拍视频版本

surround_view/__init__.py

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+from .fisheye_camera import FisheyeCameraModel
+from .imagebuffer import MultiBufferManager
+from .capture_thread import CaptureThread
+from .process_thread import CameraProcessingThread
+from .simple_gui import display_image, PointSelector
+from .birdview import BirdView, ProjectedImageBuffer

surround_view/base_thread.py

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+from queue import Queue
+import cv2
+from PyQt5.QtCore import (QThread, QTime, QMutex, pyqtSignal, QMutexLocker)
+
+from .structures import ThreadStatisticsData
+
+
+class BaseThread(QThread):
+
+    """
+    Base class for all types of threads (capture, processing, stitching, ...,
+    etc). Mainly for collecting statistics of the threads.
+    """
+
+    FPS_STAT_QUEUE_LENGTH = 32
+
+    update_statistics_gui = pyqtSignal(ThreadStatisticsData)
+
+    def __init__(self, parent=None):
+        super(BaseThread, self).__init__(parent)
+        self.init_commons()
+
+    def init_commons(self):
+        self.stopped = False
+        self.stop_mutex = QMutex()
+        self.clock = QTime()
+        self.fps = Queue()
+        self.processing_time = 0
+        self.processing_mutex = QMutex()
+        self.fps_sum = 0
+        self.stat_data = ThreadStatisticsData()
+
+    def stop(self):
+        with QMutexLocker(self.stop_mutex):
+            self.stopped = True
+
+    def update_fps(self, dt):
+        # add instantaneous fps value to queue
+        if dt > 0:
+            self.fps.put(1000 / dt)
+
+        # discard redundant items in the fps queue
+        if self.fps.qsize() > self.FPS_STAT_QUEUE_LENGTH:
+            self.fps.get()
+
+        # update statistics
+        if self.fps.qsize() == self.FPS_STAT_QUEUE_LENGTH:
+            while not self.fps.empty():
+                self.fps_sum += self.fps.get()
+
+            self.stat_data.average_fps = round(self.fps_sum / self.FPS_STAT_QUEUE_LENGTH, 2)
+            self.fps_sum = 0

surround_view/birdview.py

@@ -0,0 +1,386 @@
+import os
+import numpy as np
+import cv2
+from PIL import Image
+from PyQt5.QtCore import QMutex, QWaitCondition, QMutexLocker
+from .base_thread import BaseThread
+from .imagebuffer import Buffer
+from . import param_settings as settings
+from .param_settings import xl, xr, yt, yb
+from . import utils
+
+
+class ProjectedImageBuffer(object):
+
+    """
+    Class for synchronizing processing threads from different cameras.
+    """
+
+    def __init__(self, drop_if_full=True, buffer_size=8):
+        self.drop_if_full = drop_if_full
+        self.buffer = Buffer(buffer_size)
+        self.sync_devices = set()
+        self.wc = QWaitCondition()
+        self.mutex = QMutex()
+        self.arrived = 0
+        self.current_frames = dict()
+
+    def bind_thread(self, thread):
+        with QMutexLocker(self.mutex):
+            self.sync_devices.add(thread.device_id)
+
+        name = thread.camera_model.camera_name
+        shape = settings.project_shapes[name]
+        self.current_frames[thread.device_id] = np.zeros(shape[::-1] + (3,), np.uint8)
+        thread.proc_buffer_manager = self
+
+    def get(self):
+        return self.buffer.get()
+
+    def set_frame_for_device(self, device_id, frame):
+        if device_id not in self.sync_devices:
+            raise ValueError("Device not held by the buffer: {}".format(device_id))
+        self.current_frames[device_id] = frame
+
+    def sync(self, device_id):
+        # only perform sync if enabled for specified device/stream
+        self.mutex.lock()
+        if device_id in self.sync_devices:
+            # increment arrived count
+            self.arrived += 1
+            # we are the last to arrive: wake all waiting threads
+            if self.arrived == len(self.sync_devices):
+                self.buffer.add(self.current_frames, self.drop_if_full)
+                self.wc.wakeAll()
+            # still waiting for other streams to arrive: wait
+            else:
+                self.wc.wait(self.mutex)
+            # decrement arrived count
+            self.arrived -= 1
+        self.mutex.unlock()
+
+    def wake_all(self):
+        with QMutexLocker(self.mutex):
+            self.wc.wakeAll()
+
+    def __contains__(self, device_id):
+        return device_id in self.sync_devices
+
+    def __str__(self):
+        return (self.__class__.__name__ + ":\n" + \
+                "devices: {}\n".format(self.sync_devices))
+
+
+def FI(front_image):
+    return front_image[:, :xl]
+
+
+def FII(front_image):
+    return front_image[:, xr:]
+
+
+def FM(front_image):
+    return front_image[:, xl:xr]
+
+
+def BIII(back_image):
+    return back_image[:, :xl]
+
+
+def BIV(back_image):
+    return back_image[:, xr:]
+
+
+def BM(back_image):
+    return back_image[:, xl:xr]
+
+
+def LI(left_image):
+    return left_image[:yt, :]
+
+
+def LIII(left_image):
+    return left_image[yb:, :]
+
+
+def LM(left_image):
+
+    return left_image[yt:yb, :]
+
+
+def RII(right_image):
+    return right_image[:yt, :]
+
+
+def RIV(right_image):
+    return right_image[yb:, :]
+
+
+def RM(right_image):
+    
+    return right_image[yt:yb, :]
+
+
+class BirdView(BaseThread):
+
+    def __init__(self,
+                 proc_buffer_manager=None,
+                 drop_if_full=True,
+                 buffer_size=8,
+                 parent=None):
+        super(BirdView, self).__init__(parent)
+        self.proc_buffer_manager = proc_buffer_manager
+        self.drop_if_full = drop_if_full
+        self.buffer = Buffer(buffer_size)
+        self.image = np.zeros((settings.total_h, settings.total_w, 3), np.uint8)
+        self.weights = None
+        self.masks = None
+        self.car_image = settings.car_image
+        self.frames = None
+
+    def get(self):
+        return self.buffer.get()
+
+    def update_frames(self, images):
+        self.frames = images
+
+    def load_weights_and_masks(self, weights_image, masks_image):
+        GMat = np.asarray(Image.open(weights_image).convert("RGBA"), dtype=np.float) / 255.0
+        self.weights = [np.stack((GMat[:, :, k],
+                                  GMat[:, :, k],
+                                  GMat[:, :, k]), axis=2)
+                        for k in range(4)]
+
+        Mmat = np.asarray(Image.open(masks_image).convert("RGBA"), dtype=np.float)
+        Mmat = utils.convert_binary_to_bool(Mmat)
+        self.masks = [Mmat[:, :, k] for k in range(4)]
+
+    def merge(self, imA, imB, k):
+        G = self.weights[k]
+        return (imA * G + imB * (1 - G)).astype(np.uint8)
+
+    @property
+    def FL(self):
+        return self.image[:yt, :xl]
+
+    @property
+    def F(self):
+        return self.image[:yt, xl:xr]
+
+    @property
+    def FR(self):
+        return self.image[:yt, xr:]
+
+    @property
+    def BL(self):
+        return self.image[yb:, :xl]
+
+    @property
+    def B(self):
+        return self.image[yb:, xl:xr]
+
+    @property
+    def BR(self):
+        return self.image[yb:, xr:]
+
+    @property
+    def L(self):
+        return self.image[yt:yb, :xl]
+
+    @property
+    def R(self):
+        return self.image[yt:yb, xr:]
+
+    @property
+    def C(self):
+        return self.image[yt:yb, xl:xr]
+
+    def stitch_all_parts(self):
+       
+        front, back, left, right = self.frames
+        np.copyto(self.F, FM(front))
+        np.copyto(self.B, BM(back))
+        np.copyto(self.L, LM(left))
+        np.copyto(self.R, RM(right))
+        np.copyto(self.FL, self.merge(FI(front), LI(left), 0))
+        np.copyto(self.FR, self.merge(FII(front), RII(right), 1))
+        np.copyto(self.BL, self.merge(BIII(back), LIII(left), 2))
+        np.copyto(self.BR, self.merge(BIV(back), RIV(right), 3))
+
+    # def copy_car_image(self):
+    #     np.copyto(self.C, self.car_image)
+    def copy_car_image(self):
+        car_rgba = settings.car_image_rgba
+
+        x1 = settings.car_x
+        y1 = settings.car_y
+        h, w = car_rgba.shape[:2]
+        x2 = x1 + w
+        y2 = y1 + h
+
+        img_h, img_w = self.image.shape[:2]
+
+        # 裁剪到图像边界
+        x1c = max(x1, 0)
+        y1c = max(y1, 0)
+        x2c = min(x2, img_w)
+        y2c = min(y2, img_h)
+
+        # 对应裁剪 car_rgba
+        cx1 = x1c - x1
+        cy1 = y1c - y1
+        cx2 = cx1 + (x2c - x1c)
+        cy2 = cy1 + (y2c - y1c)
+
+        car_crop = car_rgba[cy1:cy2, cx1:cx2]
+        roi = self.image[y1c:y2c, x1c:x2c]
+
+        # ★ 强制对齐尺寸，避免广播错误
+        rh, rw = roi.shape[:2]
+        ch, cw = car_crop.shape[:2]
+        if rh != ch or rw != cw:
+            min_h = min(rh, ch)
+            min_w = min(rw, cw)
+            car_crop = car_crop[:min_h, :min_w]
+            roi = self.image[y1c:y1c + min_h, x1c:x1c + min_w]
+
+        alpha = car_crop[:, :, 3].astype(np.float32) / 255.0
+        alpha = np.stack([alpha, alpha, alpha], axis=2)
+
+        car_bgr = car_crop[:, :, :3].astype(np.float32)
+        roi_f   = roi.astype(np.float32)
+
+        blended = (car_bgr * alpha + roi_f * (1.0 - alpha)).astype(np.uint8)
+        np.copyto(self.image[y1c:y1c + blended.shape[0], x1c:x1c + blended.shape[1]], blended)
+
+    def make_luminance_balance(self):
+
+        def tune(x):
+            if x >= 1:
+                return x * np.exp((1 - x) * 0.5)
+            else:
+                return x * np.exp((1 - x) * 0.8)
+
+        front, back, left, right = self.frames
+        m1, m2, m3, m4 = self.masks
+        Fb, Fg, Fr = cv2.split(front)
+        Bb, Bg, Br = cv2.split(back)
+        Lb, Lg, Lr = cv2.split(left)
+        Rb, Rg, Rr = cv2.split(right)
+
+        a1 = utils.mean_luminance_ratio(RII(Rb), FII(Fb), m2)
+        a2 = utils.mean_luminance_ratio(RII(Rg), FII(Fg), m2)
+        a3 = utils.mean_luminance_ratio(RII(Rr), FII(Fr), m2)
+
+        b1 = utils.mean_luminance_ratio(BIV(Bb), RIV(Rb), m4)
+        b2 = utils.mean_luminance_ratio(BIV(Bg), RIV(Rg), m4)
+        b3 = utils.mean_luminance_ratio(BIV(Br), RIV(Rr), m4)
+
+        c1 = utils.mean_luminance_ratio(LIII(Lb), BIII(Bb), m3)
+        c2 = utils.mean_luminance_ratio(LIII(Lg), BIII(Bg), m3)
+        c3 = utils.mean_luminance_ratio(LIII(Lr), BIII(Br), m3)
+
+        d1 = utils.mean_luminance_ratio(FI(Fb), LI(Lb), m1)
+        d2 = utils.mean_luminance_ratio(FI(Fg), LI(Lg), m1)
+        d3 = utils.mean_luminance_ratio(FI(Fr), LI(Lr), m1)
+
+        t1 = (a1 * b1 * c1 * d1)**0.25
+        t2 = (a2 * b2 * c2 * d2)**0.25
+        t3 = (a3 * b3 * c3 * d3)**0.25
+
+        x1 = t1 / (d1 / a1)**0.5
+        x2 = t2 / (d2 / a2)**0.5
+        x3 = t3 / (d3 / a3)**0.5
+
+        x1 = tune(x1)
+        x2 = tune(x2)
+        x3 = tune(x3)
+
+        Fb = utils.adjust_luminance(Fb, x1)
+        Fg = utils.adjust_luminance(Fg, x2)
+        Fr = utils.adjust_luminance(Fr, x3)
+
+        y1 = t1 / (b1 / c1)**0.5
+        y2 = t2 / (b2 / c2)**0.5
+        y3 = t3 / (b3 / c3)**0.5
+
+        y1 = tune(y1)
+        y2 = tune(y2)
+        y3 = tune(y3)
+
+        Bb = utils.adjust_luminance(Bb, y1)
+        Bg = utils.adjust_luminance(Bg, y2)
+        Br = utils.adjust_luminance(Br, y3)
+
+        z1 = t1 / (c1 / d1)**0.5
+        z2 = t2 / (c2 / d2)**0.5
+        z3 = t3 / (c3 / d3)**0.5
+
+        z1 = tune(z1)
+        z2 = tune(z2)
+        z3 = tune(z3)
+
+        Lb = utils.adjust_luminance(Lb, z1)
+        Lg = utils.adjust_luminance(Lg, z2)
+        Lr = utils.adjust_luminance(Lr, z3)
+
+        w1 = t1 / (a1 / b1)**0.5
+        w2 = t2 / (a2 / b2)**0.5
+        w3 = t3 / (a3 / b3)**0.5
+
+        w1 = tune(w1)
+        w2 = tune(w2)
+        w3 = tune(w3)
+
+        Rb = utils.adjust_luminance(Rb, w1)
+        Rg = utils.adjust_luminance(Rg, w2)
+        Rr = utils.adjust_luminance(Rr, w3)
+
+        self.frames = [cv2.merge((Fb, Fg, Fr)),
+                       cv2.merge((Bb, Bg, Br)),
+                       cv2.merge((Lb, Lg, Lr)),
+                       cv2.merge((Rb, Rg, Rr))]
+        return self
+
+    def get_weights_and_masks(self, images):
+        front, back, left, right = images
+        G0, M0 = utils.get_weight_mask_matrix(FI(front), LI(left))
+        G1, M1 = utils.get_weight_mask_matrix(FII(front), RII(right))
+        G2, M2 = utils.get_weight_mask_matrix(BIII(back), LIII(left))
+        G3, M3 = utils.get_weight_mask_matrix(BIV(back), RIV(right))
+        self.weights = [np.stack((G, G, G), axis=2) for G in (G0, G1, G2, G3)]
+        self.masks = [(M / 255.0).astype(int) for M in (M0, M1, M2, M3)]
+        return np.stack((G0, G1, G2, G3), axis=2), np.stack((M0, M1, M2, M3), axis=2)
+
+    def make_white_balance(self):
+        self.image = utils.make_white_balance(self.image)
+
+    def run(self):
+        if self.proc_buffer_manager is None:
+            raise ValueError("This thread requires a buffer of projected images to run")
+
+        while True:
+            self.stop_mutex.lock()
+            if self.stopped:
+                self.stopped = False
+                self.stop_mutex.unlock()
+                break
+            self.stop_mutex.unlock()
+            self.processing_time = self.clock.elapsed()
+            self.clock.start()
+
+            self.processing_mutex.lock()
+
+            self.update_frames(self.proc_buffer_manager.get().values())
+            self.stitch_all_parts()  # 直接拼接原始投影图像
+            # self.make_luminance_balance().stitch_all_parts()
+            self.make_white_balance()
+            self.copy_car_image()
+            self.buffer.add(self.image.copy(), self.drop_if_full)
+            self.processing_mutex.unlock()
+
+            # update statistics
+            self.update_fps(self.processing_time)
+            self.stat_data.frames_processed_count += 1
+            # inform GUI of updated statistics
+            self.update_statistics_gui.emit(self.stat_data)

surround_view/capture_thread.py

@@ -0,0 +1,113 @@
+import cv2
+from PyQt5.QtCore import qDebug
+from .base_thread import BaseThread
+from .structures import ImageFrame
+
+
+
+class CaptureThread(BaseThread):
+
+    def __init__(self,
+                 device_id,
+                 flip_method=0,
+                 drop_if_full=True,
+                 api_preference=cv2.CAP_V4L2,
+                 resolution=None,
+                 parent=None):
+        """
+        device_id: device number of the camera.
+        flip_method: 0 for identity, 2 for 180 degree rotation (if the camera is installed
+            up-side-down).
+        drop_if_full: drop the frame if buffer is full.
+        api_preference: cv2.CAP_GSTREAMER for csi cameras, usually cv2.CAP_ANY would suffice.
+        resolution: camera resolution (width, height).
+        """
+        super(CaptureThread, self).__init__(parent)
+        self.device_id = device_id
+        self.flip_method = 0
+        self.drop_if_full = drop_if_full
+        self.api_preference = api_preference
+        self.resolution = resolution
+        self.cap = cv2.VideoCapture()
+        # an instance of the MultiBufferManager object,
+        # for synchronizing this thread with other cameras.
+        self.buffer_manager = None
+
+    def run(self):
+        if self.buffer_manager is None:
+            raise ValueError("This thread has not been binded to any buffer manager yet")
+
+        while True:
+            self.stop_mutex.lock()
+            if self.stopped:
+                self.stopped = False
+                self.stop_mutex.unlock()
+                break
+            self.stop_mutex.unlock()
+
+            # save capture time
+            self.processing_time = self.clock.elapsed()
+            # start timer (used to calculate capture rate)
+            self.clock.start()
+
+            # synchronize with other streams (if enabled for this stream)
+            self.buffer_manager.sync(self.device_id)
+
+            if not self.cap.grab():
+                continue
+
+            # retrieve frame and add it to buffer
+            _, frame = self.cap.read()
+          
+            # Skip empty frames (e.g., when camera capture times out)
+            if frame is None or frame.size == 0:
+                continue
+                
+            # Apply image flip if needed
+            if self.flip_method == 2:
+                frame = cv2.rotate(frame, cv2.ROTATE_180)
+                
+            # frame = cv2.resize(frame, (960, 640))
+            img_frame = ImageFrame(self.clock.msecsSinceStartOfDay(), frame)
+            self.buffer_manager.get_device(self.device_id).add(img_frame, self.drop_if_full)
+
+            # update statistics
+            # self.update_fps(self.processing_time)
+            # self.stat_data.frames_processed_count += 1
+            # # inform GUI of updated statistics
+            # self.update_statistics_gui.emit(self.stat_data)
+
+        qDebug("Stopping capture thread...")
+
+    def connect_camera(self):
+        self.cap.open(self.device_id)
+        # return false if failed to open camera
+        if not self.cap.isOpened():
+            qDebug("Cannot open camera {}".format(self.device_id))
+            return False
+        else:
+            # try to set camera resolution
+            if self.resolution is not None:
+                width, height = self.resolution
+                self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"YUYV"))
+                self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
+                self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
+                # some camera may become closed if the resolution is not supported
+                if not self.cap.isOpened():
+                    qDebug("Resolution not supported by camera device: {}".format(self.resolution))
+                    return False
+
+
+        return True
+
+    def disconnect_camera(self):
+        # disconnect camera if it's already opened.
+        if self.cap.isOpened():
+            self.cap.release()
+            return True
+        # else do nothing and return
+        else:
+            return False
+
+    def is_camera_connected(self):
+        return self.cap.isOpened()

surround_view/fisheye_camera.py

@@ -0,0 +1,128 @@
+import os
+import numpy as np
+import cv2
+
+from . import param_settings as settings
+
+
+class FisheyeCameraModel(object):
+
+    """
+    Fisheye camera model, for undistorting, projecting and flipping camera frames.
+    """
+
+    def __init__(self, camera_param_file, camera_name):
+        if not os.path.isfile(camera_param_file):
+            raise ValueError("Cannot find camera param file")
+
+        if camera_name not in settings.camera_names:
+            raise ValueError("Unknown camera name: {}".format(camera_name))
+
+        self.camera_file = camera_param_file
+        self.camera_name = camera_name
+        self.scale_xy = (1.0, 1.0)
+        self.shift_xy = (0, 0)
+        self.undistort_maps = None
+        self.project_matrix = None
+        self.project_shape = settings.project_shapes[self.camera_name]
+        self.load_camera_params()
+
+    def load_camera_params(self):
+        fs = cv2.FileStorage(self.camera_file, cv2.FILE_STORAGE_READ)
+        self.camera_matrix = fs.getNode("camera_matrix").mat()
+        self.dist_coeffs = fs.getNode("dist_coeffs").mat()
+        self.resolution = fs.getNode("resolution").mat().flatten()
+
+        scale_xy = fs.getNode("scale_xy").mat()
+        if scale_xy is not None:
+            self.scale_xy = scale_xy
+
+        shift_xy = fs.getNode("shift_xy").mat()
+        if shift_xy is not None:
+            self.shift_xy = shift_xy
+
+        project_matrix = fs.getNode("project_matrix").mat()
+        if project_matrix is not None:
+            self.project_matrix = project_matrix
+
+        fs.release()
+        self.update_undistort_maps()
+
+    def update_undistort_maps(self):
+        new_matrix = self.camera_matrix.copy()
+        new_matrix[0, 0] *= self.scale_xy[0]
+        new_matrix[1, 1] *= self.scale_xy[1]
+        new_matrix[0, 2] += self.shift_xy[0]
+        new_matrix[1, 2] += self.shift_xy[1]
+        width, height = self.resolution
+
+        self.undistort_maps = cv2.fisheye.initUndistortRectifyMap(
+            self.camera_matrix,
+            self.dist_coeffs,
+            np.eye(3),
+            new_matrix,
+            (width, height),
+            cv2.CV_16SC2
+        )
+        return self
+
+    def set_scale_and_shift(self, scale_xy=(1.0, 1.0), shift_xy=(0, 0)):
+        self.scale_xy = scale_xy
+        self.shift_xy = shift_xy
+        self.update_undistort_maps()
+        return self
+
+    def undistort(self, image):
+        result = cv2.remap(image, *self.undistort_maps, interpolation=cv2.INTER_LINEAR,
+                           borderMode=cv2.BORDER_CONSTANT)
+        return result
+    # def undistort(self, image):
+    #     uimg = cv2.UMat(image)
+    #     uresult = cv2.remap(
+    #         uimg,
+    #         *self.undistort_maps,
+    #         interpolation=cv2.INTER_LINEAR,
+    #         borderMode=cv2.BORDER_CONSTANT,
+    #         borderValue=(0, 0, 0)
+    #     )
+    #     return uresult.get()    
+
+    # def project(self, image):
+    #     result = cv2.warpPerspective(image, self.project_matrix, self.project_shape)
+    #     return result
+
+    def project(self, image):
+        # 转为 UMat（触发 GPU 路径）
+        uimg = cv2.UMat(image)
+        uresult = cv2.warpPerspective(
+            uimg,
+            self.project_matrix,
+            self.project_shape,
+            flags=cv2.INTER_LINEAR,
+            borderMode=cv2.BORDER_CONSTANT,
+            borderValue=(0, 0, 0)
+        )
+        return uresult.get()  # 转回 numpy array 供后续使用
+
+    def flip(self, image):
+        if self.camera_name == "front":
+            return image.copy()
+
+        elif self.camera_name == "back":
+            return image.copy()[::-1, ::-1, :]
+
+        elif self.camera_name == "left":
+            return cv2.transpose(image)[::-1]
+
+        else:
+            return cv2.transpose(image)[:,::-1]
+
+    def save_data(self):
+        fs = cv2.FileStorage(self.camera_file, cv2.FILE_STORAGE_WRITE)
+        fs.write("camera_matrix", self.camera_matrix)
+        fs.write("dist_coeffs", self.dist_coeffs)
+        fs.write("resolution", self.resolution)
+        fs.write("project_matrix", self.project_matrix)
+        fs.write("scale_xy", np.float32(self.scale_xy))
+        fs.write("shift_xy", np.float32(self.shift_xy))
+        fs.release()

surround_view/imagebuffer.py

@@ -0,0 +1,161 @@
+from PyQt5.QtCore import QSemaphore, QMutex
+from PyQt5.QtCore import QMutexLocker, QWaitCondition
+from queue import Queue
+
+
+class Buffer(object):
+
+    def __init__(self, buffer_size=5):
+        self.buffer_size = buffer_size
+        self.free_slots = QSemaphore(self.buffer_size)
+        self.used_slots = QSemaphore(0)
+        self.clear_buffer_add = QSemaphore(1)
+        self.clear_buffer_get = QSemaphore(1)
+        self.queue_mutex = QMutex()
+        self.queue = Queue(self.buffer_size)
+
+    def add(self, data, drop_if_full=False):
+        self.clear_buffer_add.acquire()
+        if drop_if_full:
+            if self.free_slots.tryAcquire():
+                self.queue_mutex.lock()
+                self.queue.put(data)
+                self.queue_mutex.unlock()
+                self.used_slots.release()
+        else:
+            self.free_slots.acquire()
+            self.queue_mutex.lock()
+            self.queue.put(data)
+            self.queue_mutex.unlock()
+            self.used_slots.release()
+
+        self.clear_buffer_add.release()
+
+    def get(self):
+        # acquire semaphores
+        self.clear_buffer_get.acquire()
+        self.used_slots.acquire()
+        self.queue_mutex.lock()
+        data = self.queue.get()
+        self.queue_mutex.unlock()
+        # release semaphores
+        self.free_slots.release()
+        self.clear_buffer_get.release()
+        # return item to caller
+        return data
+
+    def clear(self):
+        # check if buffer contains items
+        if self.queue.qsize() > 0:
+            # stop adding items to buffer (will return false if an item is currently being added to the buffer)
+            if self.clear_buffer_add.tryAcquire():
+                # stop taking items from buffer (will return false if an item is currently being taken from the buffer)
+                if self.clear_buffer_get.tryAcquire():
+                    # release all remaining slots in queue
+                    self.free_slots.release(self.queue.qsize())
+                    # acquire all queue slots
+                    self.free_slots.acquire(self.buffer_size)
+                    # reset used_slots to zero
+                    self.used_slots.acquire(self.queue.qsize())
+                    # clear buffer
+                    for _ in range(self.queue.qsize()):
+                        self.queue.get()
+                    # release all slots
+                    self.free_slots.release(self.buffer_size)
+                    # allow get method to resume
+                    self.clear_buffer_get.release()
+                else:
+                    return False
+                # allow add method to resume
+                self.clear_buffer_add.release()
+                return True
+            else:
+                return False
+        else:
+            return False
+
+    def size(self):
+        return self.queue.qsize()
+
+    def maxsize(self):
+        return self.buffer_size
+
+    def isfull(self):
+        return self.queue.qsize() == self.buffer_size
+
+    def isempty(self):
+        return self.queue.qsize() == 0
+
+
+class MultiBufferManager(object):
+
+    """
+    Class for synchronizing capture threads from different cameras.
+    """
+
+    def __init__(self, do_sync=True):
+        self.sync_devices = set()
+        self.do_sync = do_sync
+        self.wc = QWaitCondition()
+        self.mutex = QMutex()
+        self.arrived = 0
+        self.buffer_maps = dict()
+
+    def bind_thread(self, thread, buffer_size, sync=True):
+        self.create_buffer_for_device(thread.device_id, buffer_size, sync)
+        thread.buffer_manager = self
+
+    def create_buffer_for_device(self, device_id, buffer_size, sync=True):
+        if sync:
+            with QMutexLocker(self.mutex):
+                self.sync_devices.add(device_id)
+
+        self.buffer_maps[device_id] = Buffer(buffer_size)
+
+    def get_device(self, device_id):
+        return self.buffer_maps[device_id]
+
+    def remove_device(self, device_id):
+        self.buffer_maps.pop(device_id)
+        with QMutexLocker(self.mutex):
+            if device_id in self.sync_devices:
+                self.sync_devices.remove(device_id)
+                self.wc.wakeAll()
+
+    def sync(self, device_id):
+        # only perform sync if enabled for specified device/stream
+        self.mutex.lock()
+        if device_id in self.sync_devices:
+            # increment arrived count
+            self.arrived += 1
+            # we are the last to arrive: wake all waiting threads
+            if self.do_sync and self.arrived == len(self.sync_devices):
+                self.wc.wakeAll()
+            # still waiting for other streams to arrive: wait
+            else:
+                self.wc.wait(self.mutex)
+            # decrement arrived count
+            self.arrived -= 1
+        self.mutex.unlock()
+
+    def wake_all(self):
+        with QMutexLocker(self.mutex):
+            self.wc.wakeAll()
+
+    def set_sync(self, enable):
+        self.do_sync = enable
+
+    def sync_enabled(self):
+        return self.do_sync
+
+    def sync_enabled_for_device(self, device_id):
+        return device_id in self.sync_devices
+
+    def __contains__(self, device_id):
+        return device_id in self.buffer_maps
+
+    def __str__(self):
+        return (self.__class__.__name__ + ":\n" + \
+                "sync: {}\n".format(self.do_sync) + \
+                "devices: {}\n".format(tuple(self.buffer_maps.keys())) + \
+                "sync enabled devices: {}".format(self.sync_devices))

surround_view/param_settings.py

@@ -0,0 +1,118 @@
+import os
+import cv2
+
+
+camera_names = ["front", "back", "left", "right"]
+
+# --------------------------------------------------------------------
+# (shift_width, shift_height): 鸟瞰图在水平和垂直方向上超出标定图案的距离
+shift_w = 100
+shift_h = 100
+
+
+# 标定图案与车辆之间在水平和垂直方向上的间隙大小
+inn_shift_w = 8
+inn_shift_h = 30
+
+# 拼接图像的总宽度/高度
+total_w = 260 + 2 * shift_w
+total_h = 350 + 2 * shift_h
+
+
+# 计算车辆在全景图中的位置
+xl = shift_w + 55 + inn_shift_w
+xr = total_w - xl
+print(xl, xr)
+yt = shift_h + 55 + inn_shift_h
+yb = total_h - yt
+# --------------------------------------------------------------------
+
+project_shapes = {
+    "front": (total_w, yt),
+    "back":  (total_w, yt),
+    "left":  (total_h, xl),
+    "right": (total_h, xl)
+}
+
+# 要选取的四个像素点的位置。
+# 运行 get_projection_map.py 脚本时，必须按相同顺序点击这些像素点。
+project_keypoints = {
+    "front": [(shift_w + 0, shift_h),
+              (shift_w + 260, shift_h),
+              (shift_w + 0, shift_h + 100),
+              (shift_w + 260, shift_h + 100)],
+
+    "back":  [(shift_w + 0, shift_h),
+              (shift_w + 260, shift_h),
+              (shift_w + 0, shift_h + 80),
+              (shift_w + 260, shift_h + 80)],
+
+    "left":  [(shift_h + 60, shift_w),
+              (shift_h + 300, shift_w),
+              (shift_h + 60, shift_w + 50),
+              (shift_h + 300, shift_w + 50)],
+
+    "right": [(shift_h + 80, shift_w),
+              (shift_h + 350, shift_w),
+              (shift_h + 80, shift_w + 50),
+              (shift_h + 350, shift_w + 50)]
+}
+
+# car_image = cv2.imread(os.path.join(os.getcwd(), "images", "car.png"))
+# car_image = cv2.resize(car_image, (xr - xl, yb - yt))
+
+# 放大系数（调整这个值控制车辆图大小，1.0 = 原始大小，1.3 = 放大30%）
+CAR_SCALE = 1.3
+
+car_w = xr - xl
+car_h = yb - yt
+
+# 放大后的车辆图尺寸
+car_display_w = int(car_w * CAR_SCALE)
+car_display_h = int(car_h * CAR_SCALE)
+
+# 放大后车辆图在全景图中的起始坐标（居中对齐）
+car_x = xl - (car_display_w - car_w) // 2
+car_y = yt - (car_display_h - car_h) // 2
+
+def _make_car_image_rgba(path, w, h, tolerance=30):
+    # 用 IMREAD_UNCHANGED 保留 PNG 透明通道
+    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+    
+    if img is None:
+        return np.zeros((h, w, 4), np.uint8)
+    
+    img = cv2.resize(img, (w, h))
+    
+    # 如果图片有 alpha 通道，直接用
+    if img.shape[2] == 4:
+        rgba = img.copy()
+    else:
+        # 没有 alpha 通道，白色转透明
+        rgba = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
+        white_mask = (
+            (rgba[:, :, 0] >= 255 - tolerance) &
+            (rgba[:, :, 1] >= 255 - tolerance) &
+            (rgba[:, :, 2] >= 255 - tolerance)
+        )
+        rgba[white_mask, 3] = 0
+    
+    # 边缘平滑
+    alpha = rgba[:, :, 3].astype(np.uint8)
+    alpha = cv2.GaussianBlur(alpha, (5, 5), 0)
+    rgba[:, :, 3] = alpha
+    
+    return rgba
+
+import numpy as np
+
+car_image_path = os.path.join(os.getcwd(), "images", "car.png")
+
+# 原始尺寸（保持兼容）
+car_image = cv2.imread(car_image_path)
+if car_image is not None:
+    car_image = cv2.resize(car_image, (car_w, car_h))
+
+# 放大版带透明通道
+car_image_rgba = _make_car_image_rgba(car_image_path, car_display_w, car_display_h)
+

surround_view/process_thread.py

@@ -0,0 +1,81 @@
+import cv2
+from PyQt5.QtCore import qDebug, QMutex
+
+from .base_thread import BaseThread
+from . import param_settings as settings
+
+
+class CameraProcessingThread(BaseThread):
+
+    """
+    Thread for processing individual camera images, i.e. undistort, project and flip.
+    """
+
+    def __init__(self,
+                 capture_buffer_manager,
+                 device_id,
+                 camera_model,
+                 drop_if_full=True,
+                 parent=None):
+        """
+        capture_buffer_manager: an instance of the `MultiBufferManager` object.
+        device_id: device number of the camera to be processed.
+        camera_model: an instance of the `FisheyeCameraModel` object.
+        drop_if_full: drop if the buffer is full.
+        """
+        super(CameraProcessingThread, self).__init__(parent)
+        self.capture_buffer_manager = capture_buffer_manager
+        self.device_id = device_id
+        self.camera_model = camera_model
+        self.drop_if_full = drop_if_full
+        # an instance of the `ProjectedImageBuffer` object
+        self.proc_buffer_manager = None
+
+    def run(self):
+        if self.proc_buffer_manager is None:
+            raise ValueError("This thread has not been binded to any processing thread yet")
+
+        while True:
+            self.stop_mutex.lock()
+            if self.stopped:
+                self.stopped = False
+                self.stop_mutex.unlock()
+                break
+            self.stop_mutex.unlock()
+
+            self.processing_time = self.clock.elapsed()
+            self.clock.start()
+
+            self.processing_mutex.lock()
+            raw_frame = self.capture_buffer_manager.get_device(self.device_id).get()
+            
+            # Validate frame before processing
+            if raw_frame.image is None or raw_frame.image.size == 0:
+                self.processing_mutex.unlock()
+                continue
+                
+            und_frame = self.camera_model.undistort(raw_frame.image)
+            pro_frame = self.camera_model.project(und_frame)
+            flip_frame = self.camera_model.flip(pro_frame)
+            self.processing_mutex.unlock()
+            
+            # Check if the processed frame has valid dimensions
+            name = self.camera_model.camera_name
+            if name in settings.project_shapes:
+                # For left and right cameras, the flip operation changes the shape
+                if name in ['left', 'right']:
+                    expected_shape = settings.project_shapes[name] + (3,)
+                else:
+                    expected_shape = settings.project_shapes[name][::-1] + (3,)
+                if flip_frame.shape != expected_shape:
+                    print(f"Warning: {name} camera frame has unexpected shape {flip_frame.shape}, expected {expected_shape}")
+                    continue
+
+            self.proc_buffer_manager.sync(self.device_id)
+            self.proc_buffer_manager.set_frame_for_device(self.device_id, flip_frame)
+
+            # update statistics
+            self.update_fps(self.processing_time)
+            self.stat_data.frames_processed_count += 1
+            # inform GUI of updated statistics
+            self.update_statistics_gui.emit(self.stat_data)

surround_view/simple_gui.py

@@ -0,0 +1,131 @@
+import cv2
+import numpy as np
+
+# return -1 if user press 'q'. return 1 if user press 'Enter'.
+def display_image(window_title, image):
+    cv2.imshow(window_title, image)
+    while True:
+        click = cv2.getWindowProperty(window_title, cv2.WND_PROP_AUTOSIZE)
+        if click < 0:
+            return -1
+
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord("q"):
+            return -1
+
+        # 'Enter' key is detected!
+        if key == 13:
+            return 1
+
+
+class PointSelector(object):
+
+    """
+    ---------------------------------------------------
+    | A simple gui point selector.                    |
+    | Usage:                                          |
+    |                                                 |
+    | 1. call the `loop` method to show the image.    |
+    | 2. click on the image to select key points,     |
+    |    press `d` to delete the last points.         |
+    | 3. press `q` to quit, press `Enter` to confirm. |
+    ---------------------------------------------------
+    """
+
+    POINT_COLOR = (0, 0, 255)
+    FILL_COLOR = (0, 255, 255)
+
+    def __init__(self, image, title="PointSelector"):
+        self.image = image
+        self.title = title
+        self.keypoints = []
+
+    def draw_image(self):
+        """
+        Display the selected keypoints and draw the convex hull.
+        """
+        # the trick: draw on another new image
+        new_image = self.image.copy()
+
+        # draw the selected keypoints
+        for i, pt in enumerate(self.keypoints):
+            cv2.circle(new_image, pt, 6, self.POINT_COLOR, -1)
+            cv2.putText(new_image, str(i), (pt[0], pt[1] - 15),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, self.POINT_COLOR, 2)
+
+        # draw a line if there are two points
+        if len(self.keypoints) == 2:
+            p1, p2 = self.keypoints
+            cv2.line(new_image, p1, p2, self.POINT_COLOR, 2)
+
+        # draw the convex hull if there are more than two points
+        if len(self.keypoints) > 2:
+            mask = self.create_mask_from_pixels(self.keypoints,
+                                                self.image.shape)
+            new_image = self.draw_mask_on_image(new_image, mask)
+
+        cv2.imshow(self.title, new_image)
+
+    def onclick(self, event, x, y, flags, param):
+        """
+        Click on a point (x, y) will add this points to the list
+        and re-draw the image.
+        """
+        if event == cv2.EVENT_LBUTTONDOWN:
+            print("click ({}, {})".format(x, y))
+            self.keypoints.append((x, y))
+            self.draw_image()
+
+    def loop(self):
+        """
+        Press "q" will exist the gui and return False
+        press "d" will delete the last selected point.
+        Press "Enter" will exist the gui and return True.
+        """
+        cv2.namedWindow(self.title)
+        cv2.setMouseCallback(self.title, self.onclick, param=())
+        cv2.imshow(self.title, self.image)
+
+        while True:
+            click = cv2.getWindowProperty(self.title, cv2.WND_PROP_AUTOSIZE)
+            if click < 0:
+                return False
+
+            key = cv2.waitKey(1) & 0xFF
+
+            # press q to return False
+            if key == ord("q"):
+                return False
+
+            # press d to delete the last point
+            if key == ord("d"):
+                if len(self.keypoints) > 0:
+                    x, y = self.keypoints.pop()
+                    print("Delete ({}, {})".format(x, y))
+                    self.draw_image()
+
+            # press Enter to confirm
+            if key == 13:
+                return True
+
+    def create_mask_from_pixels(self, pixels, image_shape):
+        """
+        Create mask from the convex hull of a list of pixels.
+        """
+        pixels = np.int32(pixels).reshape(-1, 2)
+        hull = cv2.convexHull(pixels)
+        mask = np.zeros(image_shape[:2], np.int8)
+        cv2.fillConvexPoly(mask, hull, 1, lineType=8, shift=0)
+        mask = mask.astype(bool)
+        return mask
+
+    def draw_mask_on_image(self, image, mask):
+        """
+        Paint the region defined by a given mask on an image.
+        """
+        new_image = np.zeros_like(image)
+        new_image[:, :] = self.FILL_COLOR
+        mask = np.array(mask, dtype=np.uint8)
+        new_mask = cv2.bitwise_and(new_image, new_image, mask=mask)
+        cv2.addWeighted(image, 1.0, new_mask, 0.5, 0.0, image)
+        return image

surround_view/structures.py

@@ -0,0 +1,12 @@
+class ImageFrame(object):
+
+    def __init__(self, timestamp, image):
+        self.timestamp = timestamp
+        self.image = image
+
+
+class ThreadStatisticsData(object):
+
+    def __init__(self):
+        self.average_fps = 0
+        self.frames_processed_count = 0

surround_view/utils.py

@@ -0,0 +1,137 @@
+import cv2
+import numpy as np
+
+
+def gstreamer_pipeline(cam_id=0,
+                       capture_width=1920,
+                       capture_height=1080,
+                       framerate=30,
+                       format="YUYV",
+                       ):
+    print("11111111233333333333545656646464646")
+    """
+    Use libgstreamer to open csi-cameras.
+    """
+    return (
+        f"v4l2src device=/dev/video{cam_id} ! "
+        f"video/x-raw,format={format},width={capture_width},height={capture_height},framerate={framerate}/1 ! "
+        "videoconvert ! "
+        "video/x-raw,format=YUYV ! "   # 转为 OpenCV 能直接用的 BGR 格式
+        "appsink"
+    )
+
+
+def convert_binary_to_bool(mask):
+    """
+    Convert a binary image (only one channel and pixels are 0 or 255) to
+    a bool one (all pixels are 0 or 1).
+    """
+    return (mask.astype(np.float) / 255.0).astype(int)
+
+
+def adjust_luminance(gray, factor):
+    """
+    Adjust the luminance of a grayscale image by a factor.
+    """
+    return np.minimum((gray * factor), 255).astype(np.uint8)
+
+
+def get_mean_statistisc(gray, mask):
+    """
+    Get the total values of a gray image in a region defined by a mask matrix.
+    The mask matrix must have values either 0 or 1.
+    """
+    return np.sum(gray * mask)
+
+
+def mean_luminance_ratio(grayA, grayB, mask):
+    return get_mean_statistisc(grayA, mask) / get_mean_statistisc(grayB, mask)
+
+
+def get_mask(img):
+    """
+    Convert an image to a mask array.
+    """
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    ret, mask = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
+    return mask
+
+
+def get_overlap_region_mask(imA, imB):
+    """
+    Given two images of the save size, get their overlapping region and
+    convert this region to a mask array.
+    """
+    overlap = cv2.bitwise_and(imA, imB)
+    mask = get_mask(overlap)
+    mask = cv2.dilate(mask, np.ones((2, 2), np.uint8), iterations=2)
+    return mask
+
+
+def get_outmost_polygon_boundary(img):
+    """
+    Given a mask image with the mask describes the overlapping region of
+    two images, get the outmost contour of this region.
+    """
+    mask = get_mask(img)
+    mask = cv2.dilate(mask, np.ones((2, 2), np.uint8), iterations=2)
+    cnts, hierarchy = cv2.findContours(
+        mask,
+        cv2.RETR_EXTERNAL,
+        cv2.CHAIN_APPROX_SIMPLE)[-2:]
+
+    # get the contour with largest aera
+    C = sorted(cnts, key=lambda x: cv2.contourArea(x), reverse=True)[0]
+
+    # polygon approximation
+    polygon = cv2.approxPolyDP(C, 0.009 * cv2.arcLength(C, True), True)
+
+    return polygon
+
+
+def get_weight_mask_matrix(imA, imB, dist_threshold=5):
+    """
+    Get the weight matrix G that combines two images imA, imB smoothly.
+    """
+    overlapMask = get_overlap_region_mask(imA, imB)
+    overlapMaskInv = cv2.bitwise_not(overlapMask)
+    indices = np.where(overlapMask == 255)
+
+    imA_diff = cv2.bitwise_and(imA, imA, mask=overlapMaskInv)
+    imB_diff = cv2.bitwise_and(imB, imB, mask=overlapMaskInv)
+
+    G = get_mask(imA).astype(np.float32) / 255.0
+
+    polyA = get_outmost_polygon_boundary(imA_diff)
+    polyB = get_outmost_polygon_boundary(imB_diff)
+
+    for y, x in zip(*indices):
+        # opencv requires a tuple of ints
+        xy_tuple = tuple([int(x), int(y)])
+        distToB = cv2.pointPolygonTest(polyB, xy_tuple, True)
+
+        if distToB < dist_threshold:
+            distToA = cv2.pointPolygonTest(polyA, xy_tuple, True)
+            distToB *= distToB
+            distToA *= distToA
+            G[y, x] = distToB / (distToA + distToB)
+
+    return G, overlapMask
+
+
+def make_white_balance(image):
+    """
+    Adjust white balance of an image base on the means of its channels.
+    """
+    B, G, R = cv2.split(image)
+    m1 = np.mean(B)
+    m2 = np.mean(G)
+    m3 = np.mean(R)
+    K = (m1 + m2 + m3) / 3
+    c1 = K / m1
+    c2 = K / m2
+    c3 = K / m3
+    B = adjust_luminance(B, c1)
+    G = adjust_luminance(G, c2)
+    R = adjust_luminance(R, c3)
+    return cv2.merge((B, G, R))