使用PixelLib来实现图像分割
小白学视觉
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2022-02-17 19:35
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帮助无人驾驶汽车视觉系统有效的了解道路场景。 医学图像分割:为执行诊断测试提供身体部位的分割。 卫星图像分析。
pip3 install tensorflow
pip3 install opencv-python
pip3 install scikit-image
pip3 install pillow
pip3 install pixellib
import pixellib
from pixellib.semantic import semantic_segmentation
segment_image = semantic_segmentation()
segment_image.load_pascalvoc_model("deeplabv3_xception_tf_dim_ordering_tf_kernels.h5")
segment_image.segmentAsPascalvoc("path_to_image", output_image_name = "path_to_output_image")
import pixellib
from pixellib.semantic import semantic_segmentation
segment_image = semantic_segmentation()
segment_image.load_pascalvoc_model(“deeplabv3_xception_tf_dim_ordering_tf_kernels.h5”)
https://github.com/ayoolaolafenwa/PixelLib/releases/download/1.1/deeplabv3_xception_tf_dim_ordering_tf_kernels.h5
segment_image.segmentAsPascalvoc(“path_to_image”, output_image_name = “path_to_output_image)
path_to_image:这个是要分割的图像路径。 output_image_name:这个是保存分割图像的路径。它将保存在当前工作目录中。
import pixellib
from pixellib.semantic import semantic_segmentation
segment_image = semantic_segmentation()
segment_image.load_pascalvoc_model("deeplabv3_xception_tf_dim_ordering_tf_kernels.h5")
segment_image.segmentAsPascalvoc("sample1.jpg", output_image_name = "image_new.jpg")
segment_image.segmentAsPascalvoc("sample1.jpg", output_image_name = "image_new.jpg", overlay = True)
import pixellib
from pixellib.semantic import semantic_segmentation
import time
segment_image = semantic_segmentation()
segment_image.load_pascalvoc_model("pascal.h5")
start = time.time()
segment_image.segmentAsPascalvoc("sample1.jpg", output_image_name= "image_new.jpg")
end = time.time()
print(f"Inference Time: {end-start:.2f}seconds")
Inference Time: 7.38seconds
output, segmap = segment_image.segmentAsPascalvoc()
import pixellib
from pixellib.semantic import semantic_segmentation
import cv2
segment_image = semantic_segmentation()
segment_image.load_pascalvoc_model("pascal.h5")
output, segmap = segment_image.segmentAsPascalvoc("sample1.jpg")
cv2.imwrite("img.jpg", output)
print(output.shape)
segmap, segoverlay = segment_image.segmentAsPascalvoc(overlay = True)
import pixellibfrom pixellib.semantic import semantic_segmentationimport cv2segment_image = semantic_segmentation()segment_image.load_pascalvoc_model("pascal.h5")segmap, segoverlay = segment_image.segmentAsPascalvoc("sample1.jpg", overlay= True)cv2.imwrite("img.jpg", segoverlay)print(segoverlay.shape)
import pixellib
from pixellib.instance import instance_segmentation
segment_image = instance_segmentation()
segment_image.load_model("mask_rcnn_coco.h5")
segment_image.segmentImage("path_to_image", output_image_name = "output_image_path")
import pixellib
from pixellib.instance import instance_segmentation
segment_image = instance_segmentation()
segment_image.load_model("mask_rcnn_coco.h5")
https://github.com/ayoolaolafenwa/PixelLib/releases/download/1.2/mask_rcnn_coco.h5
segment_image.segmentImage("path_to_image", output_image_name = "output_image_path")
path_to_image:模型要预测的图像路径。 output_image_path:保存分割结果的路径。它将保存在当前工作目录中。
import pixellib
from pixellib.instance import instance_segmentation
segment_image = instance_segmentation()
segment_image.load_model("mask_rcnn_coco.h5")
segment_image.segmentImage("sample2.jpg", output_image_name = "image_new.jpg")
segment_image.segmentImage("path_to_image", output_image_name = "output_image_path", show_bboxes = True)
import pixellib
from pixellib.instance import instance_segmentation
import time
segment_image = instance_segmentation()
segment_image.load_model("mask_rcnn_coco.h5")
start = time.time()
segment_image.segmentImage("former.jpg", output_image_name= "image_new.jpg")
end = time.time()
print(f"Inference Time: {end-start:.2f}seconds")
Inference Time: 12.87seconds
检测到的对象数组 对象对应类的id数组 分割掩码数组 输出的数组
segmask, output = segment_image.segmentImage()
import pixellib
from pixellib.instance import instance_segmentation
import cv2
instance_seg = instance_segmentation()
instance_seg.load_model("mask_rcnn_coco.h5")
segmask, output = instance_seg.segmentImage("sample2.jpg")
cv2.imwrite("img.jpg", output)
print(output.shape)
segmask, output = segment_image.segmentImage(show_bboxes = True)
import pixellib
from pixellib.instance import instance_segmentation
import cv2
instance_seg = instance_segmentation()
instance_seg.load_model("mask_rcnn_coco.h5")
segmask, output = instance_seg.segmentImage("sample2.jpg", show_bboxes= True)
cv2.imwrite("img.jpg", output)
print(output.shape)
https://github.com/ayoolaolafenwa/PixelLib
https://pixellib.readthedocs.io/en/latest/
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