CVImage class in imagepipeline module.
TODO: remove all image manipulation code from GameModel class. Moving all the OpenCV image manipulation actions to the CVImage class would make the GameModel procedures more legible. TODO: abstract multi-step processes in GameModel class as an ImagePipeline. The purpose is to improve testability by making each manipulation action in CVImage to result in a series of images and data structures.
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				|  | @ -0,0 +1,57 @@ | |||
| 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 | ||||
| 
 | ||||
|      | ||||
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