yolov4直接调用zed相机实现三维测距（免标定）

此项目直接调用zed相机实现三维测距，无需标定，相关内容如下：
1. yolov5 + 双目测距（标定测距）
2. yolov5直接调用zed相机实现三维测距
3. 具体实现效果已在哔哩哔哩发布，点击此链接跳转

相关配置
python==3.7
Windows系统
pycharm平台
zed api（zed api配置步骤）

1.首先加载模型

    config_path = "yolov4-tiny.cfg"weight_path = "yolov4-tiny.weights"meta_path = "coco.names"svo_path = Nonezed_id = 0help_str = 'zed_yolo.py -c <config> -w <weight> -m <meta> -s <svo_file> -z <zed_id>'try:opts, args = getopt.getopt(argv, "hc:w:m:s:z:", ["config=", "weight=", "meta=", "svo_file=", "zed_id="])except getopt.GetoptError:log.exception(help_str)sys.exit(2)for opt, arg in opts:if opt == '-h':log.info(help_str)sys.exit()elif opt in ("-c", "--config"):config_path = argelif opt in ("-w", "--weight"):weight_path = argelif opt in ("-m", "--meta"):meta_path = argelif opt in ("-s", "--svo_file"):svo_path = argelif opt in ("-z", "--zed_id"):zed_id = int(arg)weightsPath_tiny = weight_pathconfigPath_tiny = config_pathnet = cv2.dnn.readNet(weightsPath_tiny, configPath_tiny)net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA_FP16)model = cv2.dnn_DetectionModel(net)

2.打开zed相机并配置

zed = sl.Camera()# Set configuration parametersinput_type = sl.InputType()init = sl.InitParameters(input_t=input_type)init.camera_resolution = sl.RESOLUTION.HD720init.depth_mode = sl.DEPTH_MODE.PERFORMANCEinit.coordinate_units = sl.UNIT.MILLIMETER# Open the cameraerr = zed.open(init)if err != sl.ERROR_CODE.SUCCESS:print(repr(err))zed.close()exit(1)# Set runtime parameters after opening the cameraruntime = sl.RuntimeParameters()runtime.sensing_mode = sl.SENSING_MODE.STANDARD# Prepare new image size to retrieve half-resolution imagesimage_size = zed.get_camera_information().camera_resolutionimage_size.width = image_size.widthimage_size.height = image_size.height# Declare your sl.Mat matricesimage_zed = sl.Mat(image_size.width, image_size.height, sl.MAT_TYPE.U8_C4)disparity = sl.Mat()  # 视差值dep = sl.Mat()  # 深度图depth_image_zed = sl.Mat(image_size.width, image_size.height, sl.MAT_TYPE.U8_C4)point_cloud = sl.Mat()

3.进行图像处理

    def YOLOv4_video(pred_image):model.setInputParams(size=(416, 416), scale=1 / 255, swapRB=True)image_test = cv2.cvtColor(pred_image, cv2.COLOR_RGBA2RGB)image = image_test.copy()print('image', image.shape)confThreshold = 0.5nmsThreshold = 0.4classes, confidences, boxes = model.detect(image, confThreshold, nmsThreshold)return classes, confidences, boxeswhile (exit_flag == True):err = zed.grab(runtime)if err == sl.ERROR_CODE.SUCCESS:i = 0# Retrieve the left image, depth image in the half-resolutionzed.retrieve_image(image_zed, sl.VIEW.LEFT, sl.MEM.CPU, image_size)zed.retrieve_image(depth_image_zed, sl.VIEW.DEPTH, sl.MEM.CPU, image_size)# 获取视差值zed.retrieve_measure(disparity, sl.MEASURE.DISPARITY, sl.MEM.CPU)dis_map = disparity.get_data()zed.retrieve_image(dep, sl.VIEW.DEPTH)  # 深度图depth_map = depth_image_zed.get_data()dep_map = dep.get_data()# Retrieve the RGBA point cloud in half resolutionzed.retrieve_measure(point_cloud, sl.MEASURE.XYZRGBA, sl.MEM.CPU, image_size)point_map = point_cloud.get_data()# Get and print distance value in mm at the center of the image# We measure the distance camera - object using Euclidean distance# To recover data from sl.Mat to use it with opencv, use the get_data() method# It returns a numpy array that can be used as a matrix with opencvimage_ocv = image_zed.get_data()# depth_image_ocv = depth_image_zed.get_data()view = np.concatenate((cv2.resize(image_ocv, (640, 360)), cv2.resize(dep_map, (640, 360))), axis=1)cv2.imshow("View", view)key = cv2.waitKey(1)if key & 0xFF == 27:  # esc退出breakif key & 0xFF == ord('s'):  # 图像保存savePath = os.path.join("./images", "V{:0>3d}.png".format(i))  # 注意根目录是否存在"./images"文件夹cv2.imwrite(savePath, view)i = i + 1classes, confidences, boxes = YOLOv4_video(image_ocv)for cl, score, (left, top, width, height) in zip(classes, confidences, boxes):start_pooint = (int(left), int(top))end_point = (int(left + width), int(top + height))x = int(left + width / 2)y = int(top + height / 2)color = COLORS[0]img = cv2.rectangle(image_ocv, start_pooint, end_point, color, 3)img = cv2.circle(img, (x, y), 5, [0, 0, 255], 5)text = f'{LABELS[cl]}: {score:0.2f}'cv2.putText(img, text, (int(left), int(top - 7)), cv2.FONT_ITALIC, 1, COLORS[0], 2)x = round(x)y = round(y)err, point_cloud_value = point_cloud.get_value(x, y)distance = math.sqrt(point_cloud_value[0] * point_cloud_value[0] + point_cloud_value[1] * point_cloud_value[1] +point_cloud_value[2] * point_cloud_value[2])print("Distance to Camera at (class : {0}, score : {1:0.2f}): distance : {2:0.2f} mm".format(LABELS[cl],score,distance),end="\r")cv2.putText(img, "Distance: " + str(round(distance / 1000, 2)) + 'm',(int(left), int(top + 25)),cv2.FONT_HERSHEY_COMPLEX, 1, COLORS[1], 2)cv2.imshow("Image", img)key = cv2.waitKey(2)frame_count = frame_count + 1if key & 0xFF == 27:  # esc退出breakif key & 0xFF == ord('s'):  # 图像保存savePath = os.path.join("./images", "V{:0>3d}.png".format(i))  # 注意根目录是否存在"./images"文件夹cv2.imwrite(savePath, view)i = i + 1cv2.destroyAllWindows()zed.close()

完整代码

import os
import sys
import numpy as np
import pyzed.sl as sl
import cv2
import math
import logging
import getoptlog = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)def main(argv):global imgconfig_path = "yolov4-tiny.cfg"weight_path = "yolov4-tiny.weights"meta_path = "coco.names"svo_path = Nonezed_id = 0help_str = 'zed_yolo.py -c <config> -w <weight> -m <meta> -s <svo_file> -z <zed_id>'try:opts, args = getopt.getopt(argv, "hc:w:m:s:z:", ["config=", "weight=", "meta=", "svo_file=", "zed_id="])except getopt.GetoptError:log.exception(help_str)sys.exit(2)for opt, arg in opts:if opt == '-h':log.info(help_str)sys.exit()elif opt in ("-c", "--config"):config_path = argelif opt in ("-w", "--weight"):weight_path = argelif opt in ("-m", "--meta"):meta_path = argelif opt in ("-s", "--svo_file"):svo_path = argelif opt in ("-z", "--zed_id"):zed_id = int(arg)# Set configuration parametersinput_type = sl.InputType()if svo_path is not None:log.info("SVO file : " + svo_path)input_type.set_from_svo_file(svo_path)else:# Launch camera by idinput_type.set_from_camera_id(zed_id)# Create a ZED camera objectzed = sl.Camera()# Set configuration parametersinput_type = sl.InputType()init = sl.InitParameters(input_t=input_type)init.camera_resolution = sl.RESOLUTION.HD720init.depth_mode = sl.DEPTH_MODE.PERFORMANCEinit.coordinate_units = sl.UNIT.MILLIMETER# Open the cameraerr = zed.open(init)if err != sl.ERROR_CODE.SUCCESS:print(repr(err))zed.close()exit(1)# Set runtime parameters after opening the cameraruntime = sl.RuntimeParameters()runtime.sensing_mode = sl.SENSING_MODE.STANDARD# Prepare new image size to retrieve half-resolution imagesimage_size = zed.get_camera_information().camera_resolutionimage_size.width = image_size.widthimage_size.height = image_size.height# Declare your sl.Mat matricesimage_zed = sl.Mat(image_size.width, image_size.height, sl.MAT_TYPE.U8_C4)disparity = sl.Mat()  # 视差值dep = sl.Mat()  # 深度图depth_image_zed = sl.Mat(image_size.width, image_size.height, sl.MAT_TYPE.U8_C4)point_cloud = sl.Mat()# =======================================  yolov4  video test et ============================================weightsPath_tiny = weight_pathconfigPath_tiny = config_pathnet = cv2.dnn.readNet(weightsPath_tiny, configPath_tiny)net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA_FP16)model = cv2.dnn_DetectionModel(net)def YOLOv4_video(pred_image):model.setInputParams(size=(416, 416), scale=1 / 255, swapRB=True)image_test = cv2.cvtColor(pred_image, cv2.COLOR_RGBA2RGB)image = image_test.copy()print('image', image.shape)confThreshold = 0.5nmsThreshold = 0.4classes, confidences, boxes = model.detect(image, confThreshold, nmsThreshold)return classes, confidences, boxesLABELS = []with open(meta_path, 'r') as f:LABELS = [cname.strip() for cname in f.readlines()]COLORS = [[0, 0, 255], [30, 255, 255], [0, 255, 0]]frame_count = 0exit_flag = Truewhile (exit_flag == True):err = zed.grab(runtime)if err == sl.ERROR_CODE.SUCCESS:i = 0# Retrieve the left image, depth image in the half-resolutionzed.retrieve_image(image_zed, sl.VIEW.LEFT, sl.MEM.CPU, image_size)zed.retrieve_image(depth_image_zed, sl.VIEW.DEPTH, sl.MEM.CPU, image_size)# 获取视差值zed.retrieve_measure(disparity, sl.MEASURE.DISPARITY, sl.MEM.CPU)dis_map = disparity.get_data()zed.retrieve_image(dep, sl.VIEW.DEPTH)  # 深度图depth_map = depth_image_zed.get_data()dep_map = dep.get_data()# Retrieve the RGBA point cloud in half resolutionzed.retrieve_measure(point_cloud, sl.MEASURE.XYZRGBA, sl.MEM.CPU, image_size)point_map = point_cloud.get_data()image_ocv = image_zed.get_data()# depth_image_ocv = depth_image_zed.get_data()view = np.concatenate((cv2.resize(image_ocv, (640, 360)), cv2.resize(dep_map, (640, 360))), axis=1)cv2.imshow("View", view)key = cv2.waitKey(1)if key & 0xFF == 27:  # esc退出breakif key & 0xFF == ord('s'):  # 图像保存savePath = os.path.join("./images", "V{:0>3d}.png".format(i))  # 注意根目录是否存在"./images"文件夹cv2.imwrite(savePath, view)i = i + 1classes, confidences, boxes = YOLOv4_video(image_ocv)for cl, score, (left, top, width, height) in zip(classes, confidences, boxes):start_pooint = (int(left), int(top))end_point = (int(left + width), int(top + height))x = int(left + width / 2)y = int(top + height / 2)color = COLORS[0]img = cv2.rectangle(image_ocv, start_pooint, end_point, color, 3)img = cv2.circle(img, (x, y), 5, [0, 0, 255], 5)text = f'{LABELS[cl]}: {score:0.2f}'cv2.putText(img, text, (int(left), int(top - 7)), cv2.FONT_ITALIC, 1, COLORS[0], 2)x = round(x)y = round(y)err, point_cloud_value = point_cloud.get_value(x, y)distance = math.sqrt(point_cloud_value[0] * point_cloud_value[0] + point_cloud_value[1] * point_cloud_value[1] +point_cloud_value[2] * point_cloud_value[2])print("Distance to Camera at (class : {0}, score : {1:0.2f}): distance : {2:0.2f} mm".format(LABELS[cl],score,distance),end="\r")cv2.putText(img, "Distance: " + str(round(distance / 1000, 2)) + 'm',(int(left), int(top + 25)),cv2.FONT_HERSHEY_COMPLEX, 1, COLORS[1], 2)cv2.imshow("Image", img)key = cv2.waitKey(2)frame_count = frame_count + 1if key & 0xFF == 27:  # esc退出breakif key & 0xFF == ord('s'):  # 图像保存savePath = os.path.join("./images", "V{:0>3d}.png".format(i))  # 注意根目录是否存在"./images"文件夹cv2.imwrite(savePath, view)i = i + 1cv2.destroyAllWindows()zed.close()print("\nFINISH")if __name__ == "__main__":main(sys.argv[1:])

测距图片挺准确的，因为免去了棋盘格标定，直接调用的zed内部参数，具体效果图看深度图可以看出来，可以对比标定和未标定的效果

深度图（将左相机画面和深度图做了拼接）

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