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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def visualize_detection(self, img, dets, classes=[], thresh=0.6):
 """ visualize detections in one image Parameters: img : numpy.array image, in bgr format dets : numpy.array each row is one object classes : tuple or list of str class names thresh : float score threshold """

        import matplotlib.pyplot as plt
        import random
        plt.imshow(img)
        height = img.shape[0]
        width = img.shape[1]
        colors = dict()
        for det in dets:
            (klass, score, x0, y0, x1, y1) = det
            if score < thresh:
                continue
            cls_id = int(klass)
            if cls_id not in colors:
                colors[cls_id] = (random.random(), random.random(), random.random())
            xmin = int(x0 * width)
            ymin = int(y0 * height)
            xmax = int(x1 * width)
            ymax = int(y1 * height)
            rect = plt.Rectangle((xmin, ymin), xmax - xmin,
                                 ymax - ymin, fill=False,
                                 edgecolor=colors[cls_id],
                                 linewidth=3.5)
            plt.gca().add_patch(rect)
            class_name = str(cls_id)
            if classes and len(classes) > cls_id:
                class_name = classes[cls_id]
            plt.gca().text(xmin, ymin - 2,
                            '{:s} {:.3f}'.format(class_name, score),
                            bbox=dict(facecolor=colors[cls_id], alpha=0.5),
                                    fontsize=12, color='white')
        plt.show()