.dist(o, color_candidate) for o in colors_set]
+        return np.min(distances)
+
+    @staticmethod
+    def hsv2rgb(h, s, v):
+        return tuple(round(c * 255) for c in colorsys.hsv_to_rgb(h, s, v))
+
+    def __getitem__(self, n):
+        return self.palette[n % len(self.palette)]
+
+    def __len__(self):
+        return len(self.palette)