diff options
Diffstat (limited to 'cv/seg')
| -rw-r--r-- | cv/seg/human_seg.py | 33 | ||||
| -rw-r--r-- | cv/seg/img2video.py | 30 | ||||
| -rw-r--r-- | cv/seg/utils.py | 128 | ||||
| -rw-r--r-- | cv/seg/video_seg/processing.py | 58 | ||||
| -rwxr-xr-x | cv/seg/video_seg/vtest.avi | bin | 0 -> 8131690 bytes |
5 files changed, 249 insertions, 0 deletions
diff --git a/cv/seg/human_seg.py b/cv/seg/human_seg.py new file mode 100644 index 0000000..106b5c7 --- /dev/null +++ b/cv/seg/human_seg.py @@ -0,0 +1,33 @@ +# 1、导入模块 +import os +import paddlehub as hub +import numpy as np +from collections import Counter +import cv2 +import matplotlib.pyplot as plt + + +path = './data/' +files = os.listdir(path) +imgs = [] + + +for i in files: + imgs.append(path + i) + +cv2.imshow('', cv2.imread(imgs[0])) + + + + +# 2、加载模型 +humanseg = hub.Module(name='deeplabv3p_xception65_humanseg') + + + +results = humanseg.segmentation(data={'image':imgs}, visualization=True) +unique, counts = np.unique(results[0]['data'], return_counts=True) +print(dict(zip(unique, counts))) + +print() + diff --git a/cv/seg/img2video.py b/cv/seg/img2video.py new file mode 100644 index 0000000..c824c9c --- /dev/null +++ b/cv/seg/img2video.py @@ -0,0 +1,30 @@ +import cv2 +import os +from tqdm import tqdm + +image_folder = './video_seg/v2j' +video_name = 'video.avi' + +images = [img for img in os.listdir(image_folder) if img.endswith(".jpg")] +frame = cv2.imread(os.path.join(image_folder, images[0])) +height, width, layers = frame.shape + +video = cv2.VideoWriter(video_name, 0, 10, (width,height)) +images.sort(key=lambda s: int(s.split('.')[0].split('-')[1])) +for image in tqdm(images): + video.write(cv2.imread(os.path.join(image_folder, image))) + +cv2.destroyAllWindows() +video.release() + + +# import os +# import moviepy.video.io.ImageSequenceClip +# image_folder = './video_seg/v2j' +# fps = 10 +# +# image_files = [os.path.join(image_folder,img) +# for img in os.listdir(image_folder) +# if img.endswith(".jpg")] +# clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=fps) +# clip.write_videofile('my_video.mp4')
\ No newline at end of file diff --git a/cv/seg/utils.py b/cv/seg/utils.py new file mode 100644 index 0000000..0329c84 --- /dev/null +++ b/cv/seg/utils.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python + +""" +Function for interactively selecting part of an array displayed as an image with matplotlib. +""" + +import matplotlib.pyplot as plt +from matplotlib import is_interactive +from matplotlib.path import Path +from matplotlib.widgets import LassoSelector, RectangleSelector +import numpy as np + + +def path_bbox(p): + """ + Return rectangular bounding box of given path. + Parameters + ---------- + p : array_like + Array of vertices with shape Nx2. + Returns + ------- + bbox : array_like + Array of bounding box vertices with shape 4x2. + """ + + assert p.ndim == 2 + assert p.shape[1] == 2 + + ix_min = p[:, 0].argmin() + ix_max = p[:, 0].argmax() + iy_min = p[:, 1].argmin() + iy_max = p[:, 1].argmax() + + return np.array([[p[ix_min, 0], p[iy_min, 1]], + [p[ix_min, 0], p[iy_max, 1]], + [p[ix_max, 0], p[iy_max, 1]], + [p[ix_max, 0], p[iy_min, 1]]]) + + +def imshow_select(data, selector='lasso', bbox=False): + """ + Display array as image with region selector. + + Parameters + ---------- + data : array_like + Array to display. + selector : str + Region selector. For `lasso`, use `LassoSelector`; for `rectangle`, + use `RectangleSelector`. + bbox : bool + If True, only return array within rectangular bounding box of selected region. + Otherwise, return array with same dimensions as `data` such that selected region + contains the corresponding values from `data` and the remainder contains 0. + Returns + ------- + region : array_like + Data for selected region. + mask : array_like + Boolean mask with same shape of `data` for selecting the returned region from `data`. + """ + + interactive = is_interactive() + if not interactive: + plt.ion() + fig = plt.figure() + ax = fig.gca() + ax.imshow(data) + + x, y = np.meshgrid(np.arange(data.shape[1], dtype=int), + np.arange(data.shape[0], dtype=int)) + pix = np.vstack((x.flatten(), y.flatten())).T + + # Store data in dict value to permit overwriting by nested + # functions in Python 2.7: + selected = {} + selected['data'] = np.zeros_like(data) + selected['mask'] = np.tile(False, data.shape) + + def _onselect_lasso(verts): + verts = np.array(verts) + p = Path(verts) + ind = p.contains_points(pix, radius=1) + selected['data'].flat[ind] = data.flat[ind] + selected['mask'].flat[ind] = True + if bbox: + b = path_bbox(verts) + selected['data'] = selected['data'][int(min(b[:, 1])):int(max(b[:, 1])), + int(min(b[:, 0])):int(max(b[:, 0]))] + + def _onselect_rectangle(start, end): + verts = np.array([[start.xdata, start.ydata], + [start.xdata, end.ydata], + [end.xdata, end.ydata], + [end.xdata, start.ydata]], int) + p = Path(verts) + ind = p.contains_points(pix, radius=1) + selected['data'].flat[ind] = data.flat[ind] + selected['mask'].flat[ind] = True + if bbox: + b = path_bbox(verts) + selected['data'] = selected['data'][min(b[:, 1]):max(b[:, 1]), + min(b[:, 0]):max(b[:, 0])] + + name_to_selector = {'lasso': LassoSelector, + 'rectangle': RectangleSelector} + selector = name_to_selector[selector] + onselect_dict = {LassoSelector: _onselect_lasso, + RectangleSelector: _onselect_rectangle} + kwargs_dict = {LassoSelector: {}, + RectangleSelector: {'interactive': True}} + + lasso = selector(ax, onselect_dict[selector], **kwargs_dict[selector]) + input('Press Enter when done') + lasso.disconnect_events() + if not interactive: + plt.ioff() + return selected['data'], selected['mask'] + + +if __name__ == '__main__': + from skimage.data import coins + + data = coins() + selected, mask = imshow_select(data, 'lasso', True) + plt.imsave('selected.png', selected) + plt.imsave('mask.png', mask)
\ No newline at end of file diff --git a/cv/seg/video_seg/processing.py b/cv/seg/video_seg/processing.py new file mode 100644 index 0000000..eeaa6bb --- /dev/null +++ b/cv/seg/video_seg/processing.py @@ -0,0 +1,58 @@ +import paddlehub as hub +from PIL import Image +import os +import cv2 +import matplotlib.pyplot as plt +import matplotlib.image as mpimg +import numpy as np + + +def video2jpg(video_file, output_path): + ''' + 将视频文件video_file每一帧转成图片保存到output_path文件夹 + ''' + try: + os.makedirs(output_path) # 创建输出文件夹 + except: + print() + + # 读取视频文件 + cap = cv2.VideoCapture(video_file) + n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + num = 0 + while True: + ret, frame = cap.read() + if ret: + cv2.imwrite('{}/{}-{}.jpg'.format(output_path, n_frames, num), frame) + num += 1 + else: + break + cap.release() # 关闭视频 + + +def humanseg(images): + + + # 装载模型 + module = hub.Module(name="deeplabv3p_xception65_humanseg") + # 执行模型segmentation(抠图)命令 + module.segmentation(data={"image": images}, visualization=True) + + # for i, img in enumerate(images): + # print(i, img) + # result = module.segmentation(data={"image": [img]}, visualization=True) + + +def file_list(listdir): + im_list = [] + imgs = os.listdir(listdir) + for img in imgs: + im_list.append(os.path.join(listdir, img)) + return im_list + + +if __name__ == '__main__': + # video2jpg('vtest.avi', 'v2j') + + img_list = file_list('./v2j') + humanseg(img_list) diff --git a/cv/seg/video_seg/vtest.avi b/cv/seg/video_seg/vtest.avi Binary files differnew file mode 100755 index 0000000..965ab12 --- /dev/null +++ b/cv/seg/video_seg/vtest.avi |