UPDATE openvino

Vision/camera_coordinate_dete.py

@@ -13,35 +13,49 @@ import cv2
 import open3d as o3d
 from Vision.tool.CameraRVC import camera
 from Vision.yolo.yolov8_pt_seg import yolov8_segment
+from Vision.yolo.yolov8_openvino import yolov8_segment_openvino
 from Vision.tool.utils import find_position
-
+from Vision.tool.utils import class_names
+import os
 
 class Detection:
 
-    def __init__(self):
+    def __init__(self, use_openvino_model = False):
-        model_path = './pt_model/best.pt'
+        print(os.getcwd())
+        self.use_openvino_model = use_openvino_model
+        if self.use_openvino_model == False:
+            model_path = ''.join([os.getcwd(), '/model/pt/best.pt'])
             device = 'cpu'
-        self.camera_rvc = camera()
             self.model = yolov8_segment()
             self.model.load_model(model_path, device)
-        # if self.camera_rvc.caminit_isok == True:
+        else:
-        #     print("RVC X Camera is opened!")
+            model_path = ''.join([os.getcwd(), '/model/openvino/bset.xml'])
-        # else:
+            device = 'CPU'
-        #     print("RVC X Camera is not opened!")
+            self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
 
-    def get_position(self, Point_isVision=False, first_depth=False):
+        img_path = ''.join([os.getcwd(), '/model/data/test0910.png'])
+        point_path = ''.join([os.getcwd(), '/model/data/test0910.xyz'])
+        self.img = cv2.imread(img_path)
+        self.point = np.loadtxt(point_path).reshape((1080, 1440, 3))
+
+
+
+    def get_position(self, Point_isVision=True):
         ""
         '''
-        :param api: 
+        :param api: None
-            Point_isVision：   点云可视化
-            first_depth：      按Z轴深度返回坐标及向量
-        
         :return: ret , img, (x,y,z), (nx, ny, nz)
         '''
-        ret, img, pm = self.camera_rvc.get_img_and_point_map()            # 拍照，获取图像及
+        #ret, img, pm = self.camera_rvc.get_img_and_point_map()            # 拍照，获取图像及
-        if self.camera_rvc.caminit_isok == True:
+        ret = 1
+        img = self.img
+        pm = self.point
         if ret == 1:
+            if self.use_openvino_model == False:
                 flag, det_cpu, dst_img, masks, category_names = self.model.model_inference(img, 0)
+            else:
+                flag, det_cpu, scores, masks, category_names = self.model.segment_objects(img)
+                dst_img = img
             if flag == 1:
                 xyz = []
                 nx_ny_nz = []
@@ -62,7 +76,10 @@ class Detection:
 
                     # 画box
                     box_x1, box_y1, box_x2, box_y2 = item[0:4].astype(np.int32)
+                    if self.use_openvino_model == False:
                         label = category_names[int(item[5])]
+                    else:
+                        label = class_names[int(item[4])]
                     rand_color = (0, 255, 255)
                     score = item[4]
                     org = (int((box_x1 + box_x2) / 2), int((box_y1 + box_y2) / 2))
@@ -74,9 +91,13 @@ class Detection:
                                 thickness=2)
                     # 画mask
                     # mask = masks[i].cpu().numpy().astype(int)
+                    if self.use_openvino_model == False:
                         mask = masks[i].cpu().data.numpy().astype(int)
+                    else:
+                        mask = masks[i].astype(int)
+                        mask = mask[box_y1:box_y2, box_x1:box_x2]
+
                     # mask = masks[i].numpy().astype(int)
-                        bbox_image = img[box_y1:box_y2, box_x1:box_x2]
                     h, w = box_y2 - box_y1, box_x2 - box_x1
                     mask_colored = np.zeros((h, w, 3), dtype=np.uint8)
                     mask_colored[np.where(mask)] = rand_color
@@ -113,9 +134,6 @@ class Detection:
                     pm_seg = select_point.reshape(-1, 3)
                     pm_seg = pm_seg[~np.isnan(pm_seg).all(axis=-1), :]  # 剔除 nan
                     # cv2.imshow('result', piont_result)
-                        if pm_seg.size < 300:
-                            print("分割出的点云数量较少，无法拟合平面！")
-                            continue
 
                     '''
                     拟合平面，计算法向量
@@ -163,12 +181,12 @@ class Detection:
                         seg_point.append(pm_seg)
                     cv2.polylines(img, [box], True, (0, 255, 0), 2)
 
-                    _idx = find_position(Depth_Z, RegionalArea, 100, first_depth)
+                _idx = find_position(Depth_Z, RegionalArea, 100, True)
 
                 if _idx == None:
                     return 1, img, None, None
                 else:
-                        cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (255, 0, 0), 20)  # 标出中心点
+                    cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20)  # 标出中心点
 
                     if Point_isVision == True:
                         pcd = o3d.geometry.PointCloud()
@@ -190,12 +208,7 @@ class Detection:
             print("RVC X Camera capture failed!")
             return 0, None, None, None
 
-        else:
-            print("RVC X Camera is not opened!")
-            return 0, None, None, None
-
     def release(self):
-        self.camera_rvc.release()
         self.model.clear()
 
 

Vision/camera_coordinate_dete_img.py

@@ -13,17 +13,27 @@ import cv2
 import open3d as o3d
 from Vision.tool.CameraRVC import camera
 from Vision.yolo.yolov8_pt_seg import yolov8_segment
+from Vision.yolo.yolov8_openvino import yolov8_segment_openvino
 from Vision.tool.utils import find_position
+from Vision.tool.utils import class_names
+import os
 
 class Detection:
 
-    def __init__(self):
+    def __init__(self, use_openvino_model = False):
-        model_path = './pt_model/best.pt'
+        self.use_openvino_model = use_openvino_model
+        if self.use_openvino_model == False:
+            model_path = ''.join([os.getcwd(), '/model/pt/best.pt'])
             device = 'cpu'
-        img_path = './pt_model/test0910.png'
-        point_path = './pt_model/test0910.xyz'
             self.model = yolov8_segment()
             self.model.load_model(model_path, device)
+        else:
+            model_path = ''.join([os.getcwd(), '/model/openvino/best.xml'])
+            device = 'CPU'
+            self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
+
+        img_path = ''.join([os.getcwd(), '/model/data/test0910.png'])
+        point_path = ''.join([os.getcwd(), '/model/data/test0910.xyz'])
         self.img = cv2.imread(img_path)
         self.point = np.loadtxt(point_path).reshape((1080, 1440, 3))
 
@@ -39,9 +49,12 @@ class Detection:
         ret = 1
         img = self.img
         pm = self.point
-
         if ret == 1:
+            if self.use_openvino_model == False:
                 flag, det_cpu, dst_img, masks, category_names = self.model.model_inference(img, 0)
+            else:
+                flag, det_cpu, scores, masks, category_names = self.model.segment_objects(img)
+                dst_img = img
             if flag == 1:
                 xyz = []
                 nx_ny_nz = []
@@ -62,7 +75,10 @@ class Detection:
 
                     # 画box
                     box_x1, box_y1, box_x2, box_y2 = item[0:4].astype(np.int32)
+                    if self.use_openvino_model == False:
                         label = category_names[int(item[5])]
+                    else:
+                        label = class_names[int(item[4])]
                     rand_color = (0, 255, 255)
                     score = item[4]
                     org = (int((box_x1 + box_x2) / 2), int((box_y1 + box_y2) / 2))
@@ -74,9 +90,13 @@ class Detection:
                                 thickness=2)
                     # 画mask
                     # mask = masks[i].cpu().numpy().astype(int)
+                    if self.use_openvino_model == False:
                         mask = masks[i].cpu().data.numpy().astype(int)
+                    else:
+                        mask = masks[i].astype(int)
+                        mask = mask[box_y1:box_y2, box_x1:box_x2]
+
                     # mask = masks[i].numpy().astype(int)
-                    bbox_image = img[box_y1:box_y2, box_x1:box_x2]
                     h, w = box_y2 - box_y1, box_x2 - box_x1
                     mask_colored = np.zeros((h, w, 3), dtype=np.uint8)
                     mask_colored[np.where(mask)] = rand_color
@@ -113,9 +133,7 @@ class Detection:
                     pm_seg = select_point.reshape(-1, 3)
                     pm_seg = pm_seg[~np.isnan(pm_seg).all(axis=-1), :]  # 剔除 nan
                     # cv2.imshow('result', piont_result)
-                    if pm_seg.size < 300:
+
-                        print("分割出的点云数量较少，无法拟合平面！")
-                        continue
                     '''
                     拟合平面，计算法向量
                     '''
@@ -167,7 +185,7 @@ class Detection:
                 if _idx == None:
                     return 1, img, None, None
                 else:
-                    cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (255, 0, 0), 20)  # 标出中心点
+                    cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20)  # 标出中心点
 
                     if Point_isVision == True:
                         pcd = o3d.geometry.PointCloud()
@@ -185,6 +203,10 @@ class Detection:
             else:
                 return 1, img, None, None
 
+        else:
+            print("RVC X Camera capture failed!")
+            return 0, None, None, None
+
     def release(self):
         self.model.clear()
 

Vision/model/openvino/metadata.yaml

@@ -0,0 +1,14 @@
+description: Ultralytics YOLOv8n-seg model trained on D:\work\ultralytics-main\ultralytics\cfg\datasets\coco8-seg-dy.yaml
+author: Ultralytics
+date: '2024-09-10T13:48:59.901222'
+version: 8.2.86
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: segment
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: box

Vision/tool/utils.py

@@ -8,6 +8,9 @@
 @Date    ：2024/8/29 15:07
 '''
 
+import numpy as np
+import cv2
+
 def find_position(Depth_Z, RegionalArea, RegionalArea_Threshold, first_depth=True):
     if first_depth == True:
         sorted_id = sorted(range(len(Depth_Z)), key=lambda k: Depth_Z[k], reverse=False)
@@ -25,6 +28,157 @@ def find_position(Depth_Z, RegionalArea, RegionalArea_Threshold, first_depth=Tru
             if RegionalArea1[i] > RegionalArea_Threshold:
                 return sorted_id[i]
 
+class_names = ['box', 'other']
+
+# Create a list of colors for each class where each color is a tuple of 3 integer values
+rng = np.random.default_rng(3)
+colors = rng.uniform(0, 255, size=(len(class_names), 3))
+
+
+def nms(boxes, scores, iou_threshold):
+    # Sort by score
+    sorted_indices = np.argsort(scores)[::-1]
+
+    keep_boxes = []
+    while sorted_indices.size > 0:
+        # Pick the last box
+        box_id = sorted_indices[0]
+        keep_boxes.append(box_id)
+
+        # Compute IoU of the picked box with the rest
+        ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])
+
+        # Remove boxes with IoU over the threshold
+        keep_indices = np.where(ious < iou_threshold)[0]
+
+        # print(keep_indices.shape, sorted_indices.shape)
+        sorted_indices = sorted_indices[keep_indices + 1]
+
+    return keep_boxes
+
+
+def compute_iou(box, boxes):
+    # Compute xmin, ymin, xmax, ymax for both boxes
+    xmin = np.maximum(box[0], boxes[:, 0])
+    ymin = np.maximum(box[1], boxes[:, 1])
+    xmax = np.minimum(box[2], boxes[:, 2])
+    ymax = np.minimum(box[3], boxes[:, 3])
+
+    # Compute intersection area
+    intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
+
+    # Compute union area
+    box_area = (box[2] - box[0]) * (box[3] - box[1])
+    boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+    union_area = box_area + boxes_area - intersection_area
+
+    # Compute IoU
+    iou = intersection_area / union_area
+
+    return iou
+
+
+def xywh2xyxy(x):
+    # Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
+    y = np.copy(x)
+    y[..., 0] = x[..., 0] - x[..., 2] / 2
+    y[..., 1] = x[..., 1] - x[..., 3] / 2
+    y[..., 2] = x[..., 0] + x[..., 2] / 2
+    y[..., 3] = x[..., 1] + x[..., 3] / 2
+    return y
+
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+
+def draw_detections(image, boxes, scores, class_ids, mask_alpha=0.3, mask_maps=None):
+    img_height, img_width = image.shape[:2]
+    size = min([img_height, img_width]) * 0.0006
+    text_thickness = int(min([img_height, img_width]) * 0.001)
+
+    mask_img = draw_masks(image, boxes, class_ids, mask_alpha, mask_maps)
+
+    # Draw bounding boxes and labels of detections
+    for box, score, class_id in zip(boxes, scores, class_ids):
+        color = colors[class_id]
+
+        x1, y1, x2, y2 = box.astype(int)
+
+        # Draw rectangle
+        cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, 2)
+
+        label = class_names[class_id]
+        caption = f'{label} {int(score * 100)}%'
+        (tw, th), _ = cv2.getTextSize(text=caption, fontFace=cv2.FONT_HERSHEY_SIMPLEX,
+                                      fontScale=size, thickness=text_thickness)
+        th = int(th * 1.2)
+
+        cv2.rectangle(mask_img, (x1, y1),
+                      (x1 + tw, y1 - th), color, -1)
+
+        cv2.putText(mask_img, caption, (x1, y1),
+                    cv2.FONT_HERSHEY_SIMPLEX, size, (255, 255, 255), text_thickness, cv2.LINE_AA)
+
+    return mask_img
+
+
+def draw_masks(image, boxes, class_ids, mask_alpha=0.3, mask_maps=None):
+    mask_img = image.copy()
+
+    # Draw bounding boxes and labels of detections
+    for i, (box, class_id) in enumerate(zip(boxes, class_ids)):
+        color = colors[class_id]
+
+        x1, y1, x2, y2 = box.astype(int)
+
+        # Draw fill mask image
+        if mask_maps is None:
+            cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1)
+        else:
+            crop_mask = mask_maps[i][y1:y2, x1:x2, np.newaxis]
+            crop_mask_img = mask_img[y1:y2, x1:x2]
+            crop_mask_img = crop_mask_img * (1 - crop_mask) + crop_mask * color
+            mask_img[y1:y2, x1:x2] = crop_mask_img
+
+    return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0)
+
+
+def draw_comparison(img1, img2, name1, name2, fontsize=2.6, text_thickness=3):
+    (tw, th), _ = cv2.getTextSize(text=name1, fontFace=cv2.FONT_HERSHEY_DUPLEX,
+                                  fontScale=fontsize, thickness=text_thickness)
+    x1 = img1.shape[1] // 3
+    y1 = th
+    offset = th // 5
+    cv2.rectangle(img1, (x1 - offset * 2, y1 + offset),
+                  (x1 + tw + offset * 2, y1 - th - offset), (0, 115, 255), -1)
+    cv2.putText(img1, name1,
+                (x1, y1),
+                cv2.FONT_HERSHEY_DUPLEX, fontsize,
+                (255, 255, 255), text_thickness)
+
+    (tw, th), _ = cv2.getTextSize(text=name2, fontFace=cv2.FONT_HERSHEY_DUPLEX,
+                                  fontScale=fontsize, thickness=text_thickness)
+    x1 = img2.shape[1] // 3
+    y1 = th
+    offset = th // 5
+    cv2.rectangle(img2, (x1 - offset * 2, y1 + offset),
+                  (x1 + tw + offset * 2, y1 - th - offset), (94, 23, 235), -1)
+
+    cv2.putText(img2, name2,
+                (x1, y1),
+                cv2.FONT_HERSHEY_DUPLEX, fontsize,
+                (255, 255, 255), text_thickness)
+
+    combined_img = cv2.hconcat([img1, img2])
+    if combined_img.shape[1] > 3840:
+        combined_img = cv2.resize(combined_img, (3840, 2160))
+
+    return combined_img
+
+
+
+
 
 
 

Vision/yolo/yolov8_openvino.py

@@ -0,0 +1,232 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+'''
+# @Time    : 2024/9/10 16:33
+# @Author  : hjw
+# @File    : yolov8_openvino.py
+'''
+import math
+import time
+import cv2
+import numpy as np
+from openvino.runtime import Core
+from Vision.tool.utils import xywh2xyxy, nms, draw_detections, sigmoid
+
+
+class yolov8_segment_openvino:
+
+    def __init__(self, path, device_name="CPU", conf_thres=0.7, iou_thres=0.5, num_masks=32):
+        self.conf_threshold = conf_thres
+        self.iou_threshold = iou_thres
+        self.num_masks = num_masks
+
+        # Initialize model
+        self.initialize_model(path, device_name)
+
+    def __call__(self, image):
+        return self.segment_objects(image)
+
+    def initialize_model(self, path, device_name):
+        self.core = Core()
+        self.net = self.core.compile_model(path, device_name=device_name)
+        self.ir = self.net.create_infer_request()
+        input_shape = self.net.inputs[0].shape
+        self.input_height = input_shape[2]
+        self.input_width = input_shape[3]
+
+    def segment_objects(self, image):
+        input_tensor = self.prepare_input(image)
+
+        # Perform inference on the image
+        outputs = self.ir.infer(input_tensor)
+
+        self.boxes, self.scores, self.class_ids, mask_pred = self.process_box_output(outputs[0])
+        self.mask_maps = self.process_mask_output(mask_pred, outputs[1])
+        self.boxes = self.boxes.tolist()
+        self.scores = self.scores.tolist()
+        for t in range(len(self.scores)):
+            self.boxes[t].append(self.scores[t])
+
+        self.boxes = np.array(self.boxes)
+        if len(self.boxes) == 0:
+            return 0, None, None, None, None
+        else:
+            return 1, self.boxes, self.scores, self.mask_maps, self.class_ids
+
+
+    def prepare_input(self, image):
+        self.img_height, self.img_width = image.shape[:2]
+
+        input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        # Resize input image
+        input_img = cv2.resize(input_img, (self.input_width, self.input_height))
+
+        # Scale input pixel values to 0 to 1
+        input_img = input_img / 255.0
+        input_img = input_img.transpose(2, 0, 1)
+        input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32)
+
+        return input_tensor
+
+    def process_box_output(self, box_output):
+
+        predictions = np.squeeze(box_output).T
+        num_classes = box_output.shape[1] - self.num_masks - 4
+
+        # Filter out object confidence scores below threshold
+        scores = np.max(predictions[:, 4:4+num_classes], axis=1)
+        predictions = predictions[scores > self.conf_threshold, :]
+        scores = scores[scores > self.conf_threshold]
+
+        if len(scores) == 0:
+            return [], [], [], np.array([])
+
+        box_predictions = predictions[..., :num_classes+4]
+        mask_predictions = predictions[..., num_classes+4:]
+
+        # Get the class with the highest confidence
+        class_ids = np.argmax(box_predictions[:, 4:], axis=1)
+
+        # Get bounding boxes for each object
+        boxes = self.extract_boxes(box_predictions)
+
+        # Apply non-maxima suppression to suppress weak, overlapping bounding boxes
+        indices = nms(boxes, scores, self.iou_threshold)
+
+        return boxes[indices], scores[indices], class_ids[indices], mask_predictions[indices]
+
+    def process_mask_output(self, mask_predictions, mask_output):
+
+        if mask_predictions.shape[0] == 0:
+            return []
+
+        mask_output = np.squeeze(mask_output)
+
+        # Calculate the mask maps for each box
+        num_mask, mask_height, mask_width = mask_output.shape  # CHW
+        masks = sigmoid(mask_predictions @ mask_output.reshape((num_mask, -1)))
+        masks = masks.reshape((-1, mask_height, mask_width))
+
+        # Downscale the boxes to match the mask size
+        scale_boxes = self.rescale_boxes(self.boxes,
+                                   (self.img_height, self.img_width),
+                                   (mask_height, mask_width))
+
+        # For every box/mask pair, get the mask map
+        mask_maps = np.zeros((len(scale_boxes), self.img_height, self.img_width))
+        blur_size = (int(self.img_width / mask_width), int(self.img_height / mask_height))
+        for i in range(len(scale_boxes)):
+
+            scale_x1 = int(math.floor(scale_boxes[i][0]))
+            scale_y1 = int(math.floor(scale_boxes[i][1]))
+            scale_x2 = int(math.ceil(scale_boxes[i][2]))
+            scale_y2 = int(math.ceil(scale_boxes[i][3]))
+
+            x1 = int(math.floor(self.boxes[i][0]))
+            y1 = int(math.floor(self.boxes[i][1]))
+            x2 = int(math.ceil(self.boxes[i][2]))
+            y2 = int(math.ceil(self.boxes[i][3]))
+
+            scale_crop_mask = masks[i][scale_y1:scale_y2, scale_x1:scale_x2]
+            crop_mask = cv2.resize(scale_crop_mask,
+                              (x2 - x1, y2 - y1),
+                              interpolation=cv2.INTER_CUBIC)
+
+            crop_mask = cv2.blur(crop_mask, blur_size)
+
+            crop_mask = (crop_mask > 0.5).astype(np.uint8)
+            mask_maps[i, y1:y2, x1:x2] = crop_mask
+        return mask_maps
+
+    def extract_boxes(self, box_predictions):
+        # Extract boxes from predictions
+        boxes = box_predictions[:, :4]
+
+        # Scale boxes to original image dimensions
+        boxes = self.rescale_boxes(boxes,
+                                   (self.input_height, self.input_width),
+                                   (self.img_height, self.img_width))
+
+        # Convert boxes to xyxy format
+        boxes = xywh2xyxy(boxes)
+
+        # Check the boxes are within the image
+        boxes[:, 0] = np.clip(boxes[:, 0], 0, self.img_width)
+        boxes[:, 1] = np.clip(boxes[:, 1], 0, self.img_height)
+        boxes[:, 2] = np.clip(boxes[:, 2], 0, self.img_width)
+        boxes[:, 3] = np.clip(boxes[:, 3], 0, self.img_height)
+        return boxes
+
+    def draw_detections(self, image, draw_scores=True, mask_alpha=0.4):
+        return draw_detections(image, self.boxes, self.scores,
+                               self.class_ids, mask_alpha)
+
+    def draw_masks(self, image, draw_scores=True, mask_alpha=0.5):
+        return draw_detections(image, self.boxes, self.scores,
+                               self.class_ids, mask_alpha, mask_maps=self.mask_maps)
+
+    @staticmethod
+    def rescale_boxes(boxes, input_shape, image_shape):
+        # Rescale boxes to original image dimensions
+        input_shape = np.array([input_shape[1], input_shape[0], input_shape[1], input_shape[0]])
+        boxes = np.divide(boxes, input_shape, dtype=np.float32)
+        boxes *= np.array([image_shape[1], image_shape[0], image_shape[1], image_shape[0]])
+        return boxes
+
+# frame = cv2.imread('../1.png')
+# # frame = cv2.resize(frame,(640,640))
+# model_path = ("../runs/segment/train2/weights/last-0903_openvino_model/last-0903.xml")
+# #model_path = ("../yolov8n-seg_openvino_model/yolov8n-seg.xml")
+# device_name = "GPU"
+# yoloseg = yolov8_segment_openvino(model_path, device_name, conf_thres=0.3, iou_thres=0.3)
+#
+# start = time.time()
+# boxes, scores, class_ids, masks = yoloseg(frame)
+# # postprocess and draw masks
+# combined_img = yoloseg.draw_masks(frame)
+# end = time.time()
+# # show FPS
+# print(end- start)
+# fps = (1 / (end - start))
+# fps_label = "Throughput: %.2f FPS" % fps
+# cv2.putText(combined_img, fps_label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+# cv2.imwrite('rs2.jpg', combined_img)
+# # show ALL
+# cv2.imshow("YOLOv8 Segmentation OpenVINO inference Demo", combined_img)
+#
+# cv2.waitKey(0)
+
+# # Initialize the VideoCapture
+# cap = cv2.VideoCapture("store-aisle-detection.mp4")
+# img = cv2.imread('../1.png')
+# # Initialize YOLOv5 Instance Segmentator
+# model_path = "yolov8n-seg.xml"
+# device_name = "GPU"
+# yoloseg = YOLOSeg(model_path, device_name, conf_thres=0.3, iou_thres=0.3)
+
+# while cap.isOpened():
+#     # Read frame from the video
+#     ret, frame = cap.read()
+#     if not ret:
+#         break
+#     # Update object localizer
+#     start = time.time()
+#     boxes, scores, class_ids, masks = yoloseg(frame)
+#     # postprocess and draw masks
+#     combined_img = yoloseg.draw_masks(frame)
+#     end = time.time()
+#     # show FPS
+#     fps = (1 / (end - start))
+#     fps_label = "Throughput: %.2f FPS" % fps
+#     cv2.putText(combined_img, fps_label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+#     # show ALL
+#     cv2.imshow("YOLOv8 Segmentation OpenVINO inference Demo", combined_img)
+#
+#     # Press Any key stop
+#     if cv2.waitKey(1) > -1:
+#         print("finished by user")
+#         break
+#
+# cap.release()
+# cv2.destroyAllWindows()