jobs_img = cv2.imread(jobs_image_path, cv2.IMREAD_COLOR) cv2.imshow("jobs_img", jobs_img) obama_img = cv2.imread(obama_image_path, cv2.IMREAD_COLOR) cv2.imshow("obama_img", obama_img) jobs_landmark = get_landmark(jobs_img) obama_landmark = get_landmark(obama_img) transformation_...
In the right column I insert an image of a t-shirt and would like to overlay the image with a small red box that marks a hole in the t-shirt. The coordinates of the box are given in a list that works with the original image size. (I've tested this usingp...
label_image=measure.label(cleared)# 连通区域标记 connectivity=1 # 4连通区域标记 # image_label_overlay = color.label2rgb(label_image) # 不同标记用不同颜色显示 # plt.imshow(image_label_overlay, interpolation='nearest') # plt.show() borders=np.logical_xor(bw, cleared)# 异或,去除背景 label_i...
ax0.imshow(cleared,plt.cm.gray) ax1.imshow(image_label_overlay) for region in measure.regionprops(label_image): #循环得到每一个连通区域属性集 #忽略小区域 if region.area < 100: continue #绘制外包矩形 minr, minc, maxr, maxc = region.bbox rect = mpatches.Rectangle((minc, minr), max...
overlay = cv2.imread('overlay.png', cv2.IMREAD_UNCHANGED) # 设置叠加图像在背景图像上的坐标 x, y = 50, 50 # 将叠加图像缩小一倍后叠加到背景图像上 result = overlay_image(background, overlay, x, y) # 显示结果并保存 cv2.imshow('Result', result) ...
img_blended, (int(wi * rf), int(hi * rf)), interpolation=cv2.INTER_CUBIC) # display images cv2.imshow("Original Image", img_sm) cv2.imshow("Insert This Image", img_insert_sm) cv2.imshow("Blended Images", img_blended_sm) # ...omitted code...
image = imread('../images/chess_football.png') # RGB imageimage_gray = rgb2gray(image)coordinates = corner_harris(image_gray, k =0.001)image[coordinates>0.01*coordinates.max()]=[255,0,0,255]pylab.figure(figsize=(20,10))pylab.imshow(image), pylab.axis('off'), pylab.show() 下一个屏...
from scipy.ndimage.morphology import binary_fill_holesim = rgb2gray(imread('../images/text1.png'))im[im <= 0.5] = 0im[im > 0.5] = 1pylab.figure(figsize=(20,15))pylab.subplot(221), pylab.imshow(im), pylab.title('original', size=20),pylab.axis('off')i = 2for n in [3,5,...
cv2.imshow(img_path+str(index), img) face_descriptor = face_rec_model.compute_face_descriptor(img2, shape) # 计算人脸的128维的向量 print(face_descriptor) k = cv2.waitKey(0) cv2.destroyAllWindows() 1 2 3 4 5 6 7 8 9 10
cv2.imshow("Frame", img) cv2.waitKey(0) cv2.destroyAllWindows() And here is the result of blue squares to blue triangles: And for both changes as in your example: If the sizes of the two templates are equal (i.e. the image you look for and the one it will be replace with)...