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import cv2
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import numpy as np
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# 调整图像亮度(img为图像， brightness在-100~100内可取)
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def brightness_change(img, brightness):
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img_t = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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h, s, v = cv2.split(img_t)
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v1 = np.clip(cv2.add(1*v, brightness), 0, 255)
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dst = np.uint8(cv2.merge((h, s, v1)))
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dst = cv2.cvtColor(dst, cv2.COLOR_HSV2BGR)
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return dst
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# 平移图像(x为水平移动距离， y为垂直移动距离)
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def translation(img, x, y):
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height, width, channel = img.shape
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M = np.float32([[1, 0, x], [0, 1, y]])
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dst = cv2.warpAffine(img, M, (width, height), borderValue=(255, 255, 255))
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return dst
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# 旋转图像(direction为0是顺时针，1是逆时针；angle为旋转角度)
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def rotation(img, direction, angle):
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if direction == 1:
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angle = -1*angle
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height, width, channel = img.shape
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M = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1)
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dst = cv2.warpAffine(img, M, (width, height), borderValue=(255, 255, 255))
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return dst
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# 翻转图像(direction决定翻转方向，0是水平翻转，1是垂直翻转)
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def flip(img, direction):
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if direction == 1:
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direction = 0
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else:
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direction = 1
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dst = cv2.flip(img, direction)
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return dst
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# 放缩图像(proportion是放缩比例)
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def scale(img, proportion):
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if proportion >= 1:
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dst = cv2.resize(img, (0, 0), fx=proportion, fy=proportion, interpolation=cv2.INTER_CUBIC)
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else:
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dst = cv2.resize(img, (0, 0), fx=proportion, fy=proportion, interpolation=cv2.INTER_AREA)
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return dst
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# 仿射变换图像(给出三组点的前后坐标，实现仿射变换)
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def affine(img, x1, y1, x1_change, y1_change, x2, y2, x2_change, y2_change, x3, y3, x3_change, y3_change):
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rows, cols = img.shape[: 2]
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post1 = np.float32([[x1, y1], [x2, y2], [x3, y3]])
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post2 = np.float32([[x1_change, y1_change], [x2_change, y2_change], [x3_change, y3_change]])
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M = cv2.getAffineTransform(post1, post2)
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dst = cv2.warpAffine(img, M, (rows, cols), borderValue=(255, 255, 255))
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return dst
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# 绘制直方图(使用histogram，imageHist是中间函数)//主要这个matplotlib不知道为什么用不了？我看了你的依赖里好像没有，我就查了下换了个方法。
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def imageHist(img, type):
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# color = (255, 255, 255)
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if type == 31:
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color = (255, 0, 0)
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windowName = 'B Hist'
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elif type == 32:
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color = (0, 255, 0)
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windowName = 'G Hist'
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elif type == 33:
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color = (0, 0, 255)
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windowName = 'R Hist'
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Hist = cv2.calcHist([img], [0], None, [256], [0.0, 255.0]) # 计算直方图
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minV, maxV, minL, MaxL = cv2.minMaxLoc(Hist) # 获取最大值和最小值，并获取其对应的下标
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HistImage = np.zeros([256, 256, 3], np.uint8) # 新建图像模板，用于直方图的绘制
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for h in range(256):
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intenormal = int(Hist[h] * 256 / maxV) # 避免超出范围
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cv2.line(HistImage, (h, 256), (h, 256 - intenormal), color) # 绘制直方图
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return HistImage
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def histogram(img):
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channels = cv2.split(img)
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image0 = imageHist(channels[0], 31)
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image1 = imageHist(channels[1], 32)
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image2 = imageHist(channels[2], 33)
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imgs = np.hstack([image0, image1, image2])
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return imgs
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# 调整图像的对比度
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def contrast_change(img, contrast):
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img_t = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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h, s, v = cv2.split(img_t)
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v1 = np.clip(cv2.add(2 * v, contrast), 0, 255)
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dst = np.uint8(cv2.merge((h, s, v1)))
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dst = cv2.cvtColor(dst, cv2.COLOR_HSV2BGR)
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return dst
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