对于python调节音量的问题，一般都是直接使用pycaw库进行调节，但是当我们要设定电脑音量的时候，不能实现精确映射（我想让我的电脑音量是40，不能直接输入40），但是由于内部确实没有精确的关系，只能用一对一映射的方式。详情请参照：
python-使用pycaw设置电脑音量（包含转换）_独憩的博客-CSDN博客
手部识别可以通过mediapipe库进行：
python-OpenCV 视频中的手部跟踪： 基于mediapipe库_独憩的博客-CSDN博客
对于调用视频识别手势进行电脑音量调节这个问题，网上有很多教程，一般是直接测量两个手指的距离进行映射，这样的问题是：我改变了自身位置就很难进行控制。故我本次对其进行改进：即先进行标定，标定的目的是获取此时的两个手指直接的最大距离，以这个maxlengh为基础，进行映射。
import cv2
import math
import time
import mediapipe as mp
from os import listdir
from datetime import datetime
import time
import datetime
from ctypes import cast, POINTER
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolumemp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
devices = AudioUtilities.GetSpeakers()
interface = devices.Activate(
IAudioEndpointVolume._iid_, CLSCTX_ALL, None)
volume = cast(interface, POINTER(IAudioEndpointVolume))
volRange = volume.GetVolumeRange()
这个函数的作用是将识别到的手的点映射到图像坐标上，为后期的画点及计算距离服务。
def Normalize_landmarks(image, hand_landmarks):
new_landmarks = []
for i in range(0, len(hand_landmarks.landmark)):
float_x = hand_landmarks.landmark[i].x
float_y = hand_landmarks.landmark[i].y
width = image.shape[1]
height = image.shape[0]
pt = mp_drawing._normalized_to_pixel_coordinates(float_x, float_y, width, height)
new_landmarks.append(pt)
return new_landmarks这个函数是画图函数，将食指与拇指的位置单独画出，并连线，至于为什么是landmarks[4]与landmarks[8]，请参照python-OpenCV 视频中的手部跟踪： 基于mediapipe库_独憩的博客-CSDN博客
def Draw_hand_points(image, normalized_hand_landmarks):
cv2.circle(image, normalized_hand_landmarks[4], 12, (255, 0, 255), -1, cv2.LINE_AA)
cv2.circle(image, normalized_hand_landmarks[8], 12, (255, 0, 255), -1, cv2.LINE_AA)
cv2.line(image, normalized_hand_landmarks[4], normalized_hand_landmarks[8], (255, 0, 255), 3)
x1, y1 = normalized_hand_landmarks[4][0], normalized_hand_landmarks[4][1]
x2, y2 = normalized_hand_landmarks[8][0], normalized_hand_landmarks[8][1]
mid_x, mid_y = (x1 + x2) // 2, (y1 + y2) // 2
length = math.sqrt((x2 - x1)**2+(y2 - y1)**2) #得到大拇指到食指的距离
if length < 100:
cv2.circle(image, (mid_x, mid_y), 12, (0, 255, 0), cv2.FILLED)
else:
cv2.circle(image, (mid_x, mid_y), 12, (255, 0, 255), cv2.FILLED)
return image, length
这两个函数的作用是将电脑的音量数字（0-100）与pycaw库中的数字对应，很蠢但是很有效，由于反向对应做不到11对应，只能找到误差最小的点进行对应：
def vol_tansfer(x):
dict = {0: -65.25, 1: -56.99, 2: -51.67, 3: -47.74, 4: -44.62, 5: -42.03, 6: -39.82, 7: -37.89, 8: -36.17,
9: -34.63, 10: -33.24,
11: -31.96, 12: -30.78, 13: -29.68, 14: -28.66, 15: -27.7, 16: -26.8, 17: -25.95, 18: -25.15, 19: -24.38,
20: -23.65,
21: -22.96, 22: -22.3, 23: -21.66, 24: -21.05, 25: -20.46, 26: -19.9, 27: -19.35, 28: -18.82, 29: -18.32,
30: -17.82,
31: -17.35, 32: -16.88, 33: -16.44, 34: -16.0, 35: -15.58, 36: -15.16, 37: -14.76, 38: -14.37, 39: -13.99,
40: -13.62,
41: -13.26, 42: -12.9, 43: -12.56, 44: -12.22, 45: -11.89, 46: -11.56, 47: -11.24, 48: -10.93, 49: -10.63,
50: -10.33,
51: -10.04, 52: -9.75, 53: -9.47, 54: -9.19, 55: -8.92, 56: -8.65, 57: -8.39, 58: -8.13, 59: -7.88,
60: -7.63,
61: -7.38, 62: -7.14, 63: -6.9, 64: -6.67, 65: -6.44, 66: -6.21, 67: -5.99, 68: -5.76, 69: -5.55, 70: -5.33,
71: -5.12, 72: -4.91, 73: -4.71, 74: -4.5, 75: -4.3, 76: -4.11, 77: -3.91, 78: -3.72, 79: -3.53, 80: -3.34,
81: -3.15, 82: -2.97, 83: -2.79, 84: -2.61, 85: -2.43, 86: -2.26, 87: -2.09, 88: -1.91, 89: -1.75,
90: -1.58,
91: -1.41, 92: -1.25, 93: -1.09, 94: -0.93, 95: -0.77, 96: -0.61, 97: -0.46, 98: -0.3, 99: -0.15, 100: 0.0}
return dict[x]
def vol_tansfer_reverse(x):
error = []
dict = {0: -65.25, 1: -56.99, 2: -51.67, 3: -47.74, 4: -44.62, 5: -42.03, 6: -39.82, 7: -37.89, 8: -36.17,
9: -34.63, 10: -33.24,
11: -31.96, 12: -30.78, 13: -29.68, 14: -28.66, 15: -27.7, 16: -26.8, 17: -25.95, 18: -25.15, 19: -24.38,
20: -23.65,
21: -22.96, 22: -22.3, 23: -21.66, 24: -21.05, 25: -20.46, 26: -19.9, 27: -19.35, 28: -18.82, 29: -18.32,
30: -17.82,
31: -17.35, 32: -16.88, 33: -16.44, 34: -16.0, 35: -15.58, 36: -15.16, 37: -14.76, 38: -14.37, 39: -13.99,
40: -13.62,
41: -13.26, 42: -12.9, 43: -12.56, 44: -12.22, 45: -11.89, 46: -11.56, 47: -11.24, 48: -10.93, 49: -10.63,
50: -10.33,
51: -10.04, 52: -9.75, 53: -9.47, 54: -9.19, 55: -8.92, 56: -8.65, 57: -8.39, 58: -8.13, 59: -7.88,
60: -7.63,
61: -7.38, 62: -7.14, 63: -6.9, 64: -6.67, 65: -6.44, 66: -6.21, 67: -5.99, 68: -5.76, 69: -5.55, 70: -5.33,
71: -5.12, 72: -4.91, 73: -4.71, 74: -4.5, 75: -4.3, 76: -4.11, 77: -3.91, 78: -3.72, 79: -3.53, 80: -3.34,
81: -3.15, 82: -2.97, 83: -2.79, 84: -2.61, 85: -2.43, 86: -2.26, 87: -2.09, 88: -1.91, 89: -1.75,
90: -1.58,
91: -1.41, 92: -1.25, 93: -1.09, 94: -0.93, 95: -0.77, 96: -0.61, 97: -0.46, 98: -0.3, 99: -0.15, 100: 0.0}
for i in range (100):
error.append(abs(dict[i]-x))
return error.index(min(error))主要的逻辑是在大循环下设置两个小循环，第一个循环是标定循环，持续5秒，可以得到5秒内的len_max。以此为依据映射到电脑音量（0-100）：
vol = int((length) / len_max * 100)hands = mp_hands.Hands(
min_detection_confidence=0.5, min_tracking_confidence=0.5)
cap = cv2.VideoCapture(0)
len_max = 0
len_min = 0
num = 0
while cap.isOpened():
stop = datetime.datetime.now() + datetime.timedelta(seconds=5)
if num == 0:
while datetime.datetime.now() < stop:
success, image = cap.read()
if not success:
print("camera frame is empty!")
continue
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
image.flags.writeable = False
results = hands.process(image)
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
mp_drawing.draw_landmarks(
image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
normalized_landmarks = Normalize_landmarks(image, hand_landmarks)
image, length = Draw_hand_points(image, normalized_landmarks)
if length>len_max:
len_max = length
strRate = 'Start calibration'
cv2.putText(image, strRate, (10, 410), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 0, 0), 2)
strRate1 = 'max length = %d'%len_max
cv2.putText(image, strRate1, (10, 110), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 0, 0), 2)
cv2.imshow('result', image)
if cv2.waitKey(5) & 0xFF == 27:
break
num = 1
success, image = cap.read()
if not success:
print("camera frame is empty!")
continue
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
image.flags.writeable = False
results = hands.process(image)
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
mp_drawing.draw_landmarks(
image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
normalized_landmarks = Normalize_landmarks(image, hand_landmarks)
try:
image, length = Draw_hand_points(image, normalized_landmarks)
# print(length) #20~300
cv2.rectangle(image, (50, 150), (85, 350), (255, 0, 0), 1)
if length >len_max:
length = len_max
vol = int((length) / len_max * 100)
volume.SetMasterVolumeLevel(vol_tansfer(vol), None)
cv2.rectangle(image, (50, 150+200-2*vol), (85, 350), (255, 0, 0), cv2.FILLED)
percent = int(length / len_max * 100)
# print(percent)
strRate = str(percent) + '%'
cv2.putText(image, strRate, (40, 410), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 0, 0), 2)
vol_now = vol_tansfer_reverse(volume.GetMasterVolumeLevel())
strvol = 'Current volume is'+str(vol_now)
cv2.putText(image, strvol, (10, 470), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 0, 0), 2)
except:
pass
cv2.imshow('result', image)
if cv2.waitKey(10) & 0xFF == ord('q'):
len_max = 0
num = 0
if cv2.waitKey(10) & 0xFF == 27:
break
cv2.destroyAllWindows()
hands.close()
cap.release()最后，敲入"q"可以重新标定，敲入"esc"可以退出。
import cv2
import math
import time
import mediapipe as mp
from os import listdir
from datetime import datetime
import time
import datetime
from ctypes import cast, POINTER
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
devices = AudioUtilities.GetSpeakers()
interface = devices.Activate(
IAudioEndpointVolume._iid_, CLSCTX_ALL, None)
volume = cast(interface, POINTER(IAudioEndpointVolume))
volRange = volume.GetVolumeRange()
minVol = volRange[0]
maxVol = volRange[1]
print(minVol, maxVol)
def Normalize_landmarks(image, hand_landmarks):
new_landmarks = []
for i in range(0, len(hand_landmarks.landmark)):
float_x = hand_landmarks.landmark[i].x
float_y = hand_landmarks.landmark[i].y
width = image.shape[1]
height = image.shape[0]
pt = mp_drawing._normalized_to_pixel_coordinates(float_x, float_y, width, height)
new_landmarks.append(pt)
return new_landmarks
def Draw_hand_points(image, normalized_hand_landmarks):
cv2.circle(image, normalized_hand_landmarks[4], 12, (255, 0, 255), -1, cv2.LINE_AA)
cv2.circle(image, normalized_hand_landmarks[8], 12, (255, 0, 255), -1, cv2.LINE_AA)
cv2.line(image, normalized_hand_landmarks[4], normalized_hand_landmarks[8], (255, 0, 255), 3)
x1, y1 = normalized_hand_landmarks[4][0], normalized_hand_landmarks[4][1]
x2, y2 = normalized_hand_landmarks[8][0], normalized_hand_landmarks[8][1]
mid_x, mid_y = (x1 + x2) // 2, (y1 + y2) // 2
length = math.sqrt((x2 - x1)**2+(y2 - y1)**2) #得到大拇指到食指的距离
if length < 100:
cv2.circle(image, (mid_x, mid_y), 12, (0, 255, 0), cv2.FILLED)
else:
cv2.circle(image, (mid_x, mid_y), 12, (255, 0, 255), cv2.FILLED)
return image, length
def vol_tansfer(x):
dict = {0: -65.25, 1: -56.99, 2: -51.67, 3: -47.74, 4: -44.62, 5: -42.03, 6: -39.82, 7: -37.89, 8: -36.17,
9: -34.63, 10: -33.24,
11: -31.96, 12: -30.78, 13: -29.68, 14: -28.66, 15: -27.7, 16: -26.8, 17: -25.95, 18: -25.15, 19: -24.38,
20: -23.65,
21: -22.96, 22: -22.3, 23: -21.66, 24: -21.05, 25: -20.46, 26: -19.9, 27: -19.35, 28: -18.82, 29: -18.32,
30: -17.82,
31: -17.35, 32: -16.88, 33: -16.44, 34: -16.0, 35: -15.58, 36: -15.16, 37: -14.76, 38: -14.37, 39: -13.99,
40: -13.62,
41: -13.26, 42: -12.9, 43: -12.56, 44: -12.22, 45: -11.89, 46: -11.56, 47: -11.24, 48: -10.93, 49: -10.63,
50: -10.33,
51: -10.04, 52: -9.75, 53: -9.47, 54: -9.19, 55: -8.92, 56: -8.65, 57: -8.39, 58: -8.13, 59: -7.88,
60: -7.63,
61: -7.38, 62: -7.14, 63: -6.9, 64: -6.67, 65: -6.44, 66: -6.21, 67: -5.99, 68: -5.76, 69: -5.55, 70: -5.33,
71: -5.12, 72: -4.91, 73: -4.71, 74: -4.5, 75: -4.3, 76: -4.11, 77: -3.91, 78: -3.72, 79: -3.53, 80: -3.34,
81: -3.15, 82: -2.97, 83: -2.79, 84: -2.61, 85: -2.43, 86: -2.26, 87: -2.09, 88: -1.91, 89: -1.75,
90: -1.58,
91: -1.41, 92: -1.25, 93: -1.09, 94: -0.93, 95: -0.77, 96: -0.61, 97: -0.46, 98: -0.3, 99: -0.15, 100: 0.0}
return dict[x]
def vol_tansfer_reverse(x):
error = []
dict = {0: -65.25, 1: -56.99, 2: -51.67, 3: -47.74, 4: -44.62, 5: -42.03, 6: -39.82, 7: -37.89, 8: -36.17,
9: -34.63, 10: -33.24,
11: -31.96, 12: -30.78, 13: -29.68, 14: -28.66, 15: -27.7, 16: -26.8, 17: -25.95, 18: -25.15, 19: -24.38,
20: -23.65,
21: -22.96, 22: -22.3, 23: -21.66, 24: -21.05, 25: -20.46, 26: -19.9, 27: -19.35, 28: -18.82, 29: -18.32,
30: -17.82,
31: -17.35, 32: -16.88, 33: -16.44, 34: -16.0, 35: -15.58, 36: -15.16, 37: -14.76, 38: -14.37, 39: -13.99,
40: -13.62,
41: -13.26, 42: -12.9, 43: -12.56, 44: -12.22, 45: -11.89, 46: -11.56, 47: -11.24, 48: -10.93, 49: -10.63,
50: -10.33,
51: -10.04, 52: -9.75, 53: -9.47, 54: -9.19, 55: -8.92, 56: -8.65, 57: -8.39, 58: -8.13, 59: -7.88,
60: -7.63,
61: -7.38, 62: -7.14, 63: -6.9, 64: -6.67, 65: -6.44, 66: -6.21, 67: -5.99, 68: -5.76, 69: -5.55, 70: -5.33,
71: -5.12, 72: -4.91, 73: -4.71, 74: -4.5, 75: -4.3, 76: -4.11, 77: -3.91, 78: -3.72, 79: -3.53, 80: -3.34,
81: -3.15, 82: -2.97, 83: -2.79, 84: -2.61, 85: -2.43, 86: -2.26, 87: -2.09, 88: -1.91, 89: -1.75,
90: -1.58,
91: -1.41, 92: -1.25, 93: -1.09, 94: -0.93, 95: -0.77, 96: -0.61, 97: -0.46, 98: -0.3, 99: -0.15, 100: 0.0}
for i in range (100):
error.append(abs(dict[i]-x))
return error.index(min(error))
hands = mp_hands.Hands(
min_detection_confidence=0.5, min_tracking_confidence=0.5)
cap = cv2.VideoCapture(0)
len_max = 0
len_min = 0
num = 0
while cap.isOpened():
stop = datetime.datetime.now() + datetime.timedelta(seconds=5)
if num == 0:
while datetime.datetime.now() < stop:
success, image = cap.read()
if not success:
print("camera frame is empty!")
continue
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
image.flags.writeable = False
results = hands.process(image)
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
mp_drawing.draw_landmarks(
image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
normalized_landmarks = Normalize_landmarks(image, hand_landmarks)
image, length = Draw_hand_points(image, normalized_landmarks)
if length>len_max:
len_max = length
strRate = 'Start calibration'
cv2.putText(image, strRate, (10, 410), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 0, 0), 2)
strRate1 = 'max length = %d'%len_max
cv2.putText(image, strRate1, (10, 110), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 0, 0), 2)
cv2.imshow('result', image)
if cv2.waitKey(5) & 0xFF == 27:
break
num = 1
success, image = cap.read()
if not success:
print("camera frame is empty!")
continue
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
image.flags.writeable = False
results = hands.process(image)
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
mp_drawing.draw_landmarks(
image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
normalized_landmarks = Normalize_landmarks(image, hand_landmarks)
try:
image, length = Draw_hand_points(image, normalized_landmarks)
# print(length) #20~300
cv2.rectangle(image, (50, 150), (85, 350), (255, 0, 0), 1)
if length >len_max:
length = len_max
vol = int((length) / len_max * 100)
volume.SetMasterVolumeLevel(vol_tansfer(vol), None)
cv2.rectangle(image, (50, 150+200-2*vol), (85, 350), (255, 0, 0), cv2.FILLED)
percent = int(length / len_max * 100)
# print(percent)
strRate = str(percent) + '%'
cv2.putText(image, strRate, (40, 410), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 0, 0), 2)
vol_now = vol_tansfer_reverse(volume.GetMasterVolumeLevel())
strvol = 'Current volume is'+str(vol_now)
cv2.putText(image, strvol, (10, 470), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 0, 0), 2)
except:
pass
cv2.imshow('result', image)
if cv2.waitKey(10) & 0xFF == ord('q'):
len_max = 0
num = 0
if cv2.waitKey(10) & 0xFF == 27:
break
cv2.destroyAllWindows()
hands.close()
cap.release()
很好奇，就使用rubyonrails自动化单元测试而言，你们正在做什么？您是否创建了一个脚本来在cron中运行rake作业并将结果邮寄给您？git中的预提交Hook？只是手动调用？我完全理解测试，但想知道在错误发生之前捕获错误的最佳实践是什么。让我们理所当然地认为测试本身是完美无缺的，并且可以正常工作。下一步是什么以确保他们在正确的时间将可能有害的结果传达给您？ 最佳答案 不确定您到底想听什么，但是有几个级别的自动代码库控制:在处理某项功能时，您可以使用类似autotest的内容获得关于哪些有效，哪些无效的即时反馈。要确保您的提
关闭。这个问题是opinion-based.它目前不接受答案。想要改进这个问题？更新问题，以便editingthispost可以用事实和引用来回答它.关闭4年前。Improvethisquestion我想在固定时间创建一系列低音和高音调的哔哔声。例如:在150毫秒时发出高音调的蜂鸣声在151毫秒时发出低音调的蜂鸣声200毫秒时发出低音调的蜂鸣声250毫秒的高音调蜂鸣声有没有办法在Ruby或Python中做到这一点？我真的不在乎输出编码是什么(.wav、.mp3、.ogg等等)，但我确实想创建一个输出文件。
在控制台中反复尝试之后，我想到了这种方法，可以按发生日期对类似activerecord的(Mongoid)对象进行分组。我不确定这是完成此任务的最佳方法，但它确实有效。有没有人有更好的建议，或者这是一个很好的方法？#eventsisanarrayofactiverecord-likeobjectsthatincludeatimeattributeevents.map{|event|#converteventsarrayintoanarrayofhasheswiththedayofthemonthandtheevent{:number=>event.time.day,:event=>ev
我正在编写一个包含C扩展的gem。通常当我写一个gem时，我会遵循TDD的过程，我会写一个失败的规范，然后处理代码直到它通过，等等......在“ext/mygem/mygem.c”中我的C扩展和在gemspec的“扩展”中配置的有效extconf.rb，如何运行我的规范并仍然加载我的C扩展？当我更改C代码时，我需要采取哪些步骤来重新编译代码？这可能是个愚蠢的问题，但是从我的gem的开发源代码树中输入“bundleinstall”不会构建任何native扩展。当我手动运行rubyext/mygem/extconf.rb时，我确实得到了一个Makefile(在整个项目的根目录中)，然后当
这是一道面试题，我没有答对，但还是很好奇怎么解。你有N个人的大家庭，分别是1,2,3,...,N岁。你想给你的大家庭拍张照片。所有的家庭成员都排成一排。“我是家里的friend，建议家庭成员安排如下:”1岁的家庭成员坐在这一排的最左边。每两个坐在一起的家庭成员的年龄相差不得超过2岁。输入:整数N，1≤N≤55。输出:摄影师可以拍摄的照片数量。示例->输入:4，输出:4符合条件的数组:[1,2,3,4][1,2,4,3][1,3,2,4][1,3,4,2]另一个例子:输入:5输出:6符合条件的数组:[1,2,3,4,5][1,2,3,5,4][1,2,4,3,5][1,2,4,5,3][
我已经构建了一些serverspec代码来在多个主机上运行一组测试。问题是当任何测试失败时，测试会在当前主机停止。即使测试失败，我也希望它继续在所有主机上运行。Rakefile:namespace:specdotask:all=>hosts.map{|h|'spec:'+h.split('.')[0]}hosts.eachdo|host|begindesc"Runserverspecto#{host}"RSpec::Core::RakeTask.new(host)do|t|ENV['TARGET_HOST']=hostt.pattern="spec/cfengine3/*_spec.r
我正在尝试编写一个将文件上传到AWS并公开该文件的Ruby脚本。我做了以下事情:s3=Aws::S3::Resource.new(credentials:Aws::Credentials.new(KEY,SECRET),region:'us-west-2')obj=s3.bucket('stg-db').object('key')obj.upload_file(filename)这似乎工作正常，除了该文件不是公开可用的，而且我无法获得它的公共(public)URL。但是当我登录到S3时，我可以正常查看我的文件。为了使其公开可用，我将最后一行更改为obj.upload_file(file
我们的git存储库中目前有一个Gemfile。但是，有一个gem我只在我的环境中本地使用(我的团队不使用它)。为了使用它，我必须将它添加到我们的Gemfile中，但每次我checkout到我们的master/dev主分支时，由于与跟踪的gemfile冲突，我必须删除它。我想要的是类似Gemfile.local的东西，它将继承从Gemfile导入的gems，但也允许在那里导入新的gems以供使用只有我的机器。此文件将在.gitignore中被忽略。这可能吗？ 最佳答案 设置BUNDLE_GEMFILE环境变量:BUNDLE_GEMFI
如何在ruby中调用C#dll？ 最佳答案 我能想到几种可能性:为您的DLL编写(或找人编写)一个COM包装器，如果它还没有，则使用Ruby的WIN32OLE库来调用它；看看RubyCLR,其中一位作者是JohnLam，他继续在Microsoft从事IronRuby方面的工作。(估计不会再维护了，可能不支持.Net2.0以上的版本)；正如其他地方已经提到的，看看使用IronRuby，如果这是您的技术选择。有一个主题是here.请注意，最后一篇文章实际上来自JohnLam(看起来像是2009年3月)，他似乎很自在地断言RubyCL
这似乎非常适得其反，因为太多的gem会在window上破裂。我一直在处理很多mysql和ruby-mysqlgem问题(gem本身发生段错误，一个名为UnixSocket的类显然在Windows机器上不能正常工作，等等)。我只是在浪费时间吗？我应该转向不同的脚本语言吗？ 最佳答案 我在Windows上使用Ruby的经验很少，但是当我开始使用Ruby时，我是在Windows上，我的总体印象是它不是Windows原生系统。因此，在主要使用Windows多年之后，开始使用Ruby促使我切换回原来的系统Unix，这次是Linux。Rub