我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用cv2.transpose()。
def _centre_crop_and_transform(self, input_img, scale=1.0, trans=False, vflip=False, hflip=False): h, w = input_img.shape[:2] cx = w // 2 cy = h // 2 crop_w, crop_h = utils.calc_crop_size(self.img_size[0], self.img_size[1], scale=scale) input_img = utils.crop_center(input_img, cx, cy, crop_w, crop_h) if trans: input_img = cv2.transpose(input_img) if hflip or vflip: if hflip and vflip: c = -1 else: c = 0 if vflip else 1 input_img = cv2.flip(input_img, flipCode=c) if scale != 1.0: input_img = cv2.resize(input_img, self.img_size, interpolation=cv2.INTER_LINEAR) return input_img
def _grabImage(self): w = self.display.widget rval, img = self.vc.read() if rval: # COLOR if self.pGrayscale.value(): img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) else: img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #img = cv2.transpose(img) if self.pFloat.value(): img = toFloatArray(img) i = w.image b = self.pBuffer.value() if b: # BUFFER LAST N IMAGES if i is None or len(i) < b: self.display.addLayer(data=img) else: # TODO: implement as ring buffer using np.roll() img = np.insert(i, 0, img, axis=0) img = img[:self.pBuffer.value()] w.setImage(img, autoRange=False, autoLevels=False) else: w.setImage(img, autoRange=False, autoLevels=False)
def get_test_aug(factor): if not factor or factor == 1: return [ [False, False, False]] elif factor == 4: # transpose, v-flip, h-flip return [ [False, False, False], [False, False, True], [False, True, False], [True, True, True]] elif factor == 8: # return list of all combinations of flips and transpose return ((1 & np.arange(0, 8)[:, np.newaxis] // 2**np.arange(2, -1, -1)) > 0).tolist() else: print('Invalid augmentation factor') return [ [False, False, False]]
def crop_image(image, contours, min_aspect_ratio=0.5): ratio = image.shape[0] / float(scale_factor) warped = four_point_transform(image, contours.reshape(4, 2) * ratio) # test to see if the box ratio is correct height, width, channels = warped.shape if height > width: aspect_ratio = width / height else: aspect_ratio = height / width if aspect_ratio < min_aspect_ratio: raise ImageNotReadable() # test to see if the orientation is correct, if not flip it if height > width: warped = cv2.transpose(warped) warped = cv2.flip(warped, 0) return warped
def augment2(x): x = x.astype(np.float32) / 255 u = 0.75 if random.random() < u: if random.random() > 0.5: x = randomDistort1(x, distort_limit=0.35, shift_limit=0.25, u=1) else: x = randomDistort2(x, num_steps=10, distort_limit=0.2, u=1) x = randomShiftScaleRotate(x, shift_limit=0.0625, scale_limit=0.10, rotate_limit=45, u=1) x = randomFlip(x, u=0.5) x = randomTranspose(x, u=0.5) x = randomContrast(x, limit=0.2, u=0.5) # x = randomSaturation(x, limit=0.2, u=0.5), x = randomFilter(x, limit=0.5, u=0.2) x = 255.0 * x x[:, :, 0] -= 124 x[:, :, 1] -= 117 x[:, :, 2] -= 104 x *= 0.0167 x = np.transpose(x, (2, 0, 1)) return x
def augment(x): x = x.astype(np.float32) / 255 u = 0.75 if random.random() < u: if random.random() > 0.5: x = randomDistort1(x, distort_limit=0.35, shift_limit=0.25, u=1) else: x = randomDistort2(x, num_steps=10, distort_limit=0.2, u=1) x = randomShiftScaleRotate(x, shift_limit=0.0625, scale_limit=0.10, rotate_limit=45, u=1) x = randomFlip(x, u=0.5) x = randomTranspose(x, u=0.5) x = randomContrast(x, limit=0.2, u=0.5) # x = randomSaturation(x, limit=0.2, u=0.5), x = randomFilter(x, limit=0.5, u=0.2) x = np.uint8(255.0 * np.transpose(x, (2, 0, 1))) return x # draw -----------------------------------
def tensor_to_img(img, mean=0, std=1, dtype=np.uint8): img = img.numpy() img = np.transpose(img, (1, 2, 0)) img = img * std + mean img = img.astype(dtype) # img = cv2.cvtColor(img , cv2.COLOR_BGR2RGB) return img ## transform (input is numpy array, read in by cv2)
def img_to_tensor(img, mean=0, std=1.): img = img.astype(np.float32) img = (img - mean) / std img = img.transpose((2, 0, 1)) tensor = torch.from_numpy(img) ##.float() return tensor ## for debug
def randomTranspose(img, u=0.5): if random.random() < u: img = img.transpose(1, 0, 2) # cv2.transpose(img) return img # http://stackoverflow.com/questions/16265673/rotate-image-by-90-180-or-270-degrees
def cropped_image(image, contours, min_aspect_ratio=0.5, show=False): ratio = image.shape[0] / float(scale_factor) warped = four_point_transform(image, contours.reshape(4, 2) * ratio) # test to see if the box ratio is correct height, width, channels = warped.shape if height > width: aspect_ratio = width / height else: aspect_ratio = height / width if aspect_ratio < min_aspect_ratio: raise ImageNotReadable() # test to see if the orientation is correct, if not flip it original_height, original_width, original_channels = image.shape if not (original_height > original_width) is (height > width): warped = cv2.transpose(warped) warped = cv2.flip(warped, 0) if show: #this is for debugging puposes cv2.imshow("Payload", warped) cv2.waitKey(0) cv2.destroyAllWindows() return warped # def test(canny, opt1, opt2, opt3, polydb, image_file): # image = cv2.imread(image_file) # if canny: # # done_image = sharpen_image(image) # done_image = filter_image(image, canny1=opt1, canny2=opt2, show=True) # else: # contrasted_image = contrast_image(image, thresh1=opt1, thresh2=opt2, show=True) # done_image = merge_image_contours(contrasted_image, distance=opt3, show=True) # contours = get_contours(done_image, polydb=polydb) # overlay(image, contours, show=True) # cropped_image(image, contours, min_aspect_ratio=0.5, show=True) # test(True, 5, 5, 0, 0.03, 'images\\1.jpg') # test(True, 5, 5, 0, 0.03, 'images\\2.jpg') # test(True, 8, 8, 0, 0.1, 'images\\5.jpg') # test(True, 5, 5, 0, 0.03, 'images\\6.jpg') # test(True, 5, 5, 0, 0.03, 'images\\7.jpg') # test(False, 180, 200, 50, 0.03, 'images\\8.jpg') # test(True, 5, 5, 0, 0.03, 'images\\9.jpg') # test(True, 5, 5, 0, 0.03, 'images\\D.jpg')
