智能机器人实验——机械臂画画

实验器材Dobot机械臂笔小型摄像头电脑软件白纸项目描述创意来源前段时间有个团队实现了通过手机拍照将照片传到机器中机器实现画图。于是想着通过Dobot机械臂实现同样甚至更好的功能。在实验指导书上看到Dobot机械臂能够简单的进行写字就联想到能够用它来画画由摄像头实时拍摄对象同时对显示的图像进行卡通化与素描化展示较好的效果最后输出图像到机械臂上让机械臂作图。实现功能1、通过python opencv包调用摄像头实现摄像头的图像实现2、对于每一帧的视频图像进行卡通化与素描化处理3、通过键盘控制摄像头进行拍照并将照片保存4、编写程序将图片从jpg格式转成svg矢量图格式将转化出的矢量图导入dobot模块控制机械臂绘图。算法实现1、图片卡通化算法def cartoon(image): gray cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray cv2.medianBlur(gray, 7) edges cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 10) color cv2.bilateralFilter(image, 12, 250, 250) cartoon cv2.bitwise_and(color, color, maskedges) return cartoon2、图片素描化算法def sumiao(image): cartoon_image1, cartoon_image2 cv2.pencilSketch(image, sigma_s60, sigma_r0.15, shade_factor0.05) return cartoon_image13、图片格式转化算法def bitmap_to_contour_svg(input_bitmap_path: str, output_svg_path: str): img cv2.imread(input_bitmap_path) dump_rings_from_image(img, output_pathoutput_svg_path) remove_matplotlib_background(output_svg_path)4、拍摄及主程序if cap.isOpened() is True: # 检查摄像头是否正常启动 while (True): # 获取摄像头拍摄到的画面 ret, frame cap.read() img frame cartoon1cartoon(img) sumiao1sumiao(img) stylish1stylish(img) # 实时展示效果画面 # cv2.imshow(frame1, frame) cv2.imshow(frame2,stylish1) cv2.imshow(frame, sumiao1) # 每5毫秒监听一次键盘动作 if cv2.waitKey(5) 0xFF ord(p): cv2.imwrite(pic_%d.jpg%i,stylish1) i 1 bmp_to_svg.bitmap_to_contour_svg(input_bitmap_pathpic_0.jpg, output_svg_pathpictuce_0.svg) if cv2.waitKey(5) 0xFF ord(q): break # 最后关闭所有窗口 cap.release() cv2.destroyAllWindows() else: print(cap is not opened!)呈现效果完整代码import cv2 import bmp_to_svg cap cv2.VideoCapture(0) i0 def cartoon(image): gray cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray cv2.medianBlur(gray, 7) edges cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 10) color cv2.bilateralFilter(image, 12, 250, 250) cartoon cv2.bitwise_and(color, color, maskedges) return cartoon def sumiao(image): cartoon_image1, cartoon_image2 cv2.pencilSketch(image, sigma_s60, sigma_r0.15, shade_factor0.05) return cartoon_image1 def stylish(image): # 风格化 cartoon_image cv2.stylization(image, sigma_s150, sigma_r0.45) return cartoon_image if cap.isOpened() is True: # 检查摄像头是否正常启动 while (True): # 获取摄像头拍摄到的画面 ret, frame cap.read() img frame cartoon1cartoon(img) sumiao1sumiao(img) stylish1stylish(img) # 实时展示效果画面 # cv2.imshow(frame1, frame) cv2.imshow(frame2,stylish1) cv2.imshow(frame, sumiao1) # 每5毫秒监听一次键盘动作 if cv2.waitKey(5) 0xFF ord(p): cv2.imwrite(pic_%d.jpg%i,stylish1) i 1 bmp_to_svg.bitmap_to_contour_svg(input_bitmap_pathpic_0.jpg, output_svg_pathpictuce_0.svg) if cv2.waitKey(5) 0xFF ord(q): break # 最后关闭所有窗口 cap.release() cv2.destroyAllWindows() else: print(cap is not opened!)bmp_to_svg.py文件from typing import Iterable, List, Tuple, Union import cv2 import matplotlib.pyplot as plt import numpy as np from xml.dom import minidom as md from queue import Queue import warnings def look_shape(a: Iterable) - Tuple: # for debug return np.array(a).shape def length_within_points(a: Iterable, empty_value: Union[int, float] 0) - int: a simple instance: array : [empty_value, empty_value, empty_value, 1, empty_value, 0, 1, 2, empty_value] Then length_within_points(array) will return index diff between 1 and 2, which is 5 a list(a) l_pivot, r_pivot -1, -2 for index, (l_val, r_val) in enumerate(zip(a[::1], a[::-1])): if l_val ! empty_value and l_pivot -1: l_pivot index if r_val ! empty_value and r_pivot -2: r_pivot len(a) - index return r_pivot - l_pivot 1 def dump_rings_from_image(image: np.ndarray, output_path: str, plot_dict: dict {color: k, linewidth: 2.0}, default_height: float 8) - List[np.ndarray]: # regular operation, no more explainations blur cv2.GaussianBlur(image, (3, 3), 0) gray cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY) edge cv2.Canny(gray, 50, 150) # get ratio between width and height to adjust the final output valid_width length_within_points(edge.sum(axis0)) valid_height length_within_points(edge.sum(axis1)) true_ratio valid_width / valid_height # get contour of the edge image contour_tuple cv2.findContours(edge, modecv2.RETR_TREE, methodcv2.CHAIN_APPROX_NONE) contours contour_tuple[0] rings [np.array(c).reshape([-1, 2]) for c in contours] # adjust coordinate system to the image coordinate system max_x, max_y, min_x, min_y 0, 0, 0, 0 for ring in rings: max_x max(max_x, ring.max(axis0)[0]) max_y max(max_y, ring.max(axis0)[1]) min_x max(min_x, ring.min(axis0)[0]) min_y max(min_y, ring.min(axis0)[1]) # adjust ratio plt.figure(figsize[default_height * true_ratio, default_height]) # plot to the matplotlib for _, ring in enumerate(rings): close_ring np.vstack((ring, ring[0])) xx close_ring[..., 0] yy max_y - close_ring[..., 1] plt.plot(xx, yy, **plot_dict) plt.axis(off) plt.savefig(output_path) def remove_matplotlib_background(svg_file: str, bg_node_name: str patch_1) - None: dom_tree: md.Document md.parse(svg_file) svg_node None # select the svg tag for node in dom_tree.childNodes: if node.nodeName svg: svg_node: md.Element node if svg_node is None: raise ValueError(not find a svg node in {}.format(svg_file)) # bfs svg node to find the background node q Queue() q.put(svg_node) target_node None # we will remove the target node while not q.empty(): cur: md.Node q.get() if cur.hasChildNodes(): for node in cur.childNodes: q.put(node) if hasattr(cur, getAttribute): # this is the id of the background node if cur.getAttribute(id) bg_node_name: target_node cur if target_node is None: warnings.warn(background node is not found, please ensure whether bg_node_name is correct) else: # remove and write target_node.parentNode.removeChild(target_node) with open(svg_file, w, encodingutf-8) as fp: dom_tree.writexml( writerfp, indent\t ) def bitmap_to_contour_svg(input_bitmap_path: str, output_svg_path: str): img cv2.imread(input_bitmap_path) dump_rings_from_image(img, output_pathoutput_svg_path) remove_matplotlib_background(output_svg_path) if __name__ __main__: bitmap_to_contour_svg( input_bitmap_pathpic_1_0.jpg, output_svg_path5.svg )