feat: visualization of the 3d follow verctor

agentic/real-sense-object-detection-and-track-service/Algorithms/TargetFollower.py

@@ -17,6 +17,9 @@ class TargetFollower:
         for info in detections:
             print(info)
 
+    def get_target(self):
+        return self.target
+
     def select_target(self, class_name, nearest=False):
         """
         Выбирает объект по указанному имени класса.

agentic/real-sense-object-detection-and-track-service/main.py

@@ -41,7 +41,8 @@ def follow_vector():
 
         return jsonify({
             'detections_info': detections_info,
-            'follow_vector': follow_vector.tolist()
+            'follow_vector': follow_vector.tolist(),
+            'target': follow_vector.get_target()
         })
 
     except Exception as e:

agentic/real-sense-object-detection-and-track-service/robot_emulation_comunication_with_server.py

@@ -1,19 +1,49 @@
+import cv2
+import numpy as np
 from Interfaces.RealSense import RealSenseController
 from Server.ServerProvider import RealSenseObjectDetectionServer
 
+
 def get_shot(controller):
     depth_matrix = controller.acquisition.get_depth_image()
     color_matrix = controller.acquisition.get_color_image()
     print(f"RS Data: {depth_matrix.shape}")
     return depth_matrix, color_matrix
 
+
+def draw_boxes_and_vector(image, detections, follow_vector):
+    for detection in detections:
+        bbox = detection['bbox']
+        class_name = detection['class_name']
+        mean_depth = detection['mean_depth']
+        x1, y1, x2, y2 = bbox
+        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        label = f"{class_name}: {mean_depth:.2f} cm"
+        cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+
+    img_center = (image.shape[1] // 2, image.shape[0] // 2)
+    vector_endpoint = (int(img_center[0] + follow_vector[0]), int(img_center[1] + follow_vector[1]))
+    cv2.arrowedLine(image, img_center, vector_endpoint, (0, 0, 255), 2, tipLength=0.3)
+    return image
+
+
+def save_results(image, depth_matrix, filename="output_image.png", depth_filename="depth_matrix.npy"):
+    cv2.imwrite(filename, image)
+    print(f"Image saved as {filename}")
+    np.save(depth_filename, depth_matrix)
+    print(f"Depth matrix saved as {depth_filename}")
+
+
 if __name__ == "__main__":
     controller = RealSenseController()
     interface = RealSenseObjectDetectionServer()
 
     depth_matrix, color_matrix = get_shot(controller)
-
     response = interface.send_images(color_matrix, depth_matrix)
 
     if response:
         print("Ответ сервера:", response)
+        detections_info = response.get("detections_info", [])
+        follow_vector = response.get("follow_vector", [0, 0, 0])
+        color_matrix_with_boxes = draw_boxes_and_vector(color_matrix.copy(), detections_info, follow_vector)
+        save_results(color_matrix_with_boxes, depth_matrix)