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