Image processing method and apparatus
An image processing method includes: acquiring a video stream of a vehicle by a camera according to a user instruction; obtaining an image corresponding to a frame in the video stream; determining whether the image meets a predetermined criterion by inputting the image into a classification model, the classification model comprising a first convolutional neural network; in response to the image meeting the predetermined criterion, adding at least one of a target box or target segmentation information to the image by inputting the image into a target detection and segmentation model, the at least one of the target box or the target segmentation information corresponding to at least one of a vehicle part or vehicle damage of the vehicle, the target detection and segmentation model comprising a convolutional neural network; and displaying the at least one of the target box or the target segmentation information to the user.
1. An image processing method for use in a mobile device, comprising:
obtaining an image corresponding to a frame in a video stream of a vehicle;
inputting the image into a classification model and determining, based on a classification result, whether the image meets a predetermined criterion, the classification model comprising a first convolutional neural network for use in the mobile device;
in response to the image meeting the predetermined criterion, adding at least one of a target box or target segmentation information to the image, the at least one of the target box or the target segmentation information corresponding to at least one of a vehicle part or vehicle damage of the vehicle; and
displaying to a user the at least one of the target box or the target segmentation information.
2. The image processing method of claim 1 , further comprising:
in response to the image not meeting the predetermined criterion, prompting the user based on the classification result from the classification model.
3. The image processing method of claim 1 , wherein the classification model classifies the image based on at least one of: whether the image is blurred, whether the image includes vehicle damage, whether a light intensity is sufficient, whether a shooting angle is skewed, or whether a shooting distance is appropriate.
4. The image processing method of claim 1 , further comprising:
presenting a shooting flow to the user before the video stream of the vehicle is acquired by a camera of the mobile device.
5. The image processing method of claim 1 , further comprising:
prompting the user based on the at least one of the target box or the target segmentation information.
6. The image processing method of claim 5 , wherein the prompting the user based on the at least one of the target box or the target segmentation information comprises:
prompting the user to move forward or backward based on the at least one of the target box or the target segmentation information.
7. The image processing method of claim 5 , wherein the prompting the user based on the at least one of the target box or the target segmentation information comprises:
prompting the user to shoot based on the at least one of the target box or the target segmentation information, to obtain a damage assessment photo corresponding to the image of the frame.
8. The image processing method of claim 7 , further comprising:
uploading the damage assessment photo to a server.
9. The image processing method of claim 7 , further comprising:
obtaining, based on the video stream, an association between the image and a first image, wherein the first image is an image of a first frame before the frame in the video stream.
10. The image processing method of claim 9 , wherein the association comprises at least one of: an optical flow, a mapping matrix, or a position and angle transformation relation between the image and the first image.
11. The image processing method of claim 9 , further comprising:
uploading the association to the server.
12. The image processing method of claim 1 , wherein the adding at least one of a target box or target segmentation information to the image comprises inputting the image into a target detection and segmentation model, the target detection and segmentation model including a second convolutional neural network for use in the mobile device,
wherein the first convolutional neural network and the second convolutional neural network are a same convolutional neural network shared by the classification model and the target detection and segmentation model.
13. A mobile device, comprising:
a memory storing instructions; and
a processor configured to execute the instructions to:
obtain an image corresponding to a frame in a video stream of a vehicle;
input the image into a classification model and determine, based on a classification result, whether the image meets a predetermined criterion, the classification model comprising a first convolutional neural network for use in the mobile device;
if the image meets the predetermined criterion, add at least one of a target box or target segmentation information to the image, the at least one of the target box or the target segmentation information corresponding to at least one of a vehicle part or vehicle damage of the vehicle; and
display to a user the at least one of the target box or the target segmentation information.
14. The mobile device of claim 13 , wherein the processor is further configured to execute the instructions to:
in response to the image not meeting the predetermined criterion, prompt the user based on the classification result from the classification model.
15. The mobile device of claim 13 , wherein the classification model classifies the image based on at least one of: whether the image is blurred, whether the image includes vehicle damage, whether a light intensity is sufficient, whether a shooting angle is skewed, or whether a shooting distance is appropriate.
16. The mobile device of claim 13 , wherein the processor is further configured to execute the instructions to:
present a shooting flow to the user before the video stream of the vehicle is acquired by a camera of the mobile device.
17. The mobile device of claim 13 , wherein the processor is further configured to execute the instructions to:
prompt the user based on the at least one of the target box or the target segmentation information.
18. The mobile device of claim 17 , wherein the prompting the user based on the at least one of the target box or the target segmentation information comprises:
prompting the user to move forward or backward based on the at least one of the target box or the target segmentation information.
19. The mobile device of claim 17 , wherein the prompting the user based on the at least one of the target box or the target segmentation information comprises:
prompting the user to shoot based on the at least one of the target box or the target segmentation information, to obtain a damage assessment photo corresponding to the image of the frame.
20. A non-transitory computer-readable medium storing instructions that, when executed by a processor of a mobile device, cause the mobile device to perform an image processing method, the method comprising:
obtaining an image corresponding to a frame in a video stream of a vehicle;
inputting the image into a classification model and determining, based on a classification result, whether the image meets a predetermined criterion, the classification model comprising a first convolutional neural network;
if the image meets the predetermined criterion, adding at least one of a target box or target segmentation information to the image, the at least one of the target box or the target segmentation information corresponding to at least one of a vehicle part or vehicle damage of the vehicle; and
displaying to a user the at least one of the target box or the target segmentation information.