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import cv2 |
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import numpy as np |
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import matplotlib.pyplot as plt |
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def vis_detections(im, dets, thresh=0.5, show_text=True): |
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"""Draw detected bounding boxes.""" |
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class_name = 'face' |
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inds = np.where(dets[:, -1] >= thresh)[0] if dets is not None else [] |
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if len(inds) == 0: |
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return |
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im = im[:, :, (2, 1, 0)] |
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fig, ax = plt.subplots(figsize=(12, 12)) |
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ax.imshow(im, aspect='equal') |
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for i in inds: |
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bbox = dets[i, :4] |
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score = dets[i, -1] |
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ax.add_patch( |
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plt.Rectangle((bbox[0], bbox[1]), |
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bbox[2] - bbox[0], |
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bbox[3] - bbox[1], fill=False, |
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edgecolor='red', linewidth=2.5) |
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) |
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if show_text: |
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ax.text(bbox[0], bbox[1] - 5, |
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'{:s} {:.3f}'.format(class_name, score), |
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bbox=dict(facecolor='blue', alpha=0.5), |
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fontsize=10, color='white') |
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ax.set_title(('{} detections with ' |
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'p({} | box) >= {:.1f}').format(class_name, class_name, |
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thresh), |
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fontsize=10) |
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plt.axis('off') |
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plt.tight_layout() |
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plt.savefig('out.png') |
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plt.show() |
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def bbox_vote(det): |
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order = det[:, 4].ravel().argsort()[::-1] |
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det = det[order, :] |
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dets = None |
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while det.shape[0] > 0: |
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area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1) |
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xx1 = np.maximum(det[0, 0], det[:, 0]) |
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yy1 = np.maximum(det[0, 1], det[:, 1]) |
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xx2 = np.minimum(det[0, 2], det[:, 2]) |
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yy2 = np.minimum(det[0, 3], det[:, 3]) |
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w = np.maximum(0.0, xx2 - xx1 + 1) |
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h = np.maximum(0.0, yy2 - yy1 + 1) |
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inter = w * h |
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o = inter / (area[0] + area[:] - inter) |
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merge_index = np.where(o >= 0.3)[0] |
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det_accu = det[merge_index, :] |
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det = np.delete(det, merge_index, 0) |
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if merge_index.shape[0] <= 1: |
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continue |
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det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4)) |
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max_score = np.max(det_accu[:, 4]) |
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det_accu_sum = np.zeros((1, 5)) |
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det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:]) |
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det_accu_sum[:, 4] = max_score |
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try: |
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dets = np.row_stack((dets, det_accu_sum)) |
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except: |
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dets = det_accu_sum |
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if dets is not None: |
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dets = dets[0:750, :] |
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return dets |
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def add_borders(curr_img, target_shape=(224, 224), fill_type=0): |
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curr_h, curr_w = curr_img.shape[0:2] |
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shift_h = max(target_shape[0] - curr_h, 0) |
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shift_w = max(target_shape[1] - curr_w, 0) |
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image = cv2.copyMakeBorder(curr_img, shift_h // 2, (shift_h + 1) // 2, shift_w // 2, (shift_w + 1) // 2, fill_type) |
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return image, shift_h, shift_w |
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def resize_image(image, target_size, resize_factor=None, is_pad=True, interpolation=3): |
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curr_image_size = image.shape[0:2] |
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if resize_factor is None and is_pad: |
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resize_factor = min(target_size[0] / curr_image_size[0], target_size[1] / curr_image_size[1]) |
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elif resize_factor is None and not is_pad: |
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resize_factor = np.sqrt((target_size[0] * target_size[1]) / (curr_image_size[0] * curr_image_size[1])) |
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image = cv2.resize(image, None, None, fx=resize_factor, fy=resize_factor, interpolation=interpolation) |
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if is_pad: |
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image, shift_h, shift_w = add_borders(image, target_size) |
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else: |
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shift_h = shift_w = 0 |
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scale = np.array([image.shape[1]/resize_factor, image.shape[0]/resize_factor, |
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image.shape[1]/resize_factor, image.shape[0]/resize_factor]) |
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return image, shift_h/image.shape[0]/2, shift_w/image.shape[1]/2, scale |
