diff --git a/experimental.py b/experimental.py new file mode 100644 index 0000000..9fe902b --- /dev/null +++ b/experimental.py @@ -0,0 +1,449 @@ +class NumpyUtility: + def __init__(self): + '''Utilitys made with chatGPT for experimentation, few are working''' + + def compress_dynamic_range(image): + # Find the 1st and 99th percentiles of the image + p1, p99 = np.percentile(image, (1, 99)) + + # Calculate the range of the image + img_range = p99 - p1 + + # Calculate the compression factor required to fit the image into 8-bit range + c = 1 / img_range + + # Subtract the 1st percentile from the image and clip it to the [0, 1] range + new_image = np.clip((image - p1) * c, 0, 1) + + # Convert the image to uint8 format + new_image = new_image.astype(np.uint8) + + return new_image + + def adjust_luminance(image, mask, amount): + # Convert the image to LAB color space + rgb_image = image.astype(np.float32) / 255.0 + xyz_image = np.dot(rgb_image, np.array([[0.412453, 0.357580, 0.180423], + [0.212671, 0.715160, 0.072169], + [0.019334, 0.119193, 0.950227]])) + xyz_image = np.clip(xyz_image, 0, 1) + lab_image = np.zeros_like(xyz_image) + lab_image[..., 0] = 116.0 * np.power(xyz_image[..., 1], 1 / 3.0) - 16.0 + lab_image[..., 1] = 500.0 * (np.power(xyz_image[..., 0], 1 / 3.0) - np.power(xyz_image[..., 1], 1 / 3.0)) + lab_image[..., 2] = 200.0 * (np.power(xyz_image[..., 1], 1 / 3.0) - np.power(xyz_image[..., 2], 1 / 3.0)) + + # Apply the luminance adjustment to the masked area + lab_image[..., 0][mask == 1] = np.clip(lab_image[..., 0][mask == 1] * (1 + amount), 0, 100) + + # Convert the image back to RGB color space + xyz_image[..., 1] = np.power((lab_image[..., 0] + 16.0) / 116.0, 3) + xyz_image[..., 0] = np.power((lab_image[..., 0] + 16.0) / 116.0 + lab_image[..., 1] / 500.0, 3) + xyz_image[..., 2] = np.power((lab_image[..., 0] + 16.0) / 116.0 - lab_image[..., 2] / 200.0, 3) + rgb_image = np.dot(xyz_image, np.array([[3.240479, -1.537150, -0.498535], + [-0.969256, 1.875992, 0.041556], + [0.055648, -0.204043, 1.057311]])) + + # Convert the image back to the range [0, 255] + rgb_image = np.clip(rgb_image, 0, 1) * 255.0 + rgb_image = rgb_image.astype(np.uint8) + + return rgb_image + + def gaussian_filter(mask, sigma): + """ + Apply Gaussian filtering to a binary mask. + + Parameters: + mask (numpy.ndarray): Binary mask to apply filtering on. + sigma (float): Standard deviation of the Gaussian kernel. + + Returns: + numpy.ndarray: Binary mask after Gaussian filtering. + """ + # Create a Gaussian kernel with the given sigma + ksize = int(2 * np.ceil(2 * sigma) + 1) + kernel = np.zeros((ksize, ksize)) + for i in range(ksize): + for j in range(ksize): + kernel[i, j] = np.exp(-((i - ksize // 2) ** 2 + (j - ksize // 2) ** 2) / (2 * sigma ** 2)) + kernel /= np.sum(kernel) + + # Apply convolution with the kernel + filtered = np.zeros_like(mask, dtype='float32') + for i in range(mask.shape[0]): + for j in range(mask.shape[1]): + roi = mask[max(i - ksize // 2, 0):min(i + ksize // 2 + 1, mask.shape[0]), + max(j - ksize // 2, 0):min(j + ksize // 2 + 1, mask.shape[1])] + filtered[i, j] = np.sum(roi * kernel[:roi.shape[0], :roi.shape[1]]) + + return filtered + + def binary_opening(mask, kernel): + """ + Perform binary morphological opening on a binary mask using a structuring element. + + Parameters: + mask (numpy.ndarray): Binary mask to perform opening on. + kernel (numpy.ndarray): Structuring element used for opening. + + Returns: + numpy.ndarray: Binary mask after morphological opening. + """ + # Create padding on all sides of the mask + pad_width = [(kernel.shape[i] // 2, kernel.shape[i] // 2) for i in range(kernel.ndim)] + mask_padded = np.pad(mask, pad_width, mode='constant', constant_values=0) + + # Apply morphological erosion using the kernel + eroded = np.zeros_like(mask_padded) + for i in range(mask_padded.ndim): + eroded = np.maximum(eroded, np.apply_along_axis(np.roll, i, mask_padded, shift=-kernel.shape[i] // 2)) + eroded = eroded[pad_width[0][0]:-pad_width[0][1], pad_width[1][0]:-pad_width[1][1]] + + # Create padding on all sides of the eroded mask + pad_width = [(kernel.shape[i] // 2, kernel.shape[i] // 2) for i in range(kernel.ndim)] + eroded_padded = np.pad(eroded, pad_width, mode='constant', constant_values=0) + + # Apply morphological dilation using the kernel + dilated = np.zeros_like(eroded_padded) + for i in range(eroded_padded.ndim): + dilated = np.minimum(dilated, np.apply_along_axis(np.roll, i, eroded_padded, shift=kernel.shape[i] // 2)) + dilated = dilated[pad_width[0][0]:-pad_width[0][1], pad_width[1][0]:-pad_width[1][1]] + + return dilated + + def binary_closing(mask, kernel): + """ + Perform binary morphological closing on a binary mask using a structuring element. + + Parameters: + mask (numpy.ndarray): Binary mask to perform closing on. + kernel (numpy.ndarray): Structuring element used for closing. + + Returns: + numpy.ndarray: Binary mask after morphological closing. + """ + # Create padding on all sides of the mask + pad_width = [(kernel.shape[i] // 2, kernel.shape[i] // 2) for i in range(kernel.ndim)] + mask_padded = np.pad(mask, pad_width, mode='constant', constant_values=0) + + # Apply morphological dilation using the kernel + dilated = np.zeros_like(mask_padded) + for i in range(mask_padded.ndim): + dilated = np.minimum(dilated, np.apply_along_axis(np.roll, i, mask_padded, shift=kernel.shape[i] // 2)) + dilated = dilated[pad_width[0][0]:-pad_width[0][1], pad_width[1][0]:-pad_width[1][1]] + + # Create padding on all sides of the dilated mask + pad_width = [(kernel.shape[i] // 2, kernel.shape[i] // 2) for i in range(kernel.ndim)] + dilated_padded = np.pad(dilated, pad_width, mode='constant', constant_values=0) + + # Apply morphological erosion using the kernel + eroded = np.zeros_like(dilated_padded) + for i in range(dilated_padded.ndim): + eroded = np.maximum(eroded, np.apply_along_axis(np.roll, i, dilated_padded, shift=-kernel.shape[i] // 2)) + eroded = eroded[pad_width[0][0]:-pad_width[0][1], pad_width[1][0]:-pad_width[1][1]] + + return eroded + + def create_shadow_mask(image, threshold=80, range_width=50): + lab_image = np.apply_along_axis(lambda x: np.dot([0.2126, 0.7152, 0.0722], x), 2, image).astype('float64') + luminance_range = np.max(lab_image) - np.min(lab_image) + if luminance_range < range_width: + range_width = luminance_range + threshold_min = np.min(lab_image) + range_width / 2 + threshold_max = np.max(lab_image) - range_width / 2 + if threshold < threshold_min: + threshold = threshold_min + elif threshold > threshold_max: + threshold = threshold_max + mask = np.logical_and(lab_image >= threshold - range_width / 2, lab_image <= threshold + range_width / 2) + if np.sum(mask) == 0: + return np.zeros_like(mask).astype(float) + else: + center_line = np.nanmean(np.where(mask, lab_image, np.nan), axis=0) + x = np.arange(center_line.shape[0]) + slope = np.zeros(center_line.shape) + slope[1:-1] = (center_line[2:] - center_line[:-2]) / 2 + slope[0] = slope[1] + slope[-1] = slope[-2] + intercept = center_line - slope * x + x = np.arange(image.shape[1]) # x-coordinates of pixels + y = np.arange(image.shape[0]) # y-coordinates of pixels + print(y) + x, y = np.meshgrid(x, y) # create 2D arrays of x- and y-coordinates + + # compute distances from each pixel to the shadow line + dist = np.abs((y[:, :, np.newaxis] - slope[np.newaxis, np.newaxis, :] * x[:, :, np.newaxis] + - intercept[np.newaxis, np.newaxis, :]) / np.sqrt(1 + slope[np.newaxis, np.newaxis, :] ** 2)) + print(dist) + + # sigma = np.nanmedian(dist)/0.6745 + # mask = np.exp(-0.5*(dist/sigma)**2) + + return dist + + def highpass_mask(img, cutoff, order=1): + # Calculate the Fourier transform of the image + fft_img = np.fft.fft2(img) + + # Shift the zero-frequency component to the center of the spectrum + fft_img = np.fft.fftshift(fft_img) + + # Construct a highpass filter in the Fourier domain + x, y = np.meshgrid(np.linspace(-1, 1, img.shape[1]), np.linspace(-1, 1, img.shape[0])) + r = np.sqrt(x ** 2 + y ** 2) + hp_filter = 1 - 1 / (1 + (cutoff / r) ** (2 * order)) + + # Apply the highpass filter to the Fourier transform of the image + fft_img_hp = fft_img * hp_filter + + # Shift the zero-frequency component back to the corners of the spectrum + fft_img_hp = np.fft.ifftshift(fft_img_hp) + + # Calculate the inverse Fourier transform of the highpass-filtered image + img_hp = np.fft.ifft2(fft_img_hp) + + # Take the absolute value of the real part of the inverse Fourier transform + img_hp = np.abs(np.real(img_hp)) + + # Normalize the highpass-filtered image to the range [0, 1] + img_hp_norm = img_hp / np.max(img_hp) + + # Invert the highpass-filtered image to create a highpass mask + mask = 1 - img_hp_norm + + return mask + + def cvtColor(hsv_img): + # Assuming you have an HSV image loaded in the variable 'hsv_img' + # Let's convert the image back to RGB color space + h, s, v = np.split(hsv_img, 3, axis=-1) + h = h.reshape(h.shape[:2]) + s = s.reshape(s.shape[:2]) + v = v.reshape(v.shape[:2]) + c = v * s + x = c * (1 - np.abs((h / 60) % 2 - 1)) + m = v - c + z = np.zeros_like(h) + + # Set up the RGB channels according to the hue value + rgb_img = np.dstack(( + np.where((0 <= h) & (h < 60), c, + np.where((120 <= h) & (h < 180), z, np.where((240 <= h) & (h < 300), x, m))), + np.where((300 <= h), c, np.where((60 <= h) & (h < 120), x, np.where((180 <= h) & (h < 240), c, m))), + np.where((0 <= h) & (h < 360), v, m) + )) + return rgb_img + + def saturate(img, amount): + hsv_img = np.copy(img) + hsv_img = np.asarray(hsv_img, dtype=np.float32) / 255.0 # Normalize pixel values + hsv_img = np.clip(hsv_img, 0, 1) # Clip values to the range [0, 1] + hsv_img = np.squeeze(cv2.cvtColor(hsv_img, cv2.COLOR_RGB2HSV_FULL)) # Convert to HSV + + # Let's adjust the saturation of the image + saturation_factor = 1.5 # Adjust this value to your preference + hsv_img[..., 1] *= saturation_factor + + # Now let's convert the image back to RGB color space + hsv_img = np.clip(hsv_img, 0, 255) # Clip values to the range [0, 255] + hsv_img = np.asarray(hsv_img, dtype=np.uint8) # Convert back to uint8 + rgb_img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2RGB_FULL) # Convert back to RGB + + # The resulting image with increased saturation is stored in the 'rgb_img' variable + + def rgb2ycbcr(rgb_img): + # Create the conversion matrix for RGB to YCbCr + conv_mat = np.array([[0.299, 0.587, 0.114], + [-0.168736, -0.331264, 0.5], + [0.5, -0.418688, -0.081312]]) + + # Reshape the input image to a 2D array of pixels + pixels = rgb_img.reshape(-1, 3) + + # Apply the conversion matrix to the pixels + ycbcr_pixels = np.dot(pixels, conv_mat.T) + + # Reshape the converted pixels back into the original image shape + ycbcr_img = ycbcr_pixels.reshape(rgb_img.shape) + + # Convert the image data type to uint8 and return it + return ycbcr_img.astype(np.uint8) + + def ycbcr2rgb(ycbcr_img): + # Create the conversion matrix for YCbCr to RGB + conv_mat = np.array([[1.0, 0.0, 1.402], + [1.0, -0.344136, -0.714136], + [1.0, 1.772, 0.0]]) + + # Reshape the input image to a 2D array of pixels + pixels = ycbcr_img.reshape(-1, 3) + + # Apply the conversion matrix to the pixels + rgb_pixels = np.dot(pixels, conv_mat.T) + + # Reshape the converted pixels back into the original image shape + rgb_img = rgb_pixels.reshape(ycbcr_img.shape) + + # Convert the image data type to uint8 and return it + return rgb_img.astype(np.uint8) + + def adjust_luminance(img, mask, adjustment): + # Convert the input image to the YCbCr color space + img = (255.0 * img).astype(np.uint8) + mask = (255.0 * mask).astype(np.uint8) + + ycbcr_img = rgb2ycbcr(img) + ycbcr_mask = rgb2ycbcr(mask) + print(ycbcr_mask) + print(ycbcr_img) + plot_histogram(ycbcr_img, title="ycbcr") + plot_histogram(ycbcr_mask, title="ycmask") + # Separate the luminance channel (Y) + y_channel = ycbcr_img[..., 0] + y_channel_mask = ycbcr_mask[..., 0] + + # Apply the adjustment to the luminance channel using the mask + y_adjusted = np.clip(y_channel + (adjustment * y_channel_mask), 0, 255).astype(np.uint8) + + # Replace the original luminance channel with the adjusted one + ycbcr_img[..., 0] = y_adjusted + + # Convert the image back to the RGB color space and return it + return ycbcr2rgb(ycbcr_img) + + def tonemap_reinhard(image, gamma=2.2, intensity=0.18, light_adapt=0.8): + """ + Tonemaps the input HDR image using the Reinhard algorithm. + + Args: + image: The input HDR image as a NumPy array. + gamma: The gamma correction value to apply to the output image. + intensity: The target scene brightness. + light_adapt: The adaptation rate for the brightness. + + Returns: + The tonemapped output image as a NumPy array. + """ + # Convert the image to floating point RGB. + #image = image.astype(np.float32) + + # Compute the log average luminance. + lum = np.exp(np.mean(np.log(0.0001 + image))) + + # Normalize the image by the log average luminance. + image /= lum + + # Apply the Reinhard tonemapping algorithm. + mapped = np.zeros_like(image) + mapped = intensity * (mapped / np.max(mapped)) + mapped = light_adapt * (mapped * (1 + mapped / np.max(mapped) ** 2)) / (1 + mapped) + mapped *= lum + + # Apply gamma correction to the tonemapped image. + mapped = np.power(np.clip(mapped, 0, 1), 1 / gamma) + + # Convert the tonemapped image to 8-bit RGB. + mapped = (255 * mapped).astype(np.uint8) + + return mapped + + def f(t): + # Helper function to compute the nonlinear transformation function for the LAB color space + delta = 6.0 / 29.0 + t_thresh = delta ** 3 + return np.where(t > t_thresh, t ** (1 / 3), (1 / 3) * (29 / 6) ** 2 * t + 4 / 29) + + def numpy_lab2rgb(lab_img): + XYZ = np.zeros_like(lab_img, dtype=np.float32) + XYZ[..., 0] = (lab_img[..., 0] + 16.0) / 116.0 + XYZ[..., 1] = (lab_img[..., 1] / 500.0) + XYZ[..., 0] + XYZ[..., 2] = XYZ[..., 0] - (lab_img[..., 2] / 200.0) + mask = XYZ > 0.2068966 + XYZ[mask] = XYZ[mask] ** 3 + XYZ[~mask] = (XYZ[~mask] - 16.0 / 116.0) / 7.787 + D50 = np.array([0.9642, 1.0, 0.8249], dtype=np.float32) + RGB_linear = np.dot(XYZ, + np.array([[3.2406, -1.5372, -0.4986], [-0.9689, 1.8758, 0.0415], [0.0557, -0.2040, 1.0570]], + dtype=np.float32).T) + RGB_linear = np.clip(RGB_linear, 0.0, 1.0) + RGB_linear_D50 = RGB_linear / D50 + sRGB = np.where(RGB_linear_D50 <= 0.0031308, 12.92 * RGB_linear_D50, + 1.055 * (RGB_linear_D50 ** (1.0 / 2.4)) - 0.055) + sRGB = np.clip(sRGB, 0.0, 1.0) + return (RGB_linear_D50 * 255).astype(np.uint8) + + def numpy_split_lab(lab_img): + L, a, b = np.rollaxis(lab_img, axis=-1) + return L, a, b + + def numpy_merge_lab(l_channel_adjusted, a_channel, b_channel): + merged_lab = np.dstack((l_channel_adjusted, a_channel, b_channel)) + return merged_lab + + def rgb2lab(img): + # Convert the RGB image to a float array with values between 0 and 1 + img = img.astype(np.float32) / 255.0 + + # Convert the RGB image to the XYZ color space + # using the transformation matrix specified by the CIE + # (https://en.wikipedia.org/wiki/CIE_1931_color_space) + r, g, b = np.split(img, 3, axis=2) + x = 0.412453 * r + 0.357580 * g + 0.180423 * b + y = 0.212671 * r + 0.715160 * g + 0.072169 * b + z = 0.019334 * r + 0.119193 * g + 0.950227 * b + + # Convert the XYZ image to the LAB color space using the D50 white point + # (https://en.wikipedia.org/wiki/Lab_color_space#Conversion_from_XYZ_to_Lab) + x /= 0.950456 + z /= 1.088754 + l = 116.0 * f(y) - 16.0 + a = 500.0 * (f(x) - f(y)) + b = 200.0 * (f(y) - f(z)) + + # Stack the LAB channels back into a single image and return it + return np.concatenate((l, a, b), axis=2) + + def apply_luminance_mask_lab(img, mask): + # Convert the image to the LAB color space + lab_img = rgb2lab(img) + print(lab_img) + + lab_mask = rgb2lab(mask) + print(lab_mask) + # Split the LAB image into its channels + l_channel, a_channel, b_channel = numpy_split_lab(lab_img) + l_channel_mask, a_channel, b_channel = numpy_split_lab(lab_mask) + + # Apply the mask to the luminance channel + l_channel_adjusted = l_channel + l_channel_mask # np.where(l_channel_mask > 0, l_channel - 50, l_channel) + + # Merge the adjusted channels back into a LAB image + lab_img_adjusted = numpy_merge_lab(l_channel_adjusted, a_channel, b_channel) + + # Convert the adjusted image back to the RGB color space and return it + return numpy_lab2rgb(lab_img_adjusted) + + def rgb2lab(img): + # Convert the RGB image to a float array with values between 0 and 1 + img = img.astype(np.float32) / 255.0 + + # Convert the RGB image to the XYZ color space + # using the transformation matrix specified by the CIE + # (https://en.wikipedia.org/wiki/CIE_1931_color_space) + r, g, b = np.split(img, 3, axis=2) + x = 0.412453 * r + 0.357580 * g + 0.180423 * b + y = 0.212671 * r + 0.715160 * g + 0.072169 * b + z = 0.019334 * r + 0.119193 * g + 0.950227 * b + + # Convert the XYZ image to the LAB color space using the D50 white point + # (https://en.wikipedia.org/wiki/Lab_color_space#Conversion_from_XYZ_to_Lab) + x /= 0.950456 + z /= 1.088754 + l = 116.0 * f(y) - 16.0 + a = 500.0 * (f(x) - f(y)) + b = 200.0 * (f(y) - f(z)) + + # Stack the LAB channels back into a single image and return it + return np.concatenate((l, a, b), axis=2) + + diff --git a/picamburst.py b/picamburst.py index 64c41f2..811ec8a 100644 --- a/picamburst.py +++ b/picamburst.py @@ -1,5 +1,6 @@ #!//usr/bin/python3 +import numpyHDR from picamera2 import Picamera2 from libcamera import controls @@ -9,7 +10,13 @@ picam2.configure(config) picam2.start() picam2.set_controls({"AwbEnable": 1, "AeEnable": 1, "AeConstraintMode": controls.AeConstraintModeEnum.Highlight}) -np_array = picam2.capture_array() -print(np_array) +np_array_ev0 = picam2.capture_array() +picam2.set_controls({"AwbEnable": 1, "AeEnable": 1, "AeConstraintMode": controls.AeConstraintModeEnum.Shadows}) +np_array_ev1 = picam2.capture_array() +picam2.set_controls({"AwbEnable": 1, "AeEnable": 1, "AeConstraintMode": controls.AeConstraintModeEnum.Normal}) +np_array_ev_neg1 = picam2.capture_array() +picam2.helpers.save(np_array_ev0, metadata, "1.jpg") +picam2.helpers.save(np_array_ev1, metadata, "2.jpg") +picam2.helpers.save(np_array_ev_neg1, metadata, "3.jpg") picam2.stop() \ No newline at end of file