postprocess.py

def postprocess(frame, outs):
    frameHeight, frameWidth = frame.shape[:2]
 
    classIds = []
    confidences = []
    boxes = []
 
    for out in outs:
        for detection in out:
            scores = detection[5:]
            classId = np.argmax(scores)
            confidence = scores[classId]
            if confidence > threshold:
                x, y, width, height = detection[:4] * np.array([frameWidth, frameHeight, frameWidth, frameHeight])
                left = int(x - width / 2)
                top = int(y - height / 2)
                classIds.append(classId)
                confidences.append(float(confidence))
                boxes.append([left, top, int(width), int(height)])
 
    indices = cv.dnn.NMSBoxes(boxes, confidences, threshold, threshold - 0.1)
    for i in indices:
        i = i[0]
        box = boxes[i]
        left = box[0]
        top = box[1]
        width = box[2]
        height = box[3]
 
        # Draw bounding box for objects
        cv.rectangle(frame, (left, top), (left + width, top + height), (0, 0, 255), thickness)
 
        # Draw class name and confidence
        label = '%s:%.2f' % (classes[classIds[i]], confidences[i])
        cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255))