pip3 install opencv-python
显示版本
import cv2
print(cv2.__version__)
import cv2 as cv
src = cv.imread("me.jpeg")
cv.namedWindow("Picture", cv.WINDOW_AUTOSIZE)
cv.imshow("Picture", src)
cv.waitKey(0)
# 关闭所有窗口
cv.destroyAllWindows()
import cv2
import matplotlib.pyplot as plt
# 读取图像
# img = cv2.imread('path_to_image.jpg')
img = cv2.imread('test/test.png')
# 将BGR图像转为RGB
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 使用matplotlib显示图像
plt.imshow(img_rgb)
plt.axis('off') # 不显示坐标轴
plt.show()
import cv2
filePath="Dataset/FFHQ/00000.png"
img=cv2.imread(filePath)
print(f"img.shape = {img.shape}") # img.shape = (128, 128, 3)
print(f"img = {img}") # img.dtype = uint8
"""
img = [[[146 130 0]
[144 128 0]
[141 125 0]
...
[164 162 133]
[159 157 133]
[163 157 134]]]
"""
import cv2
# 加载图像
img = cv2.imread('path_to_image.jpg', 0) # 0表示以灰度模式读取
# 应用Canny边缘检测
edges = cv2.Canny(img, 100, 200)
# 显示结果
cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()
import cv2 as cv
capture = cv.VideoCapture(1)
while (True):
# ret为返回值，frame为视频的每一帧
ret, frame = capture.read()
cv.imshow("video", frame)
c = cv.waitKey(50)
# 按了esc候可以退出
if c == 27:
break
import cv2 as cv
image = cv.imread("me.jpeg")
print(type(image)) # 类别
print(image.shape) # 高，宽，通道数目
print(image.size) # 像素数据，上面三个属性的乘积
print(image.dtype)
import cv2
import numpy as np
import matplotlib.pyplot as plt
# 加载彩色图像
image = cv2.imread(r'C:\Users\neo\Downloads\tongue.jpg')
# 将彩色图像分成RGB通道
red_channel, green_channel, blue_channel = cv2.split(image)
# 对每个通道应用equalizeHist函数
red_channel_eq = cv2.equalizeHist(red_channel)
green_channel_eq = cv2.equalizeHist(green_channel)
blue_channel_eq = cv2.equalizeHist(blue_channel)
# 合并均衡化的通道
equalized_image = cv2.merge((red_channel_eq, green_channel_eq, blue_channel_eq))
# 显示图像
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
# 显示原始图像
axes[0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
axes[0].set_title('原始图像')
# 显示均衡化后的图像
axes[1].imshow(cv2.cvtColor(equalized_image, cv2.COLOR_BGR2RGB))
axes[1].set_title('均衡化后的图像')
# 隐藏坐标轴
for ax in axes:
ax.axis('off')
# 展示图像
plt.show()
import cv2
import numpy as np
def detect_color(image_path):
# 读取图像
img = cv2.imread(image_path)
# 转换到HSV颜色空间
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# 定义HSV范围
# 注意：这些范围可能需要根据你的实际图像进行调整
lower_red = np.array([0, 120, 70])
upper_red = np.array([10, 255, 255])
lower_green = np.array([35, 100, 50])
upper_green = np.array([85, 255, 255])
lower_blue = np.array([90, 40, 50])
upper_blue = np.array([140, 255, 255])
lower_yellow = np.array([20, 100, 100])
upper_yellow = np.array([30, 255, 255])
# 设置白色在 HSV 中的范围
# lower_white = np.array([0, 0, 200], dtype=np.uint8)
# upper_white = np.array([180, 30, 255], dtype=np.uint8)
lower_white = np.array([0, 0, 200])
upper_white = np.array([180, 30, 255])
# 创建颜色掩码
mask_red = cv2.inRange(hsv, lower_red, upper_red)
mask_green = cv2.inRange(hsv, lower_green, upper_green)
mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
# 创建掩码
mask_white = cv2.inRange(hsv, lower_white, upper_white)
# 计算每种颜色的像素数
red_count = cv2.countNonZero(mask_red)
green_count = cv2.countNonZero(mask_green)
blue_count = cv2.countNonZero(mask_blue)
yellow_count = cv2.countNonZero(mask_yellow)
mask_white = cv2.countNonZero(mask_white)
print(red_count, green_count, blue_count, yellow_count, mask_white)
# 确定最突出的颜色
max_count = max(red_count, green_count, blue_count, yellow_count, mask_white)
if max_count == red_count:
return "Red"
elif max_count == green_count:
return "Green"
elif max_count == blue_count:
return "Blue"
elif max_count == yellow_count:
return "Yellow"
elif max_count == mask_white:
return "White"
else:
return "Unknown Color"
color = detect_color("/Users/neo/Downloads/WechatIMG3054.jpg")
print(color)
import cv2
import numpy as np
from matplotlib import pyplot as plt
# Photoshop 自动色阶调整算法
def autoLevels(img, cutoff=0.1):
channels = img.shape[2] # h,w,ch
table = np.zeros((1, 256, 3), np.uint8)
for ch in range(channels):
# cutoff=0.1, 计算 0.1%, 99.9% 分位的灰度值
low = np.percentile(img[:, :, ch], q=cutoff) # ch 通道, cutoff=0.1, 0.1 分位的灰度值
high = np.percentile(img[:, :, ch], q=100 - cutoff) # 99.9 分位的灰度值, [0, high] 占比99.9%
# 输入动态线性拉伸
Sin = min(max(low, 0), high - 2) # Sin, 黑场阈值, 0<=Sin<Hin
Hin = min(high, 255) # Hin, 白场阈值, Sin<Hin<=255
difIn = Hin - Sin
V1 = np.array([(min(max(255 * (i - Sin) / difIn, 0), 255)) for i in range(256)])
# 灰场伽马调节
gradMed = np.median(img[:, :, ch]) # 拉伸前的中值
Mt = V1[int(gradMed)] / 128. # 拉伸后的映射值
V2 = 255 * np.power(V1 / 255, 1 / Mt) # 伽马调节
# 输出线性拉伸
Sout, Hout = 5, 250 # Sout 输出黑场阈值, Hout 输出白场阈值
difOut = Hout - Sout
table[0, :, ch] = np.array([(min(max(Sout + difOut * V2[i] / 255, 0), 255)) for i in range(256)])
return cv2.LUT(img, table)
# Photoshop 自动色阶调整算法
img = cv2.imread(r'C:\Users\neo\Downloads\tongue.jpg', flags=1) # 读取彩色图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转换为灰度图像
print("cutoff={}, minG={}, maxG={}".format(0.0, gray.min(), gray.min()))
# 色阶手动调整
# equManual = levelsAdjust(img, 63, 205, 0.8, 10, 245) # 手动调节
# 色阶自动调整
cutoff = 0.1 # 截断比例, 建议范围 [0.0,1.0]
equAuto = autoLevels(img, cutoff)
plt.figure(figsize=(9, 6))
plt.subplot(131), plt.title("Origin"), plt.axis('off')
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
# plt.subplot(132), plt.title("ManualTuned"), plt.axis('off')
# plt.imshow(cv2.cvtColor(equManual, cv2.COLOR_BGR2RGB))
plt.subplot(133), plt.title("AutoLevels"), plt.axis('off')
plt.imshow(cv2.cvtColor(equAuto, cv2.COLOR_BGR2RGB))
plt.tight_layout()
plt.show()
import cv2
import numpy as np
def get_brightness(image):
# 将图像转换为灰度图
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 计算灰度图的平均亮度
brightness = np.mean(gray)
return brightness
def adjust_brightness(image, value):
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
v = np.clip(v.astype(int) + value, 0, 255).astype(np.uint8)
hsv = cv2.merge((h, s, v))
return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
if __name__ == "__main__":
try:
# 读取图片，这里你需要替换为实际的图片路径
image = cv2.imread('4.jpg')
if image is None:
print("无法读取图片，请检查文件路径。")
else:
# 获取原始图片的亮度
original_brightness = get_brightness(image)
print(f"原始图片亮度: {original_brightness}")
# 调整亮度的值，可根据需要修改
brightness_value = 50
adjusted_image = adjust_brightness(image, brightness_value)
# 获取调整后图片的亮度
adjusted_brightness = get_brightness(adjusted_image)
print(f"调整后图片亮度: {adjusted_brightness}")
# 显示原始图片和调整后的图片
cv2.imshow('Original Image', image)
cv2.imshow('Adjusted Image', adjusted_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
except Exception as e:
print(f"发生错误: {e}")
