c4dynamics.detectors.yolo3_opencv.yolov3.detect

yolov3.detect(frame: ndarray) → list[pixelpoint]

Detects objects in a frame using the YOLOv3 model.

At each call, the detector performs the following steps:

1. Preprocesses the frame by creating a blob, normalizing pixel values, and swapping Red and Blue channels.

2. Sets input to the YOLOv3 model and performs a forward pass to obtain detections.

3. Extracts detected objects based on a confidence threshold, calculates bounding box coordinates, and filters results using Non-Maximum Suppression (NMS).

Parameters:

frame (numpy.array) – An input frame for object detection.

Returns:

out (list[pixelpoint]) – A list of pixelpoint objects representing detected objects, each containing bounding box coordinates and class label.

Examples

Setup

Import required packages:

>>> import cv2  # opencv-python
>>> import c4dynamics as c4d
>>> from matplotlib import pyplot as plt

Fetch ‘planes.png’ and ‘aerobatics.mp4’ using the c4dynamics’ datasets module (see c4dynamics.datasets):

>>> impath = c4d.datasets.image('planes')
Fetched successfully
>>> vidpath = c4d.datasets.video('aerobatics')
Fetched successfully

Load YOLOv3 detector:

>>> yolo3 = c4d.detectors.yolov3()
Fetched successfully

Let the auxiliary function:

>>> def ptup(n): return '(' + str(n[0]) + ', ' + str(n[1]) + ')'

Object detection in a single frame

>>> img = cv2.imread(impath)
>>> pts = yolo3.detect(img)
>>> for p in pts:
...   cv2.rectangle(img, p.box[0], p.box[1], [0, 255, 0], 2) 

>>> plt.figure() 
>>> plt.axis(False) 
>>> plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) 

Object detection in a video

>>> video_cap = cv2.VideoCapture(vidpath)
>>> while video_cap.isOpened():
...   ret, frame = video_cap.read()
...   if not ret: break
...   pts = yolo3.detect(frame)
...   for p in pts:
...     cv2.rectangle(frame, p.box[0], p.box[1], [0, 255, 0], 2) 
...     cv2.imshow('YOLOv3', frame)  
...   cv2.waitKey(10) 

The output structure

The output of the detect() function is a list of pixelpoint object. The pixelpoint has unique attributes to manipulate the detected object class and bounding box.

>>> print('{:^10} | {:^10} | {:^10} | {:^16} | {:^16} | {:^10} | {:^14}' 
...             .format('# object', 'center x', 'center y', 'box top-left', 'box bottom-right', 'class', 'frame size'))
>>> # main loop:
>>> for i, p in enumerate(pts):
...   print('{:^10d} | {:^10.3f} | {:^10.3f} | {:^16} | {:^16} | {:^10} | {:^14}'
...         .format(i, p.x, p.y, ptup(p.box[0]), ptup(p.box[1]), p.class_id, ptup(p.fsize)))
...   cv2.rectangle(img, p.box[0], p.box[1], [0, 0, 0], 2)      
...   point = (int((p.box[0][0] + p.box[1][0]) / 2 - 75), p.box[1][1] + 22)
...   cv2.putText(img, p.class_id, point, cv2.FONT_HERSHEY_SIMPLEX, 1, [0, 0, 0], 2)  
# object  |  center x  |  center y  |   box top-left   | box bottom-right |   class    |  frame size
   0      |   0.584    |   0.376    |    (691, 234)    |    (802, 306)    | aeroplane  |  (1280, 720)
   1      |   0.457    |   0.473    |    (528, 305)    |    (642, 376)    | aeroplane  |  (1280, 720)
   2      |   0.471    |   0.322    |    (542, 196)    |    (661, 267)    | aeroplane  |  (1280, 720)
   3      |   0.546    |   0.873    |    (645, 588)    |    (752, 668)    | aeroplane  |  (1280, 720)

>>> plt.figure()  
>>> plt.axis(False) 
>>> plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))