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python3使用cv2对图像进行基本操作

int(img.shape[2]))) for i in range(int(img.shape[0])-2): for j in range(int(img.shape[1])-2):...int(img.shape[2]))) for i in range(int(img.shape[0])-2): for j in range(int(img.shape[1])-2):...int(img.shape[2]))) for i in range(int(img.shape[0])-2): for j in range(int(img.shape[1])-2):...int(img.shape[2]))) for i in range(int(img.shape[0])-2): for j in range(int(img.shape[1])-2):...)) pooling_img = np.zeros((int(int(img.shape[0])/2), int(int(img.shape[1])/2

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Python opencv 图像特效处理

[0], img.shape[1]), np.uint8) for i in range(0, img.shape[0]): for j in range(0, img.shape[1]):...[0], img.shape[1]), np.uint8) for i in range(gray.shape[0]): for j in range(gray.shape[1]):...[0], img.shape[1], img.shape[2]), np.uint8) for i in range(img.shape[0]): for j in range(img.shape...[0] width = img.shape[1] for m in range(100, 200): for n in range(200, 300): if m % 10 == 0 and n % 10...[0] width = img.shape[1] dat = np.zeros(img.shape, np.uint8) for m in range(height - 8): for n in

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python3使用cv2对图像进行基本操作

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opencv算法（持续更新）

膨胀腐蚀膨胀，白色区域扩大腐蚀，黑色区域扩大阈值 threshold阈值处理，处理针对每个通道，对rgb图片处理后，容易产生多种颜色，灰度图片的处理黑白单色抠图 mask = np.zeros([img.shape...[0], img.shape[1]], dtype='uint8') bgdModle = np.zeros((1, 65), np.float64) fgdModle = np.zeros((1, 65...), np.float64) rect = (50, 0, img.shape[1]-40, img.shape[0]) cv2.rectangle(img, (50, 0), (img.shape[1...]-40, img.shape[0]), (0, 0, 255), 5) cv2.grabCut(img, mask, rect, bgdModle, fgdModle, 3, cv2.GC_INIT_WITH_RECT...[0]+2, img.shape[1]+2], dtype='uint8') cv2.floodFill(img, mask, (120, 120), (255, 255, 255),

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用PyTorch做物体检测和追踪

[0] - img.shape[1], 0) * (img_size / max(img.shape)) pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size.../ max(img.shape)) unpad_h = img_size - pad_y unpad_w = img_size - pad_x if detections is not None:...img.shape[0] x1 = ((x1 - pad_x // 2) / unpad_w) * img.shape[1] color = bbox_colors[int...[0] - img.shape[1], 0) * (img_size / max(img.shape)) pad_y = max(img.shape[1] - img.shape...// 2) / unpad_h) * img.shape[0]) x1 = int(((x1 - pad_x // 2) / unpad_w) * img.shape[1])

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图像的表示和可视化

io,data,colorfrom matplotlib import pyplot as pltimg=io.imread('123.jpg')img1=data.astronaut()print(img.shape...)img0=io.imread('123.jpg',as_gray=True)print("图像类型：",type(img))#显示类型print("图像大小：",img.shape)#显示类型print...(img.shape[0])print(img.shape[1])print(img.shape[2])print(img.size)print(img.max())print(img.min())print...plt.show() opencv import cv2 as cv img=cv.imread('123.jpg') print("图像类型：",type(img))#显示类型print("图像大小：",img.shape...)#显示类型print(img.shape[0])print(img.shape[1])print(img.shape[2])print(img.size)print(img.max())print(img.min

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计算机视觉（1）：数字图像的存储结构

[0] - 1)y = random.randint(0, img.shape[1] - 1)print(f"像素值示例：[{x}, {y}]={img[x][y]}") # 输出类似"[0,0,0]...代码解析：for i in range(img.shape[0]): # 遍历每一行 for j in range(img.shape[1]): # 遍历每一列 for k...[0])y = random.randint(0, img.shape[1])print(f'单通道图片转换为 BGR 三通道图片尺寸:{img.shape} 大小:{img.size} Bytes...[0]): for j in range(img.shape[1]): for k in range(img.shape[2]): # 每个像素的每个通道设置像素值...img[i, j][k] = numpy.uint8((i + j) * (k + 1) % 256)print(f'BGR 三通道图片设置像素值尺寸:{img.shape}

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最大值灰度化

plt img=cv2.imread('C:/Users/xpp/Desktop/Lena.png')#原始图像 cv2.imshow("original",img) max_gray=np.zeros(img.shape...[0:2],dtype='uint8') ave_gray=np.zeros(img.shape[0:2],dtype='uint8') weight_gray=np.zeros(img.shape[0...:2],dtype='uint8') for ii in range(img.shape[0]): for jj in range(img.shape[1]): r,g,b=img

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【OpenCV】Chapter7.图像噪声与滤波器

/img/lena_noise.jpg", 0) img_h = img.shape[0] img_w = img.shape[1] # 算术平均滤波 (Arithmentic mean filter.../img/lena_noise.jpg", 0) img_h = img.shape[0] img_w = img.shape[1] # 算术平均滤波 (Arithmentic mean filter.../img/lena_noise.jpg", 0) img_h = img.shape[0] img_w = img.shape[1] m, n = 3, 3 kernalMean = np.ones...) # 中值滤波器 imgMaxFilter = np.zeros(img.shape) # 最大值滤波器 imgMinFilter = np.zeros(img.shape) # 最小值滤波器.../img/lena_noise.jpg", 0) img_h = img.shape[0] img_w = img.shape[1] m, n = 5, 5 kernalMean = np.ones

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加权平均值灰度化

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图像中的裂纹检测

= intermediate.predict(img[np.newaxis,:,:,:]) conv_output = np.squeeze(conv_output) h = int(img.shape...[0]/conv_output.shape[0]) w = int(img.shape[1]/conv_output.shape[1]) act_maps = sp.ndimage.zoom...(conv_output, (h, w, 1), order=1) out = np.dot(act_maps.reshape((img.shape[0]*img.shape[1],512)),...class_weights).reshape(img.shape[0],img.shape[1]) plt.imshow(img.astype('float32...').reshape(img.shape[0], img.shape[1],3)) plt.imshow(out, cmap='jet', alpha=0.35)

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图像中的裂纹检测

conv_output = intermediate.predict(img[np.newaxis,:,:,:]) conv_output = np.squeeze(conv_output) h = int(img.shape...[0]/conv_output.shape[0]) w = int(img.shape[1]/conv_output.shape[1]) act_maps = sp.ndimage.zoom...(conv_output, (h, w, 1), order=1) out = np.dot(act_maps.reshape((img.shape[0]*img.shape[1],512)),...class_weights).reshape(img.shape[0],img.shape[1]) plt.imshow(img.astype('float32').reshape(img.shape...[0], img.shape[1],3)) plt.imshow(out, cmap='jet', alpha=0.35) plt.title('Crack' if

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平均值灰度化

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使用 Python 和 OpenCV 进行数据增广

scale: percentage of cropped area ''' # Crop image height, width = int(img.shape[0]*scale),...int(img.shape[1]*scale) x = random.randint(0, img.shape[1] - int(width)) y = random.randint(0..., (img.shape[1], img.shape[0])) # Modify annotation new_boxes=[] for box in gt_boxes: obj_name...[1] and y1img.shape[0]) and (x2>0 and y2>0): if x1<0: x1=0 if y1<0: y1=0 if x2>img.shape[1]: x2=img.shape...[1] if y2>img.shape[0]: y2=img.shape[0] new_boxes.append([obj_name, x1, y1, x2, y2]) return

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使用Python+OpenCV进行数据增广方法综述（附代码演练）

scale: percentage of cropped area ''' # Crop image height, width = int(img.shape[0]*...scale), int(img.shape[1]*scale) x = random.randint(0, img.shape[1] - int(width)) y = random.randint...(0, img.shape[0] - int(height)) cropped = img[y:y+height, x:x+width] resized = cv2.resize(cropped..., (img.shape[1], img.shape[0])) # Modify annotation new_boxes=[] for box in gt_boxes...if x2>img.shape[1]: x2=img.shape[1] if y2>img.shape[0]: y2=img.shape[0] new_boxes.append

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数据增强之仿射变换

transform # for example, choose red, blue, green points in above wiki image point1 = np.float32([[0,0], [img.shape...[1],0], [0,img.shape[0]]]) point2 = np.float32([[0,0], [img.shape[1],0], [img.shape[0]*np.tan(theta),...img.shape[0]]]) M_shear = cv2.getAffineTransform(point1, point2) # shear equals to M_shear sheared_point

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OpenCV+TensorFlow 人工智能图像处理 (2)

检查最终的结果 import cv2 img = cv2.imread('images/image0.jpg', 1) imgInfo = img.shape print (imgInfo) height...计算对应的像素 import cv2 import numpy as np img = cv2.imread('images/image0.jpg', 1) imgInfo = img.shape height...dst = np.zeros(img.shape, np.uint8) height, width = imgInfo[0], imgInfo[1] for i in range(0, height)...图片缩放 # 缩放1倍 import cv2 import numpy as np img = cv2.imread('images/image0.jpg', 1) imgInfo = img.shape...仿射变换 import cv2 import numpy as np img = cv2.imread('images/image0.jpg', 1) imgInfo = img.shape height

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计算机视觉系列案例 | 基于YOLOv3及Sort算法实现目标跟踪

# 读取图像大小等数据 img = np.array(pilimg) pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape...)) pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape)) unpad_h = img_size - pad_y...* img.shape[0]) x1 = int(((x1 - pad_x // 2) / unpad_w) * img.shape[1]) # 设定检测框的颜色 color...[0] - img.shape[1], 0) * (img_size / max(img.shape)) pad_y = max(img.shape[1] - img.shape[0], 0)...// 2) / unpad_h) * img.shape[0]) x1 = int(((x1 - pad_x // 2) / unpad_w) * img.shape[1])

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心形模糊

)*scale)*((-y+center_y)*scale)+5*((-y+center_y)*scale)**2<128#心形模糊 return mask mask_img=np.ones(img.shape...kernel_size)) for face in face_list: box=face["box"] x,y,w,h=box#边界框 mask=generate_mask(img.shape...[0],img.shape[1],max(w,h)/2,x+w/2,y+h/2) mask_img[mask]=[0,0,0] mask_img_verse=np.ones(img.shape,

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Python OpenCV浅析3种滤镜效果

关键代码如下所示： row = img.shape[0] col = img.shape[1] 根据灰度图像中像素的行数和列数，创建一个等高、等宽的，纯黑色的画布。...fuDiao()方法的代码如下所示： def fuDiao(img): kernel = np.array([[1, 0], [0, -1]]) row = img.shape...关键代码如下所示： row = img.shape[0] col = img.shape[1] 根据灰度图像中像素的行数和列数，创建一个等高、等宽的，纯黑色的画布。...关键代码如下所示： row = img.shape[0] col = img.shape[1] channel = img.shape[2] 根据目标图像中像素的行数和列数以及目标图像的通道数，创建一个等高...tuTouJing()方法的代码如下所示： def tuTouJing(img): row = img.shape[0] col = img.shape[1]

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点击加载更多

python3使用cv2对图像进行基本操作

Python opencv 图像特效处理

python3使用cv2对图像进行基本操作

opencv算法（持续更新）

用PyTorch做物体检测和追踪

图像的表示和可视化

计算机视觉（1）：数字图像的存储结构

最大值灰度化

【OpenCV】Chapter7.图像噪声与滤波器

加权平均值灰度化

图像中的裂纹检测

图像中的裂纹检测

平均值灰度化

使用 Python 和 OpenCV 进行数据增广

使用Python+OpenCV进行数据增广方法综述（附代码演练）

数据增强之仿射变换

OpenCV+TensorFlow 人工智能图像处理 (2)

计算机视觉系列案例 | 基于YOLOv3及Sort算法实现目标跟踪

心形模糊

Python OpenCV浅析3种滤镜效果

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