CODEWITHSUNDEEP
pythonopencvperspective
jonas_he23
jonas_he23

Reputation: 5

OpenCV perspective transform

I am currently working on creating a python software that tracks players on a soccer field. I got the player detection working with YoloV3 and was able to output quite a nice result with players centroids and boxes drawn. What i want to do now is translate the players position and project their centroids onto a png/jpg of a soccerfield. For this I inteded to use two arrays with refrence points one for the soccerfield-image and one for the source video. But my question now is how do I translate the coordinates of the centroids to the soccerfield image.

Similiar example: Example How the boxes and Markers are drawn:

def draw_labels_and_boxes(img, boxes, confidences, classids, idxs, colors, labels):
    # If there are any detections
    if len(idxs) > 0:
        for i in idxs.flatten():
            # Get the bounding box coordinates
            x, y = boxes[i][0], boxes[i][1]
            w, h = boxes[i][2], boxes[i][3]

            # Draw the bounding box rectangle and label on the image
            cv.rectangle(img, (x, y), (x + w, y + h), (255, 255, 255), 2)
            cv.drawMarker (img, (int(x + w / 2), int(y + h / 2)), (x, y), 0, 20, 3)
    return img

Boxes are generated like this:

def generate_boxes_confidences_classids(outs, height, width, tconf):
    boxes = []
    confidences = []
    classids = []

    for out in outs:
        for detection in out:
            # print (detection)
            # a = input('GO!')

            # Get the scores, classid, and the confidence of the prediction
            scores = detection[5:]
            classid = np.argmax(scores)
            confidence = scores[classid]

            # Consider only the predictions that are above a certain confidence level
            if confidence > tconf:
                # TODO Check detection
                box = detection[0:4] * np.array([width, height, width, height])
                centerX, centerY, bwidth, bheight = box.astype('int')

                # Using the center x, y coordinates to derive the top
                # and the left corner of the bounding box
                x = int(centerX - (bwidth / 2))
                y = int(centerY - (bheight / 2))

                # Append to list
                boxes.append([x, y, int(bwidth), int(bheight)])
                confidences.append(float(confidence))
                classids.append(classid)

    return boxes, confidences, classids

Upvotes: 0

Views: 1227

Answers (2)

SKG
SKG

Reputation: 163

Recently during COVID19 pandemic many developers have developed "social-distancing-monitoring-system". There a few of them also developed "Bird's Eye View" of the system. Your problem is just similar. As external links are not accepted here, so I am not able to post the exact link(s). Please check their codes in GitHub.

Upvotes: 0

Ananda
Ananda

Reputation: 3272

Assuming a stationary camera,

  • Find the coordinates of the four corners of the field.
  • Find the corresponding four corners in the top-view image that you want to create.
  • Find a homography matrix using these two sets of points. You can use OpenCV's findHomography for this.
  • Transform all the centroids using this homography matrix and that should give you your coordinates in the new image space. You can use warpPerspective for doing this.

Upvotes: 0

Related Questions