Delete some unnecessary code
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@@ -37,17 +37,6 @@ def refine_foreground(image, mask, r=90):
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new_foreground = Image.fromarray((estimated_foreground * 255.0).astype(np.uint8))
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return new_foreground
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def refine_foreground2(image, mask):
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if mask.size != image.size:
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mask = mask.resize(image.size)
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image = np.array(image) / 255.0
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mask = np.array(mask) / 255.0
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estimated_foreground = FB_blur_fusion_foreground_estimator_2_Adaptive(image, mask)
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new_foreground = Image.fromarray((estimated_foreground * 255.0).astype(np.uint8))
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return new_foreground
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def FB_blur_fusion_foreground_estimator_2(image, alpha, r=90):
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# Thanks to the source: https://github.com/Photoroom/fast-foreground-estimation
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alpha = alpha[:, :, None]
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@@ -70,32 +59,6 @@ def FB_blur_fusion_foreground_estimator(image, F, B, alpha, r=90):
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F = np.clip(F, 0, 1)
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return F, blurred_B
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def FB_blur_fusion_foreground_estimator_2_Adaptive(image, alpha, r=6):
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if isinstance(image, Image.Image):
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image = np.array(image) / 255.0
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(H,W) = image.shape[:2]
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r1 = int(W*0.1)
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alpha = alpha[:, :, None]
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F, blur_B = FB_blur_fusion_foreground_estimator(
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image, image, image, alpha, r1)
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r2 = int(W*0.007)
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F,blur_B = FB_blur_fusion_foreground_estimator_Adaptive(image, F, blur_B, alpha, r2)
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return F
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def FB_blur_fusion_foreground_estimator_Adaptive(image, F, B, alpha, r=90):
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if isinstance(image, Image.Image):
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image = np.array(image) / 255.0
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blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None]
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blurred_FA = cv2.blur(F * alpha, (r, r))
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blurred_F = blurred_FA / (blurred_alpha + 1e-5)
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blurred_B1A = cv2.blur(B * (1 - alpha), (r, r))
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blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)
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F = blurred_F + alpha * \
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(image - alpha * blurred_F - (1 - alpha) * blurred_B)
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F = np.clip(F, 0, 1)
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return F, blurred_B
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def get_device_by_name(device):
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"""
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根据名称获取设备
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