Python cv2.COLOR_GRAY2BGRA Examples
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code examples of cv2.COLOR_GRAY2BGRA().
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Example #1
Source File: engine_cv3.py From opencv-engine with MIT License | 5 votes |
def enable_alpha(self): if self.image_channels < 4: with_alpha = np.zeros((self.size[1], self.size[0], 4), self.image.dtype) if self.image_channels == 3: cv2.cvtColor(self.image, cv2.COLOR_BGR2BGRA, with_alpha) else: cv2.cvtColor(self.image, cv2.COLOR_GRAY2BGRA, with_alpha) self.image = with_alpha
Example #2
Source File: utils.py From ad-versarial with MIT License | 5 votes |
def to_alpha(logo): if has_alpha(logo): return logo if is_gray(logo): return cv2.cvtColor(logo, cv2.COLOR_GRAY2BGRA) else: return cv2.cvtColor(logo, cv2.COLOR_BGR2BGRA)
Example #3
Source File: corruptions.py From robustness with Apache License 2.0 | 4 votes |
def spatter(x, severity=1): c = [(0.65, 0.3, 4, 0.69, 0.6, 0), (0.65, 0.3, 3, 0.68, 0.6, 0), (0.65, 0.3, 2, 0.68, 0.5, 0), (0.65, 0.3, 1, 0.65, 1.5, 1), (0.67, 0.4, 1, 0.65, 1.5, 1)][severity - 1] x = np.array(x, dtype=np.float32) / 255. liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1]) liquid_layer = gaussian(liquid_layer, sigma=c[2]) liquid_layer[liquid_layer < c[3]] = 0 if c[5] == 0: liquid_layer = (liquid_layer * 255).astype(np.uint8) dist = 255 - cv2.Canny(liquid_layer, 50, 150) dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5) _, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC) dist = cv2.blur(dist, (3, 3)).astype(np.uint8) dist = cv2.equalizeHist(dist) ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]]) dist = cv2.filter2D(dist, cv2.CV_8U, ker) dist = cv2.blur(dist, (3, 3)).astype(np.float32) m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA) m /= np.max(m, axis=(0, 1)) m *= c[4] # water is pale turqouise color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1])), axis=2) color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA) x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA) return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255 else: m = np.where(liquid_layer > c[3], 1, 0) m = gaussian(m.astype(np.float32), sigma=c[4]) m[m < 0.8] = 0 # mud brown color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]), 42 / 255. * np.ones_like(x[..., :1]), 20 / 255. * np.ones_like(x[..., :1])), axis=2) color *= m[..., np.newaxis] x *= (1 - m[..., np.newaxis]) return np.clip(x + color, 0, 1) * 255
Example #4
Source File: make_imagenet_c.py From robustness with Apache License 2.0 | 4 votes |
def spatter(x, severity=1): c = [(0.65, 0.3, 4, 0.69, 0.6, 0), (0.65, 0.3, 3, 0.68, 0.6, 0), (0.65, 0.3, 2, 0.68, 0.5, 0), (0.65, 0.3, 1, 0.65, 1.5, 1), (0.67, 0.4, 1, 0.65, 1.5, 1)][severity - 1] x = np.array(x, dtype=np.float32) / 255. liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1]) liquid_layer = gaussian(liquid_layer, sigma=c[2]) liquid_layer[liquid_layer < c[3]] = 0 if c[5] == 0: liquid_layer = (liquid_layer * 255).astype(np.uint8) dist = 255 - cv2.Canny(liquid_layer, 50, 150) dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5) _, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC) dist = cv2.blur(dist, (3, 3)).astype(np.uint8) dist = cv2.equalizeHist(dist) # ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32) # ker -= np.mean(ker) ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]]) dist = cv2.filter2D(dist, cv2.CV_8U, ker) dist = cv2.blur(dist, (3, 3)).astype(np.float32) m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA) m /= np.max(m, axis=(0, 1)) m *= c[4] # water is pale turqouise color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1])), axis=2) color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA) x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA) return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255 else: m = np.where(liquid_layer > c[3], 1, 0) m = gaussian(m.astype(np.float32), sigma=c[4]) m[m < 0.8] = 0 # m = np.abs(m) ** (1/c[4]) # mud brown color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]), 42 / 255. * np.ones_like(x[..., :1]), 20 / 255. * np.ones_like(x[..., :1])), axis=2) color *= m[..., np.newaxis] x *= (1 - m[..., np.newaxis]) return np.clip(x + color, 0, 1) * 255
Example #5
Source File: make_cifar_c.py From robustness with Apache License 2.0 | 4 votes |
def spatter(x, severity=1): c = [(0.62,0.1,0.7,0.7,0.5,0), (0.65,0.1,0.8,0.7,0.5,0), (0.65,0.3,1,0.69,0.5,0), (0.65,0.1,0.7,0.69,0.6,1), (0.65,0.1,0.5,0.68,0.6,1)][severity - 1] x = np.array(x, dtype=np.float32) / 255. liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1]) liquid_layer = gaussian(liquid_layer, sigma=c[2]) liquid_layer[liquid_layer < c[3]] = 0 if c[5] == 0: liquid_layer = (liquid_layer * 255).astype(np.uint8) dist = 255 - cv2.Canny(liquid_layer, 50, 150) dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5) _, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC) dist = cv2.blur(dist, (3, 3)).astype(np.uint8) dist = cv2.equalizeHist(dist) # ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32) # ker -= np.mean(ker) ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]]) dist = cv2.filter2D(dist, cv2.CV_8U, ker) dist = cv2.blur(dist, (3, 3)).astype(np.float32) m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA) m /= np.max(m, axis=(0, 1)) m *= c[4] # water is pale turqouise color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1])), axis=2) color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA) x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA) return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255 else: m = np.where(liquid_layer > c[3], 1, 0) m = gaussian(m.astype(np.float32), sigma=c[4]) m[m < 0.8] = 0 # m = np.abs(m) ** (1/c[4]) # mud brown color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]), 42 / 255. * np.ones_like(x[..., :1]), 20 / 255. * np.ones_like(x[..., :1])), axis=2) color *= m[..., np.newaxis] x *= (1 - m[..., np.newaxis]) return np.clip(x + color, 0, 1) * 255
Example #6
Source File: make_tinyimagenet_c.py From robustness with Apache License 2.0 | 4 votes |
def spatter(x, severity=1): c = [(0.62,0.1,0.7,0.7,0.6,0), (0.65,0.1,0.8,0.7,0.6,0), (0.65,0.3,1,0.69,0.6,0), (0.65,0.1,0.7,0.68,0.6,1), (0.65,0.1,0.5,0.67,0.6,1)][severity - 1] x = np.array(x, dtype=np.float32) / 255. liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1]) liquid_layer = gaussian(liquid_layer, sigma=c[2]) liquid_layer[liquid_layer < c[3]] = 0 if c[5] == 0: liquid_layer = (liquid_layer * 255).astype(np.uint8) dist = 255 - cv2.Canny(liquid_layer, 50, 150) dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5) _, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC) dist = cv2.blur(dist, (3, 3)).astype(np.uint8) dist = cv2.equalizeHist(dist) # ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32) # ker -= np.mean(ker) ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]]) dist = cv2.filter2D(dist, cv2.CV_8U, ker) dist = cv2.blur(dist, (3, 3)).astype(np.float32) m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA) m /= np.max(m, axis=(0, 1)) m *= c[4] # water is pale turqouise color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1])), axis=2) color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA) x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA) return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255 else: m = np.where(liquid_layer > c[3], 1, 0) m = gaussian(m.astype(np.float32), sigma=c[4]) m[m < 0.8] = 0 # m = np.abs(m) ** (1/c[4]) # mud brown color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]), 42 / 255. * np.ones_like(x[..., :1]), 20 / 255. * np.ones_like(x[..., :1])), axis=2) color *= m[..., np.newaxis] x *= (1 - m[..., np.newaxis]) return np.clip(x + color, 0, 1) * 255
Example #7
Source File: make_imagenet_c_inception.py From robustness with Apache License 2.0 | 4 votes |
def spatter(x, severity=1): c = [(0.65,0.3,4,0.69,0.9,0), (0.65,0.3,3.5,0.68,0.9,0), (0.65,0.3,3,0.68,0.8,0), (0.65,0.3,1.2,0.65,1.8,1), (0.67,0.4,1.2,0.65,1.8,1)][severity - 1] x = np.array(x, dtype=np.float32) / 255. liquid_layer = np.random.normal(size=x.shape[:2], loc=c[0], scale=c[1]) liquid_layer = gaussian(liquid_layer, sigma=c[2]) liquid_layer[liquid_layer < c[3]] = 0 if c[5] == 0: liquid_layer = (liquid_layer * 255).astype(np.uint8) dist = 255 - cv2.Canny(liquid_layer, 50, 150) dist = cv2.distanceTransform(dist, cv2.DIST_L2, 5) _, dist = cv2.threshold(dist, 20, 20, cv2.THRESH_TRUNC) dist = cv2.blur(dist, (3, 3)).astype(np.uint8) dist = cv2.equalizeHist(dist) # ker = np.array([[-1,-2,-3],[-2,0,0],[-3,0,1]], dtype=np.float32) # ker -= np.mean(ker) ker = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]]) dist = cv2.filter2D(dist, cv2.CV_8U, ker) dist = cv2.blur(dist, (3, 3)).astype(np.float32) m = cv2.cvtColor(liquid_layer * dist, cv2.COLOR_GRAY2BGRA) m /= np.max(m, axis=(0, 1)) m *= c[4] # water is pale turqouise color = np.concatenate((175 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1]), 238 / 255. * np.ones_like(m[..., :1])), axis=2) color = cv2.cvtColor(color, cv2.COLOR_BGR2BGRA) x = cv2.cvtColor(x, cv2.COLOR_BGR2BGRA) return cv2.cvtColor(np.clip(x + m * color, 0, 1), cv2.COLOR_BGRA2BGR) * 255 else: m = np.where(liquid_layer > c[3], 1, 0) m = gaussian(m.astype(np.float32), sigma=c[4]) m[m < 0.8] = 0 # m = np.abs(m) ** (1/c[4]) # mud brown color = np.concatenate((63 / 255. * np.ones_like(x[..., :1]), 42 / 255. * np.ones_like(x[..., :1]), 20 / 255. * np.ones_like(x[..., :1])), axis=2) color *= m[..., np.newaxis] x *= (1 - m[..., np.newaxis]) return np.clip(x + color, 0, 1) * 255
Example #8
Source File: step2_train_mass_segmenter.py From kaggle_ndsb2017 with MIT License | 4 votes |
def predict_patients(patients_dir, model_path, holdout, patient_predictions, model_type): model = get_unet(0.001) model.load_weights(model_path) for item_name in os.listdir(patients_dir): if not os.path.isdir(patients_dir + item_name): continue patient_id = item_name if holdout >= 0: patient_fold = helpers.get_patient_fold(patient_id, submission_set_neg=True) if patient_fold < 0: if holdout != 0: continue else: patient_fold %= 3 if patient_fold != holdout: continue # if "100953483028192176989979435275" not in patient_id: # continue print(patient_id) patient_dir = patients_dir + patient_id + "/" mass = 0 img_type = "_i" if model_type == "masses" else "_c" slices = glob.glob(patient_dir + "*" + img_type + ".png") if model_type == "emphysema": slices = slices[int(len(slices) / 2):] for img_path in slices: src_img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE) src_img = cv2.resize(src_img, dsize=(settings.SEGMENTER_IMG_SIZE, settings.SEGMENTER_IMG_SIZE)) src_img = prepare_image_for_net(src_img) p = model.predict(src_img, batch_size=1) p[p < 0.5] = 0 mass += p.sum() p = p[0, :, :, 0] * 255 # cv2.imwrite(img_path.replace("_i.png", "_mass.png"), p) src_img = src_img.reshape((settings.SEGMENTER_IMG_SIZE, settings.SEGMENTER_IMG_SIZE)) src_img *= 255 # src_img = cv2.cvtColor(src_img.astype(numpy.uint8), cv2.COLOR_GRAY2BGR) # p = cv2.cvtColor(p.astype(numpy.uint8), cv2.COLOR_GRAY2BGRA) src_img = cv2.addWeighted(p.astype(numpy.uint8), 0.2, src_img.astype(numpy.uint8), 1 - 0.2, 0) cv2.imwrite(img_path.replace(img_type + ".png", "_" + model_type + "o.png"), src_img) if mass > 1: print(model_type + ": ", mass) patient_predictions.append((patient_id, mass)) df = pandas.DataFrame(patient_predictions, columns=["patient_id", "prediction"]) df.to_csv(settings.BASE_DIR + model_type + "_predictions.csv", index=False)