Add millimeters in solutions/distance_caculation.py + object-cropping.md visuals (#7860)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/guides/index.md

@@ -37,7 +37,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Objects Counting in Regions](region-counting.md) 🚀 NEW: Explore counting objects in specific regions with Ultralytics YOLOv8 for precise and efficient object detection in varied areas.
 - [Security Alarm System](security-alarm-system.md) 🚀 NEW: Discover the process of creating a security alarm system with Ultralytics YOLOv8. This system triggers alerts upon detecting new objects in the frame. Subsequently, you can customize the code to align with your specific use case.
 - [Heatmaps](heatmaps.md) 🚀 NEW: Elevate your understanding of data with our Detection Heatmaps! These intuitive visual tools use vibrant color gradients to vividly illustrate the intensity of data values across a matrix. Essential in computer vision, heatmaps are skillfully designed to highlight areas of interest, providing an immediate, impactful way to interpret spatial information.
-- [Instance Segmentation with Object Tracking](instance-segmentation-and-tracking.md) 🚀 NEW: Explore our feature on Object Segmentation in Bounding Boxes Shape, providing a visual representation of precise object boundaries for enhanced understanding and analysis.
+- [Instance Segmentation with Object Tracking](instance-segmentation-and-tracking.md) 🚀 NEW: Explore our feature on [Object Segmentation](https://docs.ultralytics.com/tasks/segment/) in Bounding Boxes Shape, providing a visual representation of precise object boundaries for enhanced understanding and analysis.
 - [VisionEye View Objects Mapping](vision-eye.md) 🚀 NEW: This feature aim computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.
 - [Speed Estimation](speed-estimation.md) 🚀 NEW: Speed estimation in computer vision relies on analyzing object motion through techniques like [object tracking](https://docs.ultralytics.com/modes/track/), crucial for applications like autonomous vehicles and traffic monitoring.
 - [Distance Calculation](distance-calculation.md) 🚀 NEW: Distance calculation, which involves measuring the separation between two objects within a defined space, is a crucial aspect. In the context of Ultralytics YOLOv8, the method employed for this involves using the bounding box centroid to determine the distance associated with user-highlighted bounding boxes.

docs/en/guides/object-cropping.md

@@ -16,6 +16,15 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 - **Reduced Data Volume**: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks.
 - **Enhanced Precision**: YOLOv8's object detection accuracy ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.
 
+
+## Visuals
+
+|                                                                               Airport Luggage                                                                                |                                                                                                                         
+|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| ![Conveyor Belt at Airport Suitcases Cropping using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/648f46be-f233-4307-a8e5-046eea38d2e4) |
+|                                                     Suitcases Cropping at airport conveyor belt using Ultralytics YOLOv8                                                     |                                                                                                       
+
+
 !!! Example "Object Cropping using YOLOv8 Example"
 
     === "Object Cropping"

ultralytics/solutions/distance_calculation.py

@@ -121,21 +121,7 @@ class DistanceCalculation:
             centroid2 (point): Second bounding box data
         """
         pixel_distance = math.sqrt((centroid1[0] - centroid2[0]) ** 2 + (centroid1[1] - centroid2[1]) ** 2)
-        return pixel_distance / self.pixel_per_meter
+        return pixel_distance / self.pixel_per_meter, (pixel_distance / self.pixel_per_meter) * 1000
-
-    def plot_distance_and_line(self, distance):
-        """
-        Plot the distance and line on frame
-        Args:
-            distance (float): Distance between two centroids
-        """
-        cv2.rectangle(self.im0, (15, 25), (280, 70), (255, 255, 255), -1)
-        cv2.putText(
-            self.im0, f"Distance : {distance:.2f}m", (20, 55), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2, cv2.LINE_AA
-        )
-        cv2.line(self.im0, self.centroids[0], self.centroids[1], self.line_color, 3)
-        cv2.circle(self.im0, self.centroids[0], 6, self.centroid_color, -1)
-        cv2.circle(self.im0, self.centroids[1], 6, self.centroid_color, -1)
 
     def start_process(self, im0, tracks):
         """
@@ -166,8 +152,10 @@ class DistanceCalculation:
                 centroid = self.calculate_centroid(self.selected_boxes[trk_id])
                 self.centroids.append(centroid)
 
-            distance = self.calculate_distance(self.centroids[0], self.centroids[1])
+            distance_m, distance_mm = self.calculate_distance(self.centroids[0], self.centroids[1])
-            self.plot_distance_and_line(distance)
+            self.annotator.plot_distance_and_line(
+                distance_m, distance_mm, self.centroids, self.line_color, self.centroid_color
+            )
 
         self.centroids = []
 

ultralytics/utils/plotting.py

@@ -519,6 +519,51 @@ class Annotator:
             self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1]) - 5), 0, 0.7, (255, 255, 255), 2
         )
 
+    def plot_distance_and_line(self, distance_m, distance_mm, centroids, line_color, centroid_color):
+        """
+        Plot the distance and line on frame.
+
+        Args:
+            distance_m (float): Distance between two bbox centroids in meters.
+            distance_mm (float): Distance between two bbox centroids in millimeters.
+            centroids (list): Bounding box centroids data.
+            line_color (RGB): Distance line color.
+            centroid_color (RGB): Bounding box centroid color.
+        """
+        (text_width_m, text_height_m), _ = cv2.getTextSize(
+            f"Distance M: {distance_m:.2f}m", cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2
+        )
+        cv2.rectangle(self.im, (15, 25), (15 + text_width_m + 10, 25 + text_height_m + 20), (255, 255, 255), -1)
+        cv2.putText(
+            self.im,
+            f"Distance M: {distance_m:.2f}m",
+            (20, 50),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.8,
+            (0, 0, 0),
+            2,
+            cv2.LINE_AA,
+        )
+
+        (text_width_mm, text_height_mm), _ = cv2.getTextSize(
+            f"Distance MM: {distance_mm:.2f}mm", cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2
+        )
+        cv2.rectangle(self.im, (15, 75), (15 + text_width_mm + 10, 75 + text_height_mm + 20), (255, 255, 255), -1)
+        cv2.putText(
+            self.im,
+            f"Distance MM: {distance_mm:.2f}mm",
+            (20, 100),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.8,
+            (0, 0, 0),
+            2,
+            cv2.LINE_AA,
+        )
+
+        cv2.line(self.im, centroids[0], centroids[1], line_color, 3)
+        cv2.circle(self.im, centroids[0], 6, centroid_color, -1)
+        cv2.circle(self.im, centroids[1], 6, centroid_color, -1)
+
     def visioneye(self, box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255), thickness=2, pins_radius=10):
         """
         Function for pinpoint human-vision eye mapping and plotting.