import gradio as gr import numpy as np import plotly.graph_objs as go from scipy.ndimage import convolve from gradio_imageslider import ImageSlider import cv2 import os # def readRAW(path): # arr = np.fromfile(path, dtype=np.int16).reshape(96,240,256) # # 将最后一维重塑为 (-1, 2)，其中 -1 自动计算为 128 # reshaped = arr.reshape(*arr.shape[:-1], -1, 2) # # 交换每一对中的两个元素 # swapped = reshaped[..., :, ::-1] # # 恢复原始形状 # histogram_data = swapped.reshape(arr.shape) # # 定义映射顺序：对每组8行进行调换 # mapping = [0, 4, 1, 5, 2, 6, 3, 7] # # 每组包含的行数 # group_size = 8 # num_groups = 12 # 96/8 # # 创建一个用于存储结果的数组（也可以原地修改） # output = np.empty_like(histogram_data) # # 对每个 group 分别进行行重排 # for g in range(num_groups): # start = g * group_size # end = start + group_size # output[start:end,:,:] = histogram_data[start:end,:,:][mapping,:,:] # return output def mipi_raw10_to_raw8_scaled(raw10_data): raw10_data = np.frombuffer(raw10_data, dtype=np.uint8) n_blocks = len(raw10_data) // 5 raw10_data = raw10_data[:n_blocks * 5].reshape(-1, 5) p0 = (raw10_data[:, 0].astype(np.uint16) << 2) | ((raw10_data[:, 4] >> 0) & 0x03) p1 = (raw10_data[:, 1].astype(np.uint16) << 2) | ((raw10_data[:, 4] >> 2) & 0x03) p2 = (raw10_data[:, 2].astype(np.uint16) << 2) | ((raw10_data[:, 4] >> 4) & 0x03) p3 = (raw10_data[:, 3].astype(np.uint16) << 2) | ((raw10_data[:, 4] >> 6) & 0x03) raw8_data = np.empty((n_blocks * 4 * 2,), dtype=np.uint8) raw8_data[0::8] = p0 & 0xFF raw8_data[1::8] = p0 >> 8 raw8_data[2::8] = p1 & 0xFF raw8_data[3::8] = p1 >> 8 raw8_data[4::8] = p2 & 0xFF raw8_data[5::8] = p2 >> 8 raw8_data[6::8] = p3 & 0xFF raw8_data[7::8] = p3 >> 8 return raw8_data.tobytes() def readRAW(path): filesize = os.path.getsize(path) with open(path, "rb") as f: raw_data = f.read() # Case 1: 如果是 MIPI RAW10 格式，大小为 7,372,800 字节 if filesize == 7372800: raw_data = mipi_raw10_to_raw8_scaled(raw_data) # 转换为 int16 并 reshape arr = np.frombuffer(raw_data, dtype=np.int16).reshape(96, 240, 256) # Byte Swap: [x,y,256] → [x,y,128,2] → swap last dim → [x,y,256] reshaped = arr.reshape(*arr.shape[:-1], -1, 2) swapped = reshaped[..., ::-1] histogram_data = swapped.reshape(arr.shape) # Line remapping (每组8行：0,4,1,5,...) mapping = [0, 4, 1, 5, 2, 6, 3, 7] group_size = 8 num_groups = 12 # 96 / 8 output = np.empty_like(histogram_data) for g in range(num_groups): start = g * group_size end = start + group_size output[start:end, :, :] = histogram_data[start:end, :, :][mapping, :, :] return output def load_bin(file, threshold=3): raw_hist = readRAW(file.name) multishot = (raw_hist[..., 254] * 1024 + raw_hist[..., 255]).astype(np.float32) normalize_data = 12000 / multishot nor_hist = raw_hist * normalize_data[..., np.newaxis] img = np.sum(nor_hist[:, :, :-2], axis=2) norm_img = (img - img.min()) / (img.max() + 1e-8) img_uint8 = (norm_img * 255).astype(np.uint8) img_zoomed = np.repeat(np.repeat(img_uint8, 4, axis=0), 4, axis=1) depth_slider_imgs = plot_depth(nor_hist,threshold) # 👈 直接在这里计算 return img_zoomed, raw_hist, nor_hist, depth_slider_imgs def plot_pixel_histogram(evt: gr.SelectData, raw_hist, nor_hist): # print("evt:", evt) x, y = evt.index # Gradio SelectData 对象 x = x // 4 y = y // 4 raw_values = raw_hist[y, x, :] nor_values = nor_hist[y, x, :] fig = go.Figure() fig.add_trace(go.Scatter(y=raw_values, mode="lines+markers")) fig.update_layout( title=f"Pixel ({x}, {y}) 在所有 {raw_values.shape[0]} 帧的强度变化", xaxis_title="帧索引 (T)", yaxis_title="强度值", ) return fig def to_uint8_image(arr): norm = (arr) / (np.max(arr) + 1e-8) return (norm * 255).astype(np.uint8) def plot_depth(norm_hist, threshold): tof = np.argmax(norm_hist[...,:-5], axis=2) img_tof = to_uint8_image(tof) noise = np.median(norm_hist[...,:8],axis=2) noise_th = noise + 4 * np.sqrt(noise) norm_hist_sub_noise = norm_hist - noise_th[...,np.newaxis] # norm_hist = norm_hist norm_hist_sub_noise[norm_hist_sub_noise<0]=0 norm_hist_pool = norm_hist_sub_noise[::10,::10,:] print(norm_hist_pool.shape) lst_scatter_th = [] for idx in range(0,256): map = norm_hist_pool[...,idx] ratio = 1/np.max(map) * 255 map_ratio = map * ratio _, otsu_thresh = cv2.threshold(map.astype(np.uint8), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_TRIANGLE) # _, otsu_thresh = cv2.threshold(map_ratio.astype(np.uint8), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) lst_scatter_th.append(_/ratio) de_scatter = norm_hist_sub_noise - np.array(lst_scatter_th)[np.newaxis, np.newaxis, :] de_scatter[de_scatter<0]=0 # de_scatter = norm_hist tof = np.argmax(de_scatter[...,:-5], axis=2) peak = np.max(de_scatter[...,:-5], axis=2) r = 4 neighbor_score = 16 snr_th = 0.1 for y in range(norm_hist.shape[0]): for x in range(norm_hist.shape[1]): t = tof[y,x] shift = np.argmax(norm_hist[y,x,max(0,t-r):min(255,t+r+1)]) -r t = t + shift cubic_S = de_scatter[max(0,y-1):min(norm_hist.shape[0],y+2), max(0,x-1):min(norm_hist.shape[1],x+2), max(0,t-1):min(255,t+2)] cubic_SN = norm_hist[max(0,y-1):min(norm_hist.shape[0],y+2), max(0,x-1):min(norm_hist.shape[1],x+2), max(0,t-1):min(255,t+2)] snr = cubic_S/np.sqrt(cubic_SN + 1e-6) # print(shift) mask = snr> snr_th if abs(shift) <= r and np.sum(mask) > neighbor_score: tof[y,x] = t else: tof[y,x] = 0 def get_value_at_depth_index(array_3d, depth_index): return array_3d[np.arange(array_3d.shape[0])[:, None], np.arange(array_3d.shape[1]), depth_index] C3 = get_value_at_depth_index(norm_hist,tof-1) C4 = get_value_at_depth_index(norm_hist,tof) C5 = get_value_at_depth_index(norm_hist,tof+1) shift_mat = (C5-C3)/(4.0 * C4 -2.0 * C3 - 2.0 * C5 + 1e-6) mask = abs(shift_mat) < 1 shift_mat = shift_mat * mask tof[tof<0]=0 img_filter = to_uint8_image(tof) colored_tof = cv2.applyColorMap(img_tof, cv2.COLORMAP_VIRIDIS)[:, :, ::-1] colored_tof_filter = cv2.applyColorMap(img_filter, cv2.COLORMAP_VIRIDIS)[:, :, ::-1] return [colored_tof, colored_tof_filter] # return [img_ref, img_ref_filter] with gr.Blocks() as demo: gr.Markdown("## 上传 96×240×256 int16 `.bin/.raw` 文件，点击图像像素查看该像素的 256 帧直方图") file_input = gr.File(label="上传 .raw/.bin 文件", file_types=[".raw", ".bin"]) image_display = gr.Image(interactive=True, label="点击像素显示强度曲线") histogram = gr.Plot(label="像素强度曲线") depth_image_slider = ImageSlider(label="Filter Depth Map with Slider View", elem_id='img-display-output', position=0.5) threshold_slider = gr.Slider(1, 30, value=3, step=1, label="Mask 阈值设定 (ref > x)") raw_hist = gr.State() nor_hist = gr.State() # 单一入口统一触发 file_input.change( load_bin, inputs=[file_input,threshold_slider], outputs=[image_display, raw_hist, nor_hist, depth_image_slider] ) image_display.select( plot_pixel_histogram, inputs=[raw_hist, nor_hist], outputs=histogram ) threshold_slider.change( load_bin, inputs=[file_input,threshold_slider], outputs=[image_display, raw_hist, nor_hist, depth_image_slider]) demo.launch()