ObjectDetection.md

mediapipe_samples

• <mediapipe 0.10.0.0>
• MediaPipe Examples

MediapipeObjectDetection

class variable

• model information
  • base_url, model_name, model_folder_path
• visualize params

instance variable

• num_detected_objects: number of detected objects
• (detector: mediapipe detector)
• (results: mediapipe detector's results)

method

• __init__( arguments are optional ): constructor
  • arguments
    • model_folder_path: If you want to change the model folder path
    • base_url: If you want to change the model
    • model_name: If you want to change the model
    • max_results: Sets the optional maximum number of top-scored detection results to return.
      • Value Range: Any positive numbers
      • Default Value: -1 (all results are returned)
    • score_threshold: Sets the prediction score threshold that overrides the one provided in the model metadata (if any). Results below this value are rejected.
      • Value Range: Any float [0.0, 1.0]
      • Default Value: 0.0 (all results are detected)
• detect( image )
  • arguments
    • image: Input image (readed by cv2)
  • return values
    • results: Probably not necessary
• get_bounding_box( id_object )
  • arguments
    • id_object: Number of the object you want to get bounding box
  • return values
    • np.array([x, y, w, h]): array of the bounding box information
      • x: x-coordinate, y: y-coordinate, w: width, h: height
• category_name = get_category_name( id_object )
• category_score = get_category_score( id_object )
• annotated_image = visualize( image )
  • annotated_image: Image with bounding boxes and category names for all detected objects on the input image
• release(): Close mediapipe's detector

how to use

• simple sample

import os
os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
import cv2
from MediapipeObjectDetection import MediapipeObjectDetection as ObjDetection

cap = cv2.VideoCapture(0)
Obj = ObjDetection(score_threshold=0.5)
while cap.isOpened():
    ret, frame = cap.read()
    Obj.detect(frame)
    print(Obj.num_detected_objects)
    annotated_frame = Obj.visualize(frame)
    cv2.imshow('annotated frame', annotated_frame)
    key = cv2.waitKey(1)&0xFF
    if key == ord('q'):
        break
cv2.destroyAllWindows()
Obj.release()
cap.release()

• other sample
  • You can see it in the main function of MediapipeObjectDetection.py

specification of Mediapipe's results

object_detection

• https://developers.google.com/mediapipe/solutions/vision/object_detector
• examples of how to reference result data results
  • x-coordinate of upper left point of the i-th object's bounding box
    results.detections[i].bounding_box.origin_x
  • category_name (e.g.parson) of the i-th object
    results.detections[i].categories[0].category_name
• data structure of result
  • results
    • detections
      • 0:
        • bounding_box
          • origin_x
          • origin_y
          • width
          • height
        • categories
          • 0:
            • category_name
            • score
      • 1:
        • bounding_box
          • origin_x
          • origin_y
          • width
          • height
        • categories
          • 0:
            • category_name
            • score
      • ...