58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
import cv2
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import numpy as np
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class CVImage:
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def __init__(self, label="", img=None, iscolor=False):
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self.label = label
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self.image = img
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self.iscolor = iscolor
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def load(self, filename, color=False, label=None):
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self.image = cv2.imread(filename, int(color))
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if label: self.label = label
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def copy(self):
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return np.copy(self.image)
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## def snip(self, point, width_height):
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## return self.image[self.point[0]:self.point[0]+self.width_height[0],
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## self.point[1]:self.point[1]+self.width_height[1]]
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def snip(self, rect):
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assert all((len(rect)==2, len(rect[0])==2, len(rect[1])==2)) #((x,y),(w,h))
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return self.image[self.rect[0][0]:self.rect[0][1],
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self.rect[1][0]:self.rect[1][1]]
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def mask(self, rect, mask_color=None, nonmask_color=None):
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assert all((len(rect)==2, len(rect[0])==2, len(rect[1])==2)) #((x,y),(w,h))
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if mask_color is None:
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mask_color = (0, 0, 0) if self.iscolor else 0
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if nonmask_color is None:
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nonnmask_color = (255, 255, 255) if self.iscolor else 255
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mask = np.full(self.image.shape, nonmask_color, dtype=np.uint8)
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cv2.rectangle(mask, *rect, color=mask_color, thickness=cv2.FILLED)
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return cv2.bitwise_and(self.image, mask)
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def sift_clusters(self, cluster_radius):
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sift = cv2.SIFT_create()
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keypoints, descriptions = sift.detectAndCompute(self.image, None)
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return pointcluster.cluster_set([k.pt for k in keypoints], cluster_radius)
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def set_blob_params(self, minThreshold = 10, maxThreshold = 200,
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minArea = None, maxArea = None,
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minCircularity = None, maxCircularity = None,
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minConvexity = None, maxConvexity = None,
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minInertiaRatio = None, maxInertiaRatio = None):
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p = cv2.SimpleBlobDetector_Params()
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def blob_detect(self, params=None):
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pass
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class ImagePipeline:
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def __init__(self):
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pass
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