Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 
 
Alexander Alekhin b19f860384 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 4 years ago
..
dnn_model_runner/dnn_conversion Merge pull request #17604 from LupusSanctus:am/pytorch_tf_cls_tutorial 4 years ago
face_detector Merge pull request #18591 from sl-sergei:download_utilities 4 years ago
.gitignore Merge pull request #18591 from sl-sergei:download_utilities 4 years ago
CMakeLists.txt Merge pull request #16150 from alalek:cmake_avoid_deprecated_link_private 5 years ago
README.md Merge remote-tracking branch 'upstream/3.4' into merge-3.4 4 years ago
action_recognition.py Merge pull request #14627 from l-bat:demo_kinetics 6 years ago
classification.cpp Merge pull request #17604 from LupusSanctus:am/pytorch_tf_cls_tutorial 4 years ago
classification.py Merge pull request #17604 from LupusSanctus:am/pytorch_tf_cls_tutorial 4 years ago
colorization.cpp samples: use findFile() in dnn 6 years ago
colorization.py Make Intel's Inference Engine backend is default if no preferable backend is specified. 7 years ago
common.hpp Merge remote-tracking branch 'upstream/3.4' into merge-3.4 6 years ago
common.py samples: use findFile() in dnn 6 years ago
custom_layers.hpp Merge pull request #12264 from dkurt:dnn_remove_forward_method 6 years ago
dasiamrpn_tracker.cpp Merge pull request #19078 from zihaomu:dasiamrpn_tracker_c_plus_plus 4 years ago
dasiamrpn_tracker.py Merge pull request #18033 from ieliz:dasiamrpn 4 years ago
download_models.py Merge pull request #18591 from sl-sergei:download_utilities 4 years ago
edge_detection.py Fix edge_detection.py sample for Python 3 6 years ago
fast_neural_style.py fix pylint warnings 5 years ago
human_parsing.cpp dnn: add a human parsing cpp sample 5 years ago
human_parsing.py Merge pull request #18220 from Omar-AE:hddl-supported 4 years ago
js_face_recognition.html Merge remote-tracking branch 'upstream/3.4' into merge-3.4 5 years ago
mask_rcnn.py Merge pull request #17394 from huningxin:fix_segmentation_py 5 years ago
mobilenet_ssd_accuracy.py fix pylint warnings 5 years ago
models.yml Merge pull request #18591 from sl-sergei:download_utilities 4 years ago
object_detection.cpp Merge remote-tracking branch 'upstream/3.4' into merge-3.4 5 years ago
object_detection.py Merge pull request #18220 from Omar-AE:hddl-supported 4 years ago
openpose.cpp Fix openpose samples 6 years ago
openpose.py FIx misc. source and comment typos 5 years ago
optical_flow.py support flownet2 with arbitary input size 4 years ago
person_reid.cpp sample of person ReIDentification. 4 years ago
person_reid.py sample of person ReIDentification. 4 years ago
scene_text_detection.cpp samples: replace regex 4 years ago
scene_text_recognition.cpp Merge pull request #17570 from HannibalAPE:text_det_recog_demo 4 years ago
scene_text_spotting.cpp Merge pull request #17570 from HannibalAPE:text_det_recog_demo 4 years ago
segmentation.cpp Merge remote-tracking branch 'upstream/3.4' into merge-3.4 6 years ago
segmentation.py Merge pull request #18220 from Omar-AE:hddl-supported 4 years ago
shrink_tf_graph_weights.py Text TensorFlow graphs parsing. MobileNet-SSD for 90 classes. 7 years ago
siamrpnpp.py fixes #18613 4 years ago
text_detection.cpp Merge pull request #17570 from HannibalAPE:text_det_recog_demo 4 years ago
text_detection.py add OpenCV sample for digit and text recongnition, and provide multiple OCR models. 4 years ago
tf_text_graph_common.py Merge pull request #19417 from LupusSanctus:am/text_graph_identity 4 years ago
tf_text_graph_efficientdet.py dnn: EfficientDet 5 years ago
tf_text_graph_faster_rcnn.py StridedSlice from TensorFlow 6 years ago
tf_text_graph_mask_rcnn.py Enable ResNet-based Mask-RCNN models from TensorFlow Object Detection API 6 years ago
tf_text_graph_ssd.py Corrected SSD text graph generation 4 years ago
virtual_try_on.py Merge remote-tracking branch 'upstream/3.4' into merge-3.4 4 years ago

README.md

OpenCV deep learning module samples

Model Zoo

Check a wiki for a list of tested models.

If OpenCV is built with Intel's Inference Engine support you can use Intel's pre-trained models.

There are different preprocessing parameters such mean subtraction or scale factors for different models. You may check the most popular models and their parameters at models.yml configuration file. It might be also used for aliasing samples parameters. In example,

python object_detection.py opencv_fd --model /path/to/caffemodel --config /path/to/prototxt

Check -h option to know which values are used by default:

python object_detection.py opencv_fd -h

Sample models

You can download sample models using download_models.py. For example, the following command will download network weights for OpenCV Face Detector model and store them in FaceDetector folder:

python download_models.py --save_dir FaceDetector opencv_fd

You can use default configuration files adopted for OpenCV from here.

You also can use the script to download necessary files from your code. Assume you have the following code inside your_script.py:

from download_models import downloadFile

filepath1 = downloadFile("https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc", None, filename="MobileNetSSD_deploy.caffemodel", save_dir="save_dir_1")
filepath2 = downloadFile("https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc", "994d30a8afaa9e754d17d2373b2d62a7dfbaaf7a", filename="MobileNetSSD_deploy.caffemodel")
print(filepath1)
print(filepath2)
# Your code

By running the following commands, you will get MobileNetSSD_deploy.caffemodel file:

export OPENCV_DOWNLOAD_DATA_PATH=download_folder
python your_script.py

Note that you can provide a directory using save_dir parameter or via OPENCV_SAVE_DIR environment variable.

Face detection

An origin model with single precision floating point weights has been quantized using TensorFlow framework. To achieve the best accuracy run the model on BGR images resized to 300x300 applying mean subtraction of values (104, 177, 123) for each blue, green and red channels correspondingly.

The following are accuracy metrics obtained using COCO object detection evaluation tool on FDDB dataset (see script) applying resize to 300x300 and keeping an origin images' sizes.

AP - Average Precision                            | FP32/FP16 | UINT8          | FP32/FP16 | UINT8          |
AR - Average Recall                               | 300x300   | 300x300        | any size  | any size       |
--------------------------------------------------|-----------|----------------|-----------|----------------|
AP @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.408     | 0.408          | 0.378     | 0.328 (-0.050) |
AP @[ IoU=0.50      | area=   all | maxDets=100 ] | 0.849     | 0.849          | 0.797     | 0.790 (-0.007) |
AP @[ IoU=0.75      | area=   all | maxDets=100 ] | 0.251     | 0.251          | 0.208     | 0.140 (-0.068) |
AP @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.050     | 0.051 (+0.001) | 0.107     | 0.070 (-0.037) |
AP @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.381     | 0.379 (-0.002) | 0.380     | 0.368 (-0.012) |
AP @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.455     | 0.455          | 0.412     | 0.337 (-0.075) |
AR @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] | 0.299     | 0.299          | 0.279     | 0.246 (-0.033) |
AR @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] | 0.482     | 0.482          | 0.476     | 0.436 (-0.040) |
AR @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.496     | 0.496          | 0.491     | 0.451 (-0.040) |
AR @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.189     | 0.193 (+0.004) | 0.284     | 0.232 (-0.052) |
AR @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.481     | 0.480 (-0.001) | 0.470     | 0.458 (-0.012) |
AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528     | 0.528          | 0.520     | 0.462 (-0.058) |

References