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Making the DXDetector operate internally on UMat rather than Mat type… #23

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Making the DXDetector operate internally on UMat rather than Mat type… #23

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8 changes: 4 additions & 4 deletions Computer Vision Toolkit/Computer Vision Toolkit/lib/Algorithms/DXDetector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ def DebrisDetect(img_path, Params):
avg_dim = (((width+height)//2)//3)*2

# Gaussian Blur
proc_img = cv2.GaussianBlur(img, (5,5), Params["LineGaussianIter"])
proc_img = cv2.GaussianBlur(cv2.UMat(img), (5,5), Params["LineGaussianIter"])

# Kernel Dilation
proc_img = cv2.dilate(proc_img, (5,5), iterations=Params["LineDilationIter"])
Expand All @@ -97,7 +97,7 @@ def DebrisDetect(img_path, Params):
dst = cv2.Canny(proc_img, Params["LineCannyEdgeLowerBound"], Params["LineCannyEdgeThreshold"])
cdst = cv2.cvtColor(dst, cv2.COLOR_GRAY2BGR)

lines = cv2.HoughLines(dst, 0.7, float(np.pi / 180.0), int(avg_dim/2))
lines = cv2.HoughLines(dst, 0.7, float(np.pi / 180.0), int(avg_dim/2)).get()

# If lines are found
if lines is not None:
Expand All @@ -124,13 +124,13 @@ def DebrisDetect(img_path, Params):
else:

# Gaussian Blur
proc2_img = cv2.GaussianBlur(img, (5,5), Params["CornerGaussianIter"])
proc2_img = cv2.GaussianBlur(cv2.UMat(img), (5,5), Params["CornerGaussianIter"])

# Erosion
proc2_img = cv2.erode(proc2_img, (5,5), iterations=Params["CornerErosionIter"])

# Stronger Bilateral Blur
proc2_img = cv2.bilateralFilter(proc2_img, 16, Params["CornerBilateralColor"], Params["CornerBilateralSpace"])
proc2_img = cv2.bilateralFilter(proc2_img, 16, Params["CornerBilateralColor"], Params["CornerBilateralSpace"]).get()

# Shi-Tomasi
gray = cv2.cvtColor(proc2_img, cv2.COLOR_BGR2GRAY)
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