我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用cv2.meanStdDev()。
def main(): jpg_inputs = find_inputs(JPGPATH, types=('.jpg',), prefix=PREFIX) tif_inputs = find_inputs(TIFPATH, types=('.tif',), prefix=PREFIX) jpg_stats = [] for f in jpg_inputs: img = cv2.imread(f[1]) mean, std = cv2.meanStdDev(img) jpg_stats.append(np.array([mean[::-1] / 255, std[::-1] / 255])) jpg_vals = np.mean(jpg_stats, axis=0) print(jpg_vals) tif_stats = [] for f in tif_inputs: img = cv2.imread(f[1], -1) img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGBA) mean, std = cv2.meanStdDev(img) tif_stats.append(np.array([mean, std])) tif_vals = np.mean(tif_stats, axis=0) print(tif_vals)
def get_image_stats(img, left=0, top=0, width=0, height=0): crop_img = img[top:(top + height), left:(left + width)] (means, stds) = cv2.meanStdDev(crop_img) stats = np.concatenate([means, stds]).flatten() return stats
def preprocess(self, resized, landmarks): #ret = resized.astype('f4') #ret -= self.mean #ret /= (1.e-6+ self.std) #return ret, (landmarks/40.)-0.5 grayImg = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY).astype('f4') m, s = cv2.meanStdDev(grayImg) grayImg = (grayImg-m)/(1.e-6 + s) return grayImg, landmarks/60.
def varianceOfLaplacian(img): ''''LAPV' algorithm (Pech2000)''' lap = cv2.Laplacian(img, ddepth=-1)#cv2.cv.CV_64F) stdev = cv2.meanStdDev(lap)[1] s = stdev[0]**2 return s[0]
def normalizedGraylevelVariance(img): ''''GLVN' algorithm (Santos97)''' mean, stdev = cv2.meanStdDev(img) s = stdev[0]**2 / mean[0] return s[0]
def __call__(self, img): # This should still be a H x W x C Numpy/OpenCv compat image, not a Torch Tensor assert isinstance(img, np.ndarray) mean, std = cv2.meanStdDev(img) mean, std = mean.astype(np.float32), std.astype(np.float32) img = img.astype(np.float32) img = (img - np.squeeze(mean)) / (np.squeeze(std) + self.std_epsilon) return img
def fourier_transform(ch_bd): dft = cv2.dft(np.float32(ch_bd), flags=cv2.DFT_COMPLEX_OUTPUT) dft_shift = np.fft.fftshift(dft) # get the Power Spectrum magnitude_spectrum = 20. * np.log(cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1])) psd1D = azimuthal_avg(magnitude_spectrum) return list(cv2.meanStdDev(psd1D))
def feature_fourier(chBd, blk, scs, end_scale): rows, cols = chBd.shape scales_half = int(end_scale / 2) scales_blk = end_scale - blk out_len = 0 pix_ctr = 0 for i in range(0, rows-scales_blk, blk): for j in range(0, cols-scales_blk, blk): for k in scs: out_len += 2 # set the output list out_list = np.zeros(out_len).astype(np.float32) for i in range(0, rows-scales_blk, blk): for j in range(0, cols-scales_blk, blk): for k in scs: ch_bd = chBd[i+scales_half-(k/2):i+scales_half-(k/2)+k, j+scales_half-(k/2):j+scales_half-(k/2)+k] # get the Fourier Transform dft = cv2.dft(np.float32(ch_bd), flags=cv2.DFT_COMPLEX_OUTPUT) dft_shift = np.fft.fftshift(dft) # get the Power Spectrum magnitude_spectrum = 20. * np.log(cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1])) psd1D = azimuthal_avg(magnitude_spectrum) sts = list(cv2.meanStdDev(psd1D)) # plt.subplot(121) # plt.imshow(ch_bd, cmap='gray') # plt.subplot(122) # plt.imshow(magnitude_spectrum, interpolation='nearest') # plt.show() # print psd1D # sys.exit() for st in sts: if np.isnan(st[0][0]): out_list[pix_ctr] = 0. else: out_list[pix_ctr] = st[0][0] pix_ctr += 1 out_list[np.isnan(out_list) | np.isinf(out_list)] = 0. return out_list
