[pico] add openvino infer (#5713)

deploy/third_engine/demo_openvino/python/README.md

 # PicoDet OpenVINO Benchmark Demo
 
-本文件夹提供利用[Intel's OpenVINO Toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)进行PicoDet测速的Benchmark Demo
+本文件夹提供利用[Intel's OpenVINO Toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)进行PicoDet测速的Benchmark Demo与带后处理的模型Inference Demo。
 
 ## 安装 OpenVINO Toolkit
 
@@ -13,9 +13,9 @@ pip install openvino==2022.1.0
 
 详细安装步骤，可参考[OpenVINO官网](https://docs.openvinotoolkit.org/latest/get_started_guides.html)
 
-## 测试
+## Benchmark测试
 
-- 准备测试模型：根据[PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet)中【导出及转换模型】步骤，采用不包含后处理的方式导出模型（`-o export.benchmark=True` ），并生成待测试模型简化后的onnx模型（可在下文链接中可直接下载）。同时在本目录下新建```out_onnxsim```文件夹，将导出的onnx模型放在该目录下。
+- 准备测试模型：根据[PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet)中【导出及转换模型】步骤，采用不包含后处理的方式导出模型（`-o export.benchmark=True` ），并生成待测试模型简化后的onnx模型（可在下文链接中直接下载）。同时在本目录下新建```out_onnxsim```文件夹，将导出的onnx模型放在该目录下。
 
 - 准备测试所用图片：本demo默认利用PaddleDetection/demo/[000000014439.jpg](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/demo/000000014439.jpg)
 
@@ -30,7 +30,7 @@ python openvino_benchmark.py --img_path ..\..\..\..\demo\000000014439.jpg --onnx
 ```
 - 注意：```--in_shape```为对应模型输入size，默认为320
 
-### Inference images
+### Inference images(w/o 后处理)
 
 ```shell
 # Linux
@@ -38,20 +38,38 @@ python openvino_benchmark.py --benchmark 0 --img_path ../../../../demo/000000014
 # Windows
 python openvino_benchmark.py --benchmark 0 --img_path ..\..\..\..\demo\000000014439.jpg --onnx_path out_onnxsim\picodet_s_320_coco_lcnet.onnx --in_shape 320
 ```
+
+## Inference images(w/ 后处理, w/o NMS)
+
+- 准备测试模型：根据[PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet)中【导出及转换模型】步骤，采用**包含后处理**但**不包含NMS**的方式导出模型（`-o export.benchmark=False export.nms=False` ），并生成待测试模型简化后的onnx模型（可在下文链接中直接下载）。同时在本目录下新建```out_onnxsim_infer```文件夹，将导出的onnx模型放在该目录下。
+
+- 准备测试所用图片：默认利用../../demo_onnxruntime/imgs/[bus.jpg](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/third_engine/demo_onnxruntime/imgs/bus.jpg)
+
+```shell
+# Linux
+python openvino_infer.py --img_path ../../demo_onnxruntime/imgs/bus.jpg --onnx_path out_onnxsim_infer/picodet_s_320_postproccesed_woNMS.onnx --in_shape 320
+# Windows
+python openvino_infer.py --img_path ..\..\demo_onnxruntime\imgs\bus.jpg --onnx_path out_onnxsim_infer\picodet_s_320_postproccesed_woNMS.onnx --in_shape 320
+```
+- 结果：
+    <div align="center">
+      <img src="../../../../docs/images/res.jpg" height="500px" >
+    </div>
+
 ## 结果
 
-测试结果如下：
+- 测速结果如下：
 
 | 模型     | 输入尺寸 | ONNX  | 预测时延<sup><small>[CPU](#latency)|
 | :-------- | :--------: | :---------------------: | :----------------: |
-| PicoDet-XS |  320*320   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_320_coco_lcnet.onnx) | 3.9ms |
-| PicoDet-XS |  416*416   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_416_coco_lcnet.onnx) | 6.1ms |
-| PicoDet-S |  320*320   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_320_coco_lcnet.onnx) |     4.8ms |
-| PicoDet-S |  416*416   |  [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_416_coco_lcnet.onnx) |     6.6ms |
-| PicoDet-M |  320*320   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_320_coco_lcnet.onnx) | 8.2ms  |
-| PicoDet-M |  416*416   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416_coco_lcnet.onnx) | 12.7ms |
-| PicoDet-L |  320*320   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_320_coco_lcnet.onnx) | 11.5ms |
-| PicoDet-L |  416*416   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_416_coco_lcnet.onnx) |     20.7ms |
-| PicoDet-L |  640*640   | [model](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_640_coco.onnx) |     62.5ms |
+| PicoDet-XS |  320*320   | [( w/ 后处理;w/o NMS)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_320_lcnet_postproccesed_woNMS.onnx) &#124; [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_320_coco_lcnet.onnx) | 3.9ms |
+| PicoDet-XS |  416*416   | [( w/ 后处理;w/o NMS)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_416_lcnet_postproccesed_woNMS.onnx) &#124; [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_416_coco_lcnet.onnx) | 6.1ms |
+| PicoDet-S |  320*320   | [( w/ 后处理;w/o NMS)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_320_lcnet_postproccesed_woNMS.onnx) &#124; [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_320_coco_lcnet.onnx) |     4.8ms |
+| PicoDet-S |  416*416   |  [( w/ 后处理;w/o NMS)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_416_lcnet_postproccesed_woNMS.onnx) &#124; [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_416_coco_lcnet.onnx) |     6.6ms |
+| PicoDet-M |  320*320   | [( w/ 后处理;w/o NMS)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_320_lcnet_postproccesed_woNMS.onnx) &#124; [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_320_coco_lcnet.onnx) | 8.2ms  |
+| PicoDet-M |  416*416   | [( w/ 后处理;w/o NMS)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416_lcnet_postproccesed_woNMS.onnx) &#124; [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416_coco_lcnet.onnx) | 12.7ms |
+| PicoDet-L |  320*320   | [( w/ 后处理;w/o NMS)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_320_lcnet_postproccesed_woNMS.onnx) &#124; [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_320_coco_lcnet.onnx) | 11.5ms |
+| PicoDet-L |  416*416   | [( w/ 后处理;w/o NMS)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_416_lcnet_postproccesed_woNMS.onnx) &#124; [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_416_coco_lcnet.onnx) |     20.7ms |
+| PicoDet-L |  640*640   | [( w/ 后处理;w/o NMS)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_640_lcnet_postproccesed_woNMS.onnx) &#124; [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_640_coco_lcnet.onnx) |     62.5ms |
 
 - <a name="latency">测试环境：</a> 英特尔酷睿i7 10750H CPU。

deploy/third_engine/demo_openvino/python/openvino_benchmark.py

@@ -339,7 +339,7 @@ if __name__ == '__main__':
     parser.add_argument(
         '--img_path',
         type=str,
-        default='demo/000000014439.jpg',
+        default='../../../../demo/000000014439.jpg',
         help="image path")
     parser.add_argument(
         '--onnx_path',

deploy/third_engine/demo_openvino/python/openvino_infer.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 cv2
+import numpy as np
+import argparse
+from scipy.special import softmax
+from openvino.runtime import Core
+
+
+def image_preprocess(img_path, re_shape):
+    img = cv2.imread(img_path)
+    img = cv2.resize(
+        img, (re_shape, re_shape), interpolation=cv2.INTER_LANCZOS4)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = np.transpose(img, [2, 0, 1]) / 255
+    img = np.expand_dims(img, 0)
+    img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
+    img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
+    img -= img_mean
+    img /= img_std
+    return img.astype(np.float32)
+
+
+def get_color_map_list(num_classes):
+    color_map = num_classes * [0, 0, 0]
+    for i in range(0, num_classes):
+        j = 0
+        lab = i
+        while lab:
+            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
+            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
+            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
+            j += 1
+            lab >>= 3
+    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
+    return color_map
+
+
+def draw_box(srcimg, results, class_label):
+    label_list = list(
+        map(lambda x: x.strip(), open(class_label, 'r').readlines()))
+    for i in range(len(results)):
+        color_list = get_color_map_list(len(label_list))
+        clsid2color = {}
+        classid, conf = int(results[i, 0]), results[i, 1]
+        xmin, ymin, xmax, ymax = int(results[i, 2]), int(results[i, 3]), int(
+            results[i, 4]), int(results[i, 5])
+
+        if classid not in clsid2color:
+            clsid2color[classid] = color_list[classid]
+        color = tuple(clsid2color[classid])
+
+        cv2.rectangle(srcimg, (xmin, ymin), (xmax, ymax), color, thickness=2)
+        print(label_list[classid] + ': ' + str(round(conf, 3)))
+        cv2.putText(
+            srcimg,
+            label_list[classid] + ':' + str(round(conf, 3)), (xmin, ymin - 10),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.8, (0, 255, 0),
+            thickness=2)
+    return srcimg
+
+
+def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
+    """
+    Args:
+        box_scores (N, 5): boxes in corner-form and probabilities.
+        iou_threshold: intersection over union threshold.
+        top_k: keep top_k results. If k <= 0, keep all the results.
+        candidate_size: only consider the candidates with the highest scores.
+    Returns:
+         picked: a list of indexes of the kept boxes
+    """
+    scores = box_scores[:, -1]
+    boxes = box_scores[:, :-1]
+    picked = []
+    indexes = np.argsort(scores)
+    indexes = indexes[-candidate_size:]
+    while len(indexes) > 0:
+        current = indexes[-1]
+        picked.append(current)
+        if 0 < top_k == len(picked) or len(indexes) == 1:
+            break
+        current_box = boxes[current, :]
+        indexes = indexes[:-1]
+        rest_boxes = boxes[indexes, :]
+        iou = iou_of(
+            rest_boxes,
+            np.expand_dims(
+                current_box, axis=0), )
+        indexes = indexes[iou <= iou_threshold]
+
+    return box_scores[picked, :]
+
+
+def iou_of(boxes0, boxes1, eps=1e-5):
+    """Return intersection-over-union (Jaccard index) of boxes.
+    Args:
+        boxes0 (N, 4): ground truth boxes.
+        boxes1 (N or 1, 4): predicted boxes.
+        eps: a small number to avoid 0 as denominator.
+    Returns:
+        iou (N): IoU values.
+    """
+    overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])
+    overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])
+
+    overlap_area = area_of(overlap_left_top, overlap_right_bottom)
+    area0 = area_of(boxes0[..., :2], boxes0[..., 2:])
+    area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
+    return overlap_area / (area0 + area1 - overlap_area + eps)
+
+
+def area_of(left_top, right_bottom):
+    """Compute the areas of rectangles given two corners.
+    Args:
+        left_top (N, 2): left top corner.
+        right_bottom (N, 2): right bottom corner.
+    Returns:
+        area (N): return the area.
+    """
+    hw = np.clip(right_bottom - left_top, 0.0, None)
+    return hw[..., 0] * hw[..., 1]
+
+
+class PicoDetNMS(object):
+    """
+    Args:
+        input_shape (int): network input image size
+        scale_factor (float): scale factor of ori image
+    """
+
+    def __init__(self,
+                 input_shape,
+                 scale_x,
+                 scale_y,
+                 strides=[8, 16, 32, 64],
+                 score_threshold=0.4,
+                 nms_threshold=0.5,
+                 nms_top_k=1000,
+                 keep_top_k=100):
+        self.input_shape = input_shape
+        self.scale_x = scale_x
+        self.scale_y = scale_y
+        self.strides = strides
+        self.score_threshold = score_threshold
+        self.nms_threshold = nms_threshold
+        self.nms_top_k = nms_top_k
+        self.keep_top_k = keep_top_k
+
+    def __call__(self, decode_boxes, select_scores):
+        batch_size = 1
+        out_boxes_list = []
+        for batch_id in range(batch_size):
+            # nms
+            bboxes = np.concatenate(decode_boxes, axis=0)
+            confidences = np.concatenate(select_scores, axis=0)
+            picked_box_probs = []
+            picked_labels = []
+            for class_index in range(0, confidences.shape[1]):
+                probs = confidences[:, class_index]
+                mask = probs > self.score_threshold
+                probs = probs[mask]
+                if probs.shape[0] == 0:
+                    continue
+                subset_boxes = bboxes[mask, :]
+                box_probs = np.concatenate(
+                    [subset_boxes, probs.reshape(-1, 1)], axis=1)
+                box_probs = hard_nms(
+                    box_probs,
+                    iou_threshold=self.nms_threshold,
+                    top_k=self.keep_top_k, )
+                picked_box_probs.append(box_probs)
+                picked_labels.extend([class_index] * box_probs.shape[0])
+
+            if len(picked_box_probs) == 0:
+                out_boxes_list.append(np.empty((0, 4)))
+
+            else:
+                picked_box_probs = np.concatenate(picked_box_probs)
+
+                # resize output boxes
+                picked_box_probs[:, 0] *= self.scale_x
+                picked_box_probs[:, 2] *= self.scale_x
+                picked_box_probs[:, 1] *= self.scale_y
+                picked_box_probs[:, 3] *= self.scale_y
+
+                # clas score box
+                out_boxes_list.append(
+                    np.concatenate(
+                        [
+                            np.expand_dims(
+                                np.array(picked_labels),
+                                axis=-1), np.expand_dims(
+                                    picked_box_probs[:, 4], axis=-1),
+                            picked_box_probs[:, :4]
+                        ],
+                        axis=1))
+
+        out_boxes_list = np.concatenate(out_boxes_list, axis=0)
+        return out_boxes_list
+
+
+def detect(img_file, compiled_model, class_label):
+    output = compiled_model.infer_new_request({0: test_image})
+    result_ie = list(output.values())
+
+    decode_boxes = []
+    select_scores = []
+    num_outs = int(len(result_ie) / 2)
+    for out_idx in range(num_outs):
+        decode_boxes.append(result_ie[out_idx])
+        select_scores.append(result_ie[out_idx + num_outs])
+
+    image = cv2.imread(img_file, 1)
+    scale_x = image.shape[1] / test_image.shape[3]
+    scale_y = image.shape[0] / test_image.shape[2]
+
+    nms = PicoDetNMS(test_image.shape[2:], scale_x, scale_y)
+    np_boxes = nms(decode_boxes, select_scores)
+
+    res_image = draw_box(image, np_boxes, class_label)
+
+    cv2.imwrite('res.jpg', res_image)
+    cv2.imshow("res", res_image)
+    cv2.waitKey()
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--img_path',
+        type=str,
+        default='../../demo_onnxruntime/imgs/bus.jpg',
+        help="image path")
+    parser.add_argument(
+        '--onnx_path',
+        type=str,
+        default='out_onnxsim_infer/picodet_s_320_postproccesed_woNMS.onnx',
+        help="onnx filepath")
+    parser.add_argument('--in_shape', type=int, default=320, help="input_size")
+    parser.add_argument(
+        '--class_label',
+        type=str,
+        default='coco_label.txt',
+        help="class label file")
+    args = parser.parse_args()
+
+    ie = Core()
+    net = ie.read_model(args.onnx_path)
+    test_image = image_preprocess(args.img_path, args.in_shape)
+    compiled_model = ie.compile_model(net, 'CPU')
+
+    detect(args.img_path, compiled_model, args.class_label)