使用兩個(gè)攝像頭來(lái)計(jì)算深度圖，在沒(méi)有RGBD的情況下可以使用，不過(guò)為了保持效率，需要使用Jetson邊緣計(jì)算設(shè)備。

先輸入的是兩個(gè)IMX219的視頻流，捕獲的視頻流是1080P的，接著傳為G流這個(gè)后處理器，看圖是處理了一下顏色空間。

這個(gè)VPI我查一下，是英偉達(dá)家的視覺(jué)編程接口，總之就是進(jìn)行了一些轉(zhuǎn)換的過(guò)程。

大概是這樣的

最后進(jìn)行這個(gè)合成運(yùn)算和對(duì)齊

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capture->rectify->SGBM->depth calc.

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數(shù)據(jù)流管道

處理流程

IMX219，Sony家的東西

sudo apt install libnvvpi1 python3-vpi1 vpi1-dev

需要安裝的庫(kù)

https://repo.download.nvidia.com/jetson/

但是這個(gè)源碼我找不到了，就是找到了deb的庫(kù)。

英偉達(dá)的庫(kù)好全啊

當(dāng)然了，算法很全面

安裝后里面有的內(nèi)容

可以處理的視頻流后端，這里是支持jetson的（廢話）

后端使用CUDA的結(jié)果

寫(xiě)一個(gè)，Python是快，但是不能進(jìn)行內(nèi)存管理，CSDK可以。U8是八位無(wú)符號(hào)數(shù)。

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#include #include #if CV_MAJOR_VERSION >= 3#include #else#include #endif#include #include 
#include #include #include #include 
int main(int argc, char *argv[]){  
  if (argc != 2)    {  
      std::cerr << "Must pass an input image to be blurred" << std::endl;  
      return 1;    }    cv::Mat cvImage = cv::imread(argv[1]); 
   if (cvImage.data == NULL)    {  
      std::cerr << "Can't open input image" << std::endl;    
    return 2;    }
    VPIStream stream;  
  vpiStreamCreate(0, &stream); 
   VPIImage image;  
  vpiImageCreateWrapperOpenCVMat(cvImage, 0, &image); 
   VPIImage imageGray;  
  vpiImageCreate(cvImage.cols, cvImage.rows, VPI_IMAGE_FORMAT_U8, 0, &imageGray);
    VPIImage blurred;  
  vpiImageCreate(cvImage.cols, cvImage.rows, VPI_IMAGE_FORMAT_U8, 0, &blurred);   
   vpiSubmitConvertImageFormat(stream, VPI_BACKEND_CUDA, image, imageGray, NULL);    
 vpiStreamSync(stream);   
 VPIImageData outData;  
  vpiImageLockData(blurred, VPI_LOCK_READ, VPI_IMAGE_BUFFER_HOST_PITCH_LINEAR, &outData);  
  cv::Mat cvOut;  
  vpiImageDataExportOpenCVMat(outData, &cvOut);
    imwrite("tutorial_blurred.png", cvOut);  
  vpiImageUnlock(blurred);  
  vpiStreamDestroy(stream);  
  vpiImageDestroy(image);
    vpiImageDestroy(imageGray);   
 vpiImageDestroy(blurred);  
  return 0;}

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CPP的版本，還需要Cmake編譯

主要是為了加速運(yùn)行視覺(jué)的算法

算法：表示不可分割的計(jì)算操作。
后端：代表負(fù)責(zé)實(shí)際計(jì)算的硬件引擎。
Streams：充當(dāng)提交算法的異步隊(duì)列，最終在給定后端按順序執(zhí)行。流和事件是計(jì)算管道的構(gòu)建塊。
緩沖區(qū)：存儲(chǔ)輸入和輸出數(shù)據(jù)。
事件：提供在流和/或應(yīng)用程序線程上操作的同步原語(yǔ)。
上下文：保存 VPI 和創(chuàng)建對(duì)象的狀態(tài)。

可以運(yùn)行的硬件

補(bǔ)一個(gè)硬件的淘汰路線

最近一個(gè)版本是有了Python的支持

真新啊

這個(gè)項(xiàng)目一開(kāi)始需要的是進(jìn)行攝像頭的標(biāo)定。

導(dǎo)入的庫(kù)

將棋盤(pán)格引入

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wget https://www.dropbox.com/sh/j4w6tg9b0ov209g/AAA1HfT7Yy33tmGI1tWaqDKCa?dl=1 -O /tmp/calib_files.zipunzip?/tmp/calib_files.zip

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需要提前執(zhí)行一下

接著開(kāi)始處理

ret, K_l, dist_coeff_l, rvecs, tvecs = cv2.calibrateCamera(obj_pts, img_pts, (arr.shape[1], arr.shape[0]), None,None)

得到校準(zhǔn)的參數(shù)

參數(shù)到手

img = Image.open(FOLDER+"001.png")arr = np.array(img)
arr_corr = cv2.undistort(arr, K_l, dist_coeff_l, None, K_l)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))ax1.imshow(arr, cmap='gray')ax2.imshow(arr_corr, cmap='gray')plt.show()

因?yàn)槭囚~(yú)眼的相機(jī)，這里使用標(biāo)定的參數(shù)進(jìn)行校正。

就是這樣

因?yàn)槭莾蓚€(gè)相機(jī)，記得標(biāo)定兩次，然后保存參數(shù)：

np.save("K_l.npy", K_l)np.save("K_r.npy", K_r)
np.save("dist_coeff_l.npy", dist_coeff_l)np.save("dist_coeff_r.npy", dist_coeff_r)

重新開(kāi)個(gè)Jupyter，導(dǎo)入庫(kù)，生成一張新圖，把相機(jī)的照片準(zhǔn)備好

建立一些列表來(lái)存放我們的數(shù)據(jù)，使用ZIP來(lái)進(jìn)行遍歷，處理角點(diǎn)

現(xiàn)在就只相當(dāng)于左右兩個(gè)棋盤(pán)圖像對(duì)齊操作

將攝像頭對(duì)齊

使用立體校正

rect_l, rect_r, proj_mat_l, proj_mat_r, Q, roiL, roiR = cv2.stereoRectify(K_l, dist_l, K_r, dist_r, gray_l.shape[::-1], Rot, Trns, 1 ,(0,0))

left_stereo_maps = cv2.initUndistortRectifyMap(K_l, dist_l, rect_l, proj_mat_l,                        
                     gray_l.shape[::-1], cv2.CV_16SC2)right_stereo_maps = cv2.initUndistortRectifyMap(K_r, dist_r, rect_r, proj_mat_r,                      
                        gray_l.shape[::-1], cv2.CV_16SC2)

初始化每個(gè)校正過(guò)的視圖

arr_l_rect = cv2.remap(arr_l, left_stereo_maps[0],left_stereo_maps[1], cv2.INTER_LANCZOS4, cv2.BORDER_CONSTANT, 0)arr_r_rect = cv2.remap(arr_r, right_stereo_maps[0],right_stereo_maps[1], cv2.INTER_LANCZOS4, cv2.BORDER_CONSTANT, 0)

將函數(shù)進(jìn)行進(jìn)行映射

開(kāi)始進(jìn)行校正

看一張就行

效果不錯(cuò)的情況下都安排了

讀取所有的圖像

對(duì)單個(gè)圖像校準(zhǔn)

fig, axs = plt.subplots(2, 2, figsize=(20,10))
# beforeaxs[0][0].title.set_text('Original L')axs[0][0].imshow(arr_l)
axs[0][1].title.set_text('Original R')axs[0][1].imshow(arr_r)
axs[1][0].title.set_text('Rectified L')axs[1][0].imshow(arr_l_rect)axs[1][0].title.set_text('Rectified R')axs[1][1].imshow(arr_r_rect)

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結(jié)果

開(kāi)始融合

可以先看下數(shù)組形狀

X和Y的

組合一下

最終的結(jié)果

import timeimport queueimport numpy as npimport cv2from threading import Threadfrom camera import Camerafrom concurrent.futures import ThreadPoolExecutor
MAX_DISP = 128WINDOW_SIZE = 10

class Depth(Thread):    def __init__(self):
        super().__init__()        print("Reading camera calibration...")   
     self._map_l, self._map_r = get_calibration()      
  self._cam_r = CameraThread(0)    
    self._cam_l = CameraThread(1)    
    self._disp_arr = None        self._should_run = True 
       self._dq = queue.deque(maxlen=3)    
    self._executor = ThreadPoolExecutor(max_workers=4)  
      self.start()
        # Wait for the deque to start filling up        while len(self._dq) < 1:            time.sleep(0.1)
    def stop(self):        self._should_run = False        self._cam_l.stop()        self._cam_r.stop()
    def disparity(self):        while len(self._dq) == 0:            time.sleep(0.01)        return self._dq.pop()
    def enqueue_async(self, vpi_image):        arr = vpi_image.cpu().copy()        self._dq.append(arr)        del vpi_image
    def run(self):        import vpi  # Don't import vpi in main thread to avoid creating a global context
        i = 0        self._warp_l = make_vpi_warpmap(self._map_l)        self._warp_r = make_vpi_warpmap(self._map_r)        ts_history = []
        while self._should_run:            i += 1            ts = []            ts.append(time.perf_counter())
            with vpi.Backend.CUDA:                ts.append(time.perf_counter())                # confidenceMap = vpi.Image(vpi_l.size, vpi.Format.U16)
                # Read Images                arr_l = self._cam_l.image                arr_r = self._cam_r.image                ts.append(time.perf_counter())
                # Convert to VPI image                vpi_l = vpi.asimage(arr_l)                vpi_r = vpi.asimage(arr_r)                ts.append(time.perf_counter())
                # Rectify                vpi_l = vpi_l.remap(self._warp_l)                vpi_r = vpi_r.remap(self._warp_r)                ts.append(time.perf_counter())
                # Resize                vpi_l = vpi_l.rescale(                    (480, 270), interp=vpi.Interp.LINEAR, border=vpi.Border.ZERO                )                vpi_r = vpi_r.rescale(            
        (480, 270), interp=vpi.Interp.LINEAR, border=vpi.Border.ZERO                )                ts.append(time.perf_counter())
                # Convert to 16bpp                vpi_l_16bpp = vpi_l.convert(vpi.Format.U16, scale=1)                vpi_r_16bpp = vpi_r.convert(vpi.Format.U16, scale=1)                ts.append(time.perf_counter())
                # Disparity                disparity_16bpp = vpi.stereodisp(                    vpi_l_16bpp,                    vpi_r_16bpp,                    out_confmap=None,                    backend=vpi.Backend.CUDA,         
           window=WINDOW_SIZE,                    maxdisp=MAX_DISP,                )                ts.append(time.perf_counter())
                # Convert again                disparity_8bpp = disparity_16bpp.convert(                    vpi.Format.U8, scale=255.0 / (32 * MAX_DISP)                )                ts.append(time.perf_counter())
                # CPU mapping                # self._disp_arr = disparity_8bpp                self._executor.submit(self.enqueue_async, disparity_8bpp)                # global frames                # frames.append(self._disp_arr)       
         ts.append(time.perf_counter())
                # Render                # cv2.imshow("Disparity", disp_arr)                # cv2.waitKey(1)                ts.append(time.perf_counter())
                ts = np.array(ts)                ts_deltas = np.diff(ts)
                ts_history.append(ts_deltas)
                debug_str = f"Iter {i}
"
            tasks = [       
         "Enter context",             


   "Read images",          


      "Convert to VPI image",                "Rectify",  
              "Resize 1080p->270p",                "Convert to 16bpp",     
           "Disparity",                "Convert 16bpp->8bpp",        
        ".cpu() mapping",                "OpenCV colormap",         
       "Render",            ]
            debug_str += f"Sum: {sum(ts_deltas):0.2f}

"            task_acc_time = 1000 * np.array(ts_history).mean(axis=0)
            for task, dt in zip(tasks, task_acc_time):          
      debug_str += f"{task.ljust(20)} {dt:0.2f}
"
            # print(debug_str)

            if i % 10 == 0:     
           vpi.clear_cache()

def disp2depth(disp_arr):    F, B, PIXEL_SIZE = (CAM_PARAMS.F, CAM_PARAMS.B, CAM_PARAMS.PIXEL_SIZE)   
 disp_arr[disp_arr < 10] = 10    disp_arr_mm = disp_arr * PIXEL_SIZE  # convert disparites from px -> mm  
  depth_arr_mm = F * B / (disp_arr_mm + 1e-10)  # calculate depth    return depth_arr_mm

def get_calibration() -> tuple:  
  fs = cv2.FileStorage(        "calib/rectify_map_imx219_160deg_1080p.yaml", cv2.FILE_STORAGE_READ   
 )    map_l = (fs.getNode("map_l_x").mat(), fs.getNode("map_l_y").mat())    map_r = (fs.getNode("map_r_x").mat(), fs.getNode("map_r_y").mat())    fs.release()
    return map_l, map_r

def make_vpi_warpmap(cv_maps):    import vpi
    src_map = cv_maps[0]    idk_what_that_is = cv_maps[1]    map_y, map_x = src_map[:, :, 0], src_map[:, :, 1]
    warp = vpi.WarpMap(vpi.WarpGrid((1920, 1080)))    # (H, W, C) -> (C,H,W)    arr_warp = np.asarray(warp)    wx = arr_warp[:, :, 0]    wy = arr_warp[:, :, 1]
    wy[:1080, :] = map_x    wx[:1080, :] = map_y
    return warp

class CameraThread(Thread):    def __init__(self, sensor_id) -> None:
        super().__init__()        self._camera = Camera(sensor_id)        self._should_run = True        self._image = self._camera.read()        self.start()
    def run(self):        while self._should_run:            self._image = self._camera.read()
    @property    def image(self):        # NOTE: if we care about atomicity of reads, we can add a lock here        return self._image
    def stop(self):        self._should_run = False        self._camera.stop()

def calculate_depth(disp_arr):    F, B, PIXEL_SIZE = (CAM_PARAMS.F, CAM_PARAMS.B, CAM_PARAMS.PIXEL_SIZE)    disp_arr_mm = disp_arr * PIXEL_SIZE  # disparites in mm  
  depth_arr_mm = F * B / (disp_arr_mm + 1e-1)  # distances    return depth_arr_mm

if __name__ == "__main__":
    DISPLAY = True    SAVE = True    frames_d = []    frames_rgb = []
    depth = Depth()    t1 = time.perf_counter()
    for i in range(100):        disp_arr = depth.disparity()        frames_d.append(disp_arr)        frames_rgb.append(depth._cam_l.image)   
     print(i)        # print(disp_arr)        # depth_arr = calculate_depth(disp_arr)        # print(f"{i}/20: {disp_arr:0.2f}")     
   # time.sleep(1)
        if DISPLAY:            disp_arr = cv2.applyColorMap(disp_arr, cv2.COLORMAP_TURBO)    
        cv2.imshow("Depth", disp_arr)       
     cv2.imshow("Image", cv2.resize(depth._cam_l.image, (480, 270)))            cv2.waitKey(1)
    depth.stop()    t2 = time.perf_counter()    print(f"Approx framerate: {len(frames_d)/(t2-t1)} FPS")
    # Save frames    for i, (disp_arr, rgb_arr) in enumerate(zip(frames_d, frames_rgb)):        print(f"{i}/{len(frames_d)}", end="
")        disp_arr = cv2.applyColorMap(disp_arr, cv2.COLORMAP_TURBO)        cv2.imwrite(f"images/depth_{i}.jpg", disp_arr)        cv2.imwrite(f"images/rgb_{i}.jpg", rgb_arr)

寫(xiě)了這么多了，不寫(xiě)了，給個(gè)demo。

給出相機(jī)的打印件

后面

由遠(yuǎn)及近

編輯：黃飛