OPENCV- TypeError: integer argument expected, got float
OPENCV- TypeError: integer argument expected, got float
伙计们，我正在尝试学习这个快速教程。
我也在努力学习我们输入的每一个代码。
但是我得到了一个错误。我正在努力学习每一个代码，所以你能告诉我为什么会出现这个错误吗？以及如何解决这个错误？
我得到 def draw_lines(img, lines, color=[255, 0, 0], thickness=3):[= 函数错误15=]
我得到的错误：
File "C:\Users\alpgu\Desktop\opencv\yolboyama.py", line 112, in
poly_left = np.poly1d(np.polyfit(
File "<array_function internals>", line 5, in polyfit
File
"C:\Users\alpgu\anaconda3\lib\site-packages\numpy\lib\polynomial.py",
line 599, in polyfit
raise TypeError("expected non-empty vector for x")
TypeError: expected non-empty vector for x
我认为问题出在::
这一行
poly_left = np.poly1d(np.polyfit(
left_line_y,
left_line_x,
deg=1
))
为什么会这样？
备注==>
当我更改我的最大 x 和最小 y 代码时（你说它应该是 int 所以我试图更改我的代码。我也尝试做 int 但它给出了另一个错误。所以我不明白我应该知道什么？）：：
min_y = int(yol_resmi.shape * (3 / 5)) # <-- Just below the horizon
max_y = int(yol_resmi.shape)
给出了
的错误
TypeError: can't multiply sequence by non-int of type 'float'
问题是关于我的 max_x - min_y 值还是关于我的 polly_left 值？
我的所有代码：
from os.path import sep
import cv2
import numpy as np
import math
cap = cv2.VideoCapture('photos' + sep + 'roads.mp4')
while(cap.isOpened()):
ret, yol_resmi = cap.read()
# Define a blank matrix that matches the image height/width.
def region_of_interest(img, vertices):
mask = np.zeros_like(img)
channel_count = yol_resmi.shape[2]
match_mask_color = (255,) * channel_count
cv2.fillPoly(mask, vertices, match_mask_color)
masked_image = cv2.bitwise_and(img, mask)
return masked_image
height, width, channel = yol_resmi.shape
region_of_interest_vertices = [
(0, height),
(width / 2, height / 2),
(width, height),
]
def draw_lines(img, lines, color=[255, 0, 0], thickness=3):
line_img = np.zeros(
(
img.shape[0],
img.shape[1],
3
),
dtype=np.uint8
)
img = np.copy(img)
if lines is None:
return
for line in lines:
for x1, y1, x2, y2 in line:
cv2.line(line_img, (x1, y1), (x2, y2), color, thickness)
img = cv2.addWeighted(img, 0.8, line_img, 1.0, 0.0)
return img
#COLOR_BGR2GRAY ekle
gray_image = cv2.cvtColor(yol_resmi, cv2.COLOR_RGB2GRAY)
cannyed_image = cv2.Canny(gray_image, 200, 300)
cropped_image = region_of_interest(
cannyed_image,
np.array(
[region_of_interest_vertices],
np.int32
),
)
lines = cv2.HoughLinesP(
cropped_image,
rho=6,
theta=np.pi / 60,
threshold=160,
lines=np.array([]),
minLineLength=40,
maxLineGap=25
)
print(lines)
line_image = draw_lines(yol_resmi, lines)
left_line_x = []
left_line_y = []
right_line_x = []
right_line_y = []
for line in lines:
for x1, y1, x2, y2 in line:
slope = (y2 - y1) / (x2 - x1) # <-- Calculating the slope.
if math.fabs(slope) < 0.5: # <-- Only consider extreme slope
continue
if slope <= 0: # <-- If the slope is negative, left group.
left_line_x.extend([x1, x2])
left_line_y.extend([y1, y2])
else: # <-- Otherwise, right group.
right_line_x.extend([x1, x2])
right_line_y.extend([y1, y2])
min_y = yol_resmi.shape[0] * (3 / 5) # <-- Just below the horizon
max_y = yol_resmi.shape[0] # <-- The bottom of the image
poly_left = np.poly1d(np.polyfit(
left_line_y,
left_line_x,
deg=1
))
left_x_start = int(poly_left(max_y))
left_x_end = int(poly_left(min_y))
poly_right = np.poly1d(np.polyfit(
right_line_y,
right_line_x,
deg=1
))
right_x_start = int(poly_right(max_y))
right_x_end = int(poly_right(min_y))
line_image = draw_lines(
yol_resmi,
[[
[left_x_start, max_y, left_x_end, min_y],
[right_x_start, max_y, right_x_end, min_y],
]],
thickness=5,
)
# Convert to grayscale here.
#gray_image = cv2.cvtColor(cropped_image, cv2.COLOR_RGB2GRAY)
# Call Canny Edge Detection here.
#cannyed_image = cv2.Canny(gray_image, 100, 200)
#cv2.imshow('yol resmi', yol_resmi)
cv2.imshow('edge detection', cropped_image)
cv2.imshow('line detection', line_image)
#cv2.imshow('Edge Detection', cannyed_image)
cv2.waitKey(1)
这就是我所能做的：
from os.path import sep
import cv2
import numpy as np
import math
import warnings
cap = cv2.VideoCapture('photos' + sep + 'roads.mp4')
while cap.isOpened():
ret, yol_resmi = cap.read()
# Define a blank matrix that matches the image height/width.
def region_of_interest(img, vertices):
mask = np.zeros_like(img)
channel_count = yol_resmi.shape[2]
match_mask_color = (255,) * channel_count
cv2.fillPoly(mask, vertices, match_mask_color)
masked_image = cv2.bitwise_and(img, mask)
return masked_image
height, width, channel = yol_resmi.shape
region_of_interest_vertices = [
(0, height),
(width / 2, height / 2),
(width, height),
]
def draw_lines(img, lines, color=[255, 0, 0], thickness=3):
line_img = np.zeros(
(
img.shape[0],
img.shape[1],
3
),
dtype=np.uint8
)
img = np.copy(img)
if lines is None:
return
for line in lines:
for x1, y1, x2, y2 in line:
print(type(x1), type(x2), type(y1), type(y2))
cv2.line(line_img, (x1, y1), (x2, int(y2)), color, thickness)
img = cv2.addWeighted(img, 0.8, line_img, 1.0, 0.0)
return img
# COLOR_BGR2GRAY ekle
gray_image = cv2.cvtColor(yol_resmi, cv2.COLOR_RGB2GRAY)
cannyed_image = cv2.Canny(gray_image, 200, 300)
cropped_image = region_of_interest(
cannyed_image,
np.array(
[region_of_interest_vertices],
np.int32
),
)
lines = cv2.HoughLinesP(
cropped_image,
rho=6,
theta=np.pi / 60,
threshold=160,
lines=np.array([]),
minLineLength=40,
maxLineGap=25
)
print(lines)
print(len(lines))
line_image = draw_lines(yol_resmi, lines)
left_line_x = [0, 0]
left_line_y = [0, 0]
right_line_x = [0, 0]
right_line_y = [0, 0]
for line in lines:
for x1, y1, x2, y2 in line:
slope = (y2 - y1) / (x2 - x1) # <-- Calculating the slope.
if math.fabs(slope) < 0.5: # <-- Only consider extreme slope
continue
if slope <= 0: # <-- If the slope is negative, left group.
left_line_x = [x1, x2]
left_line_y = [y1, y2]
else: # <-- Otherwise, right group.
right_line_x = [x1, x2]
right_line_y = [y1, y2]
min_y = yol_resmi.shape[0] * (3 / 5) # <-- Just below the horizon
max_y = yol_resmi.shape[0] # <-- The bottom of the image
try:
poly_left = np.poly1d(np.polyfit(
left_line_y,
left_line_x,
deg=1
))
left_x_start = int(poly_left(max_y))
left_x_end = int(poly_left(min_y))
poly_right = np.poly1d(np.polyfit(
right_line_y,
right_line_x,
deg=1
))
right_x_start = int(poly_right(max_y))
right_x_end = int(poly_right(min_y))
except np.linalg.LinAlgError as e:
warnings.warn("There is no lines in chosen region")
cv2.imshow('line detection', yol_resmi)
cv2.waitKey(1)
continue
line_image = draw_lines(
yol_resmi,
[[
[left_x_start, max_y, left_x_end, min_y],
[right_x_start, max_y, right_x_end, min_y],
]],
thickness=5,
)
# Convert to grayscale here.
# gray_image = cv2.cvtColor(cropped_image, cv2.COLOR_RGB2GRAY)
# Call Canny Edge Detection here.
# cannyed_image = cv2.Canny(gray_image, 100, 200)
# cv2.imshow('yol resmi', yol_resmi)
cv2.imshow('edge detection', cropped_image)
cv2.imshow('line detection', line_image)
# cv2.imshow('Edge Detection', cannyed_image)
cv2.waitKey(1)
伙计们，我正在尝试学习这个快速教程。 我也在努力学习我们输入的每一个代码。 但是我得到了一个错误。我正在努力学习每一个代码，所以你能告诉我为什么会出现这个错误吗？以及如何解决这个错误？
我得到 def draw_lines(img, lines, color=[255, 0, 0], thickness=3):[= 函数错误15=]
我得到的错误：
File "C:\Users\alpgu\Desktop\opencv\yolboyama.py", line 112, in poly_left = np.poly1d(np.polyfit(
File "<array_function internals>", line 5, in polyfit
File "C:\Users\alpgu\anaconda3\lib\site-packages\numpy\lib\polynomial.py", line 599, in polyfit raise TypeError("expected non-empty vector for x")
TypeError: expected non-empty vector for x
我认为问题出在::
这一行poly_left = np.poly1d(np.polyfit(
left_line_y,
left_line_x,
deg=1
))
为什么会这样？
备注==> 当我更改我的最大 x 和最小 y 代码时（你说它应该是 int 所以我试图更改我的代码。我也尝试做 int 但它给出了另一个错误。所以我不明白我应该知道什么？）：：
min_y = int(yol_resmi.shape * (3 / 5)) # <-- Just below the horizon
max_y = int(yol_resmi.shape)
给出了
的错误TypeError: can't multiply sequence by non-int of type 'float'
问题是关于我的 max_x - min_y 值还是关于我的 polly_left 值？
我的所有代码：
from os.path import sep
import cv2
import numpy as np
import math
cap = cv2.VideoCapture('photos' + sep + 'roads.mp4')
while(cap.isOpened()):
ret, yol_resmi = cap.read()
# Define a blank matrix that matches the image height/width.
def region_of_interest(img, vertices):
mask = np.zeros_like(img)
channel_count = yol_resmi.shape[2]
match_mask_color = (255,) * channel_count
cv2.fillPoly(mask, vertices, match_mask_color)
masked_image = cv2.bitwise_and(img, mask)
return masked_image
height, width, channel = yol_resmi.shape
region_of_interest_vertices = [
(0, height),
(width / 2, height / 2),
(width, height),
]
def draw_lines(img, lines, color=[255, 0, 0], thickness=3):
line_img = np.zeros(
(
img.shape[0],
img.shape[1],
3
),
dtype=np.uint8
)
img = np.copy(img)
if lines is None:
return
for line in lines:
for x1, y1, x2, y2 in line:
cv2.line(line_img, (x1, y1), (x2, y2), color, thickness)
img = cv2.addWeighted(img, 0.8, line_img, 1.0, 0.0)
return img
#COLOR_BGR2GRAY ekle
gray_image = cv2.cvtColor(yol_resmi, cv2.COLOR_RGB2GRAY)
cannyed_image = cv2.Canny(gray_image, 200, 300)
cropped_image = region_of_interest(
cannyed_image,
np.array(
[region_of_interest_vertices],
np.int32
),
)
lines = cv2.HoughLinesP(
cropped_image,
rho=6,
theta=np.pi / 60,
threshold=160,
lines=np.array([]),
minLineLength=40,
maxLineGap=25
)
print(lines)
line_image = draw_lines(yol_resmi, lines)
left_line_x = []
left_line_y = []
right_line_x = []
right_line_y = []
for line in lines:
for x1, y1, x2, y2 in line:
slope = (y2 - y1) / (x2 - x1) # <-- Calculating the slope.
if math.fabs(slope) < 0.5: # <-- Only consider extreme slope
continue
if slope <= 0: # <-- If the slope is negative, left group.
left_line_x.extend([x1, x2])
left_line_y.extend([y1, y2])
else: # <-- Otherwise, right group.
right_line_x.extend([x1, x2])
right_line_y.extend([y1, y2])
min_y = yol_resmi.shape[0] * (3 / 5) # <-- Just below the horizon
max_y = yol_resmi.shape[0] # <-- The bottom of the image
poly_left = np.poly1d(np.polyfit(
left_line_y,
left_line_x,
deg=1
))
left_x_start = int(poly_left(max_y))
left_x_end = int(poly_left(min_y))
poly_right = np.poly1d(np.polyfit(
right_line_y,
right_line_x,
deg=1
))
right_x_start = int(poly_right(max_y))
right_x_end = int(poly_right(min_y))
line_image = draw_lines(
yol_resmi,
[[
[left_x_start, max_y, left_x_end, min_y],
[right_x_start, max_y, right_x_end, min_y],
]],
thickness=5,
)
# Convert to grayscale here.
#gray_image = cv2.cvtColor(cropped_image, cv2.COLOR_RGB2GRAY)
# Call Canny Edge Detection here.
#cannyed_image = cv2.Canny(gray_image, 100, 200)
#cv2.imshow('yol resmi', yol_resmi)
cv2.imshow('edge detection', cropped_image)
cv2.imshow('line detection', line_image)
#cv2.imshow('Edge Detection', cannyed_image)
cv2.waitKey(1)
这就是我所能做的：
from os.path import sep
import cv2
import numpy as np
import math
import warnings
cap = cv2.VideoCapture('photos' + sep + 'roads.mp4')
while cap.isOpened():
ret, yol_resmi = cap.read()
# Define a blank matrix that matches the image height/width.
def region_of_interest(img, vertices):
mask = np.zeros_like(img)
channel_count = yol_resmi.shape[2]
match_mask_color = (255,) * channel_count
cv2.fillPoly(mask, vertices, match_mask_color)
masked_image = cv2.bitwise_and(img, mask)
return masked_image
height, width, channel = yol_resmi.shape
region_of_interest_vertices = [
(0, height),
(width / 2, height / 2),
(width, height),
]
def draw_lines(img, lines, color=[255, 0, 0], thickness=3):
line_img = np.zeros(
(
img.shape[0],
img.shape[1],
3
),
dtype=np.uint8
)
img = np.copy(img)
if lines is None:
return
for line in lines:
for x1, y1, x2, y2 in line:
print(type(x1), type(x2), type(y1), type(y2))
cv2.line(line_img, (x1, y1), (x2, int(y2)), color, thickness)
img = cv2.addWeighted(img, 0.8, line_img, 1.0, 0.0)
return img
# COLOR_BGR2GRAY ekle
gray_image = cv2.cvtColor(yol_resmi, cv2.COLOR_RGB2GRAY)
cannyed_image = cv2.Canny(gray_image, 200, 300)
cropped_image = region_of_interest(
cannyed_image,
np.array(
[region_of_interest_vertices],
np.int32
),
)
lines = cv2.HoughLinesP(
cropped_image,
rho=6,
theta=np.pi / 60,
threshold=160,
lines=np.array([]),
minLineLength=40,
maxLineGap=25
)
print(lines)
print(len(lines))
line_image = draw_lines(yol_resmi, lines)
left_line_x = [0, 0]
left_line_y = [0, 0]
right_line_x = [0, 0]
right_line_y = [0, 0]
for line in lines:
for x1, y1, x2, y2 in line:
slope = (y2 - y1) / (x2 - x1) # <-- Calculating the slope.
if math.fabs(slope) < 0.5: # <-- Only consider extreme slope
continue
if slope <= 0: # <-- If the slope is negative, left group.
left_line_x = [x1, x2]
left_line_y = [y1, y2]
else: # <-- Otherwise, right group.
right_line_x = [x1, x2]
right_line_y = [y1, y2]
min_y = yol_resmi.shape[0] * (3 / 5) # <-- Just below the horizon
max_y = yol_resmi.shape[0] # <-- The bottom of the image
try:
poly_left = np.poly1d(np.polyfit(
left_line_y,
left_line_x,
deg=1
))
left_x_start = int(poly_left(max_y))
left_x_end = int(poly_left(min_y))
poly_right = np.poly1d(np.polyfit(
right_line_y,
right_line_x,
deg=1
))
right_x_start = int(poly_right(max_y))
right_x_end = int(poly_right(min_y))
except np.linalg.LinAlgError as e:
warnings.warn("There is no lines in chosen region")
cv2.imshow('line detection', yol_resmi)
cv2.waitKey(1)
continue
line_image = draw_lines(
yol_resmi,
[[
[left_x_start, max_y, left_x_end, min_y],
[right_x_start, max_y, right_x_end, min_y],
]],
thickness=5,
)
# Convert to grayscale here.
# gray_image = cv2.cvtColor(cropped_image, cv2.COLOR_RGB2GRAY)
# Call Canny Edge Detection here.
# cannyed_image = cv2.Canny(gray_image, 100, 200)
# cv2.imshow('yol resmi', yol_resmi)
cv2.imshow('edge detection', cropped_image)
cv2.imshow('line detection', line_image)
# cv2.imshow('Edge Detection', cannyed_image)
cv2.waitKey(1)