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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,102 @@ | ||
| #!/usr/bin/env python3 | ||
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| from ros_compatibility.node import CompatibleNode | ||
| import ros_compatibility as roscomp | ||
| import torch | ||
| from torchvision.models.segmentation import deeplabv3_resnet101 | ||
| from torchvision.models.segmentation import DeepLabV3_ResNet101_Weights | ||
| import torchvision.transforms as t | ||
| import cv2 | ||
| from rospy.numpy_msg import numpy_msg | ||
| from sensor_msgs.msg import Image | ||
| from cv_bridge import CvBridge | ||
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| class PerceptionTestingNode(CompatibleNode): | ||
| def __init__(self, name, **kwargs): | ||
| # starting comment | ||
|
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| super().__init__(name, **kwargs) | ||
| # self.model = torch.hub.load('pytorch/vision:v0.10.0', | ||
| # 'deeplabv3_resnet50', pretrained=True) | ||
|
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| self.model = deeplabv3_resnet101(DeepLabV3_ResNet101_Weights) | ||
| # self.model.eval() | ||
| # print("Model Test: ", self.model(torch.zeros((1,3,720,1280)))) | ||
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| self.bridge = CvBridge() | ||
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| self.role_name = self.get_param("role_name", "hero") | ||
| self.side = self.get_param("side", "Center") | ||
| self.setup_camera_subscriptions() | ||
| self.setup_camera_publishers() | ||
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| def setup_camera_subscriptions(self): | ||
| self.new_subscription( | ||
| msg_type=numpy_msg(Image), | ||
| callback=self.handle_camera_image, | ||
| topic=f"/carla/{self.role_name}/{self.side}/image", | ||
| qos_profile=1 | ||
| ) | ||
|
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| def setup_camera_publishers(self): | ||
| self.publisher = self.new_publisher( | ||
| msg_type=numpy_msg(Image), | ||
| topic=f"/paf/{self.role_name}/{self.side}/segmented_image", | ||
| qos_profile=1 | ||
| ) | ||
|
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| def handle_camera_image(self, image): | ||
| self.model.eval() | ||
| self.loginfo(f"got image from camera {self.side}") | ||
|
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| cv_image = self.bridge.imgmsg_to_cv2(img_msg=image, | ||
| desired_encoding='passthrough') | ||
| cv_image = cv2.cvtColor(cv_image, cv2.COLOR_RGB2BGR) | ||
| """ | ||
| image_array = np.frombuffer(image.data, dtype=np.uint8) | ||
| print(image_array.shape) | ||
| image_array = image_array.reshape((image.height, image.width, -1)) | ||
| print(image_array.shape) | ||
| # remove alpha channel | ||
| image_array = image_array[:, :, :3] | ||
| print(image_array.shape)""" | ||
|
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| preprocess = t.Compose([ | ||
| t.ToTensor(), | ||
| t.Normalize(mean=[0.485, 0.456, 0.406], | ||
| std=[0.229, 0.224, 0.225]) | ||
| ]) | ||
| input_image = preprocess(cv_image).unsqueeze(dim=0) | ||
| prediction = self.model(input_image)['out'][0] | ||
| # prediction = id2rgb(prediction) | ||
| # print(prediction) | ||
| print(prediction.shape) | ||
|
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| masked_image = self.create_mask(prediction, input_image) | ||
| self.publisher.publish(self.bridge.cv2_to_imgmsg(masked_image)) | ||
|
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| pass | ||
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| def create_mask(self, model_output, input_image): | ||
| palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1]) | ||
| colors = torch.as_tensor([i for i in range(21)])[:, None] * palette | ||
| colors = (colors % 255).numpy().astype("uint8") | ||
| r = Image.fromarray(model_output.byte().cpu().numpy()) | ||
| r = r.resize(input_image.shape[2], input_image.shape[3]) | ||
| r.putpalette(colors) | ||
| return r | ||
|
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| def run(self): | ||
| self.spin() | ||
| pass | ||
| # while True: | ||
| # self.spin() | ||
|
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| if __name__ == "__main__": | ||
| roscomp.init("PerceptionTestingNode") | ||
| # try: | ||
|
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| node = PerceptionTestingNode("PerceptionTestingNode") | ||
| node.run() |
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