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@ -12,6 +12,7 @@ |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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from unittest import result |
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import cv2 |
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import numpy as np |
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@ -194,7 +195,7 @@ def resize_rle(rle, im_h, im_w, im_scale_x, im_scale_y, interp): |
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def matching(im1, im2): |
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""" Match two images, used change detection. |
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""" Match two images, used change detection. (Just RGB) |
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Args: |
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im1 (np.ndarray): The image of time 1 |
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@ -217,4 +218,51 @@ def matching(im1, im2): |
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den_automatic_points = np.float32([kp2[m[0].trainIdx].pt for m in good_matches]).reshape(-1, 1, 2) |
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H, _ = cv2.findHomography(src_automatic_points, den_automatic_points, cv2.RANSAC, 5.0) |
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im1_t = cv2.warpPerspective(im1, H, (im2.shape[1], im2.shape[0])) |
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return im1_t, im2 |
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return im1_t, im2 |
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def de_haze(im, gamma=False): |
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""" Priori defogging of dark channel. (Just RGB) |
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Args: |
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im (np.ndarray): Image. |
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gamma (bool, optional): Use gamma correction or not. Defaults to False. |
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""" |
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def guided_filter(I, p, r, eps): |
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m_I = cv2.boxFilter(I, -1, (r, r)) |
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m_p = cv2.boxFilter(p, -1, (r, r)) |
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m_Ip = cv2.boxFilter(I * p, -1, (r, r)) |
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cov_Ip = m_Ip - m_I * m_p |
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m_II = cv2.boxFilter(I * I, -1, (r, r)) |
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var_I = m_II - m_I * m_I |
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a = cov_Ip / (var_I + eps) |
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b = m_p - a * m_I |
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m_a = cv2.boxFilter(a, -1, (r, r)) |
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m_b = cv2.boxFilter(b, -1, (r, r)) |
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return m_a * I + m_b |
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def de_fog(im, r, w, maxatmo_mask, eps): |
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# im is RGB and range[0, 1] |
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atmo_mask = np.min(im, 2) |
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dark_channel = cv2.erode(atmo_mask, np.ones((15, 15))) |
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atmo_mask = guided_filter(atmo_mask, dark_channel, r, eps) |
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bins = 2000 |
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ht = np.histogram(atmo_mask, bins) |
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d = np.cumsum(ht[0]) / float(atmo_mask.size) |
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for lmax in range(bins - 1, 0, -1): |
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if d[lmax] <= 0.999: |
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break |
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atmo_illum = np.mean(im, 2)[atmo_mask >= ht[1][lmax]].max() |
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atmo_mask = np.minimum(atmo_mask * w, maxatmo_mask) |
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return atmo_mask, atmo_illum |
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if np.max(im) > 1: |
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im = im / 255. |
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result = np.zeros(im.shape) |
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mask_img, atmo_illum = de_fog(im, r=81, w=0.95, maxatmo_mask=0.80, eps=1e-8) |
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for k in range(3): |
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result[:, :, k] = (im[:, :, k] - mask_img) / (1 - mask_img / atmo_illum) |
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result = np.clip(result, 0, 1) |
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if gamma: |
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result = result ** (np.log(0.5) / np.log(result.mean())) |
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return (result * 255).astype("uint8") |