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How to use is_pad method in Airtest

Best Python code snippet using Airtest

utils.py

Source:utils.py

1import cv22import numpy as np3import matplotlib.pyplot as plt4def vis_detections(im, dets, thresh=0.5, show_text=True):5    """Draw detected bounding boxes."""6    class_name = 'face'7    inds = np.where(dets[:, -1] >= thresh)[0] if dets is not None else []8    if len(inds) == 0:9        return10    im = im[:, :, (2, 1, 0)]11    fig, ax = plt.subplots(figsize=(12, 12))12    ax.imshow(im, aspect='equal')13    for i in inds:14        bbox = dets[i, :4]15        score = dets[i, -1]16        ax.add_patch(17            plt.Rectangle((bbox[0], bbox[1]),18                          bbox[2] - bbox[0],19                          bbox[3] - bbox[1], fill=False,20                          edgecolor='red', linewidth=2.5)21        )22        if show_text:23            ax.text(bbox[0], bbox[1] - 5,24                    '{:s} {:.3f}'.format(class_name, score),25                    bbox=dict(facecolor='blue', alpha=0.5),26                    fontsize=10, color='white')27    ax.set_title(('{} detections with '28                  'p({} | box) >= {:.1f}').format(class_name, class_name,29                                                  thresh),30                 fontsize=10)31    plt.axis('off')32    plt.tight_layout()33    plt.savefig('out.png')34    plt.show()35def bbox_vote(det):36    order = det[:, 4].ravel().argsort()[::-1]37    det = det[order, :]38    dets = None39    while det.shape[0] > 0:40        # IOU41        area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)42        xx1 = np.maximum(det[0, 0], det[:, 0])43        yy1 = np.maximum(det[0, 1], det[:, 1])44        xx2 = np.minimum(det[0, 2], det[:, 2])45        yy2 = np.minimum(det[0, 3], det[:, 3])46        w = np.maximum(0.0, xx2 - xx1 + 1)47        h = np.maximum(0.0, yy2 - yy1 + 1)48        inter = w * h49        o = inter / (area[0] + area[:] - inter)50        # get needed merge det and delete these det51        merge_index = np.where(o >= 0.3)[0]52        det_accu = det[merge_index, :]53        det = np.delete(det, merge_index, 0)54        if merge_index.shape[0] <= 1:55            continue56        det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))57        max_score = np.max(det_accu[:, 4])58        det_accu_sum = np.zeros((1, 5))59        det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:])60        det_accu_sum[:, 4] = max_score61        try:62            dets = np.row_stack((dets, det_accu_sum))63        except:64            dets = det_accu_sum65    if dets is not None:66        dets = dets[0:750, :]67    return dets68def add_borders(curr_img, target_shape=(224, 224), fill_type=0):69    curr_h, curr_w = curr_img.shape[0:2]70    shift_h = max(target_shape[0] - curr_h, 0)71    shift_w = max(target_shape[1] - curr_w, 0)72    image = cv2.copyMakeBorder(curr_img, shift_h // 2, (shift_h + 1) // 2, shift_w // 2, (shift_w + 1) // 2, fill_type)73    return image, shift_h, shift_w74def resize_image(image, target_size, resize_factor=None, is_pad=True, interpolation=3):75    curr_image_size = image.shape[0:2]76    if resize_factor is None and is_pad:77        resize_factor = min(target_size[0] / curr_image_size[0], target_size[1] / curr_image_size[1])78    elif resize_factor is None and not is_pad:79        resize_factor = np.sqrt((target_size[0] * target_size[1]) / (curr_image_size[0] * curr_image_size[1]))80    image = cv2.resize(image, None, None, fx=resize_factor, fy=resize_factor, interpolation=interpolation)81    if is_pad:82        image, shift_h, shift_w = add_borders(image, target_size)83    else:84        shift_h = shift_w = 085    scale = np.array([image.shape[1]/resize_factor, image.shape[0]/resize_factor,86                      image.shape[1]/resize_factor, image.shape[0]/resize_factor])...

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