mirror of
https://github.com/opencv/opencv.git
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1f3255d76b
Scripts for downloading models in DNN samples * Initial commit. Utility classes and functions for downloading files * updated download script * Support YAML parsing, update download script and configs * Fix problem with archived files * fix models.yml * Move download utilities to more appropriate place * Fix script description * Update README * update utilities for broader range of files * fix loading with no hashsum provided * remove unnecessary import * fix for Python2 * Add usage examples for downloadFile function * Add more secure cache folder selection * Remove trailing whitespaces * Fix indentation * Update function interface * Change function for temp dir, change entry name in models.yml * Update getCacheDirectory function call * Return python implementation for cache directory selection, use more specific env variable * Fix whitespace
167 lines
5.6 KiB
YAML
167 lines
5.6 KiB
YAML
%YAML 1.0
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---
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################################################################################
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# Object detection models.
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################################################################################
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# OpenCV's face detection network
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opencv_fd:
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load_info:
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url: "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
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sha1: "15aa726b4d46d9f023526d85537db81cbc8dd566"
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model: "opencv_face_detector.caffemodel"
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config: "opencv_face_detector.prototxt"
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mean: [104, 177, 123]
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scale: 1.0
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width: 300
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height: 300
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rgb: false
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sample: "object_detection"
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# YOLO4 object detection family from Darknet (https://github.com/AlexeyAB/darknet)
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# YOLO object detection family from Darknet (https://pjreddie.com/darknet/yolo/)
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# Might be used for all YOLOv2, TinyYolov2, YOLOv3, YOLOv4 and TinyYolov4
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yolo:
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load_info:
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url: "https://pjreddie.com/media/files/yolov3.weights"
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sha1: "520878f12e97cf820529daea502acca380f1cb8e"
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model: "yolov3.weights"
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config: "yolov3.cfg"
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mean: [0, 0, 0]
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scale: 0.00392
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width: 416
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height: 416
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rgb: true
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classes: "object_detection_classes_yolov3.txt"
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sample: "object_detection"
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tiny-yolo-voc:
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load_info:
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url: "https://pjreddie.com/media/files/yolov2-tiny-voc.weights"
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sha1: "24b4bd049fc4fa5f5e95f684a8967e65c625dff9"
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model: "tiny-yolo-voc.weights"
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config: "tiny-yolo-voc.cfg"
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mean: [0, 0, 0]
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scale: 0.00392
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width: 416
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height: 416
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rgb: true
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classes: "object_detection_classes_pascal_voc.txt"
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sample: "object_detection"
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# Caffe implementation of SSD model from https://github.com/chuanqi305/MobileNet-SSD
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ssd_caffe:
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load_info:
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url: "https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc"
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sha1: "994d30a8afaa9e754d17d2373b2d62a7dfbaaf7a"
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model: "MobileNetSSD_deploy.caffemodel"
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config: "MobileNetSSD_deploy.prototxt"
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mean: [127.5, 127.5, 127.5]
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scale: 0.007843
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width: 300
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height: 300
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rgb: false
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classes: "object_detection_classes_pascal_voc.txt"
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sample: "object_detection"
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# TensorFlow implementation of SSD model from https://github.com/tensorflow/models/tree/master/research/object_detection
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ssd_tf:
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load_info:
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url: "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz"
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sha1: "9e4bcdd98f4c6572747679e4ce570de4f03a70e2"
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download_sha: "6157ddb6da55db2da89dd561eceb7f944928e317"
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download_name: "ssd_mobilenet_v1_coco_2017_11_17.tar.gz"
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member: "ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb"
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model: "ssd_mobilenet_v1_coco_2017_11_17.pb"
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config: "ssd_mobilenet_v1_coco_2017_11_17.pbtxt"
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mean: [0, 0, 0]
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scale: 1.0
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width: 300
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height: 300
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rgb: true
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classes: "object_detection_classes_coco.txt"
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sample: "object_detection"
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# TensorFlow implementation of Faster-RCNN model from https://github.com/tensorflow/models/tree/master/research/object_detection
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faster_rcnn_tf:
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load_info:
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url: "http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz"
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sha1: "f2e4bf386b9bb3e25ddfcbbd382c20f417e444f3"
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download_sha: "c710f25e5c6a3ce85fe793d5bf266d581ab1c230"
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download_name: "faster_rcnn_inception_v2_coco_2018_01_28.tar.gz"
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member: "faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb"
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model: "faster_rcnn_inception_v2_coco_2018_01_28.pb"
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config: "faster_rcnn_inception_v2_coco_2018_01_28.pbtxt"
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mean: [0, 0, 0]
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scale: 1.0
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width: 800
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height: 600
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rgb: true
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sample: "object_detection"
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################################################################################
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# Image classification models.
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################################################################################
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# SqueezeNet v1.1 from https://github.com/DeepScale/SqueezeNet
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squeezenet:
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load_info:
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url: "https://raw.githubusercontent.com/DeepScale/SqueezeNet/b5c3f1a23713c8b3fd7b801d229f6b04c64374a5/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel"
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sha1: "3397f026368a45ae236403ccc81cfcbe8ebe1bd0"
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model: "squeezenet_v1.1.caffemodel"
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config: "squeezenet_v1.1.prototxt"
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mean: [0, 0, 0]
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scale: 1.0
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width: 227
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height: 227
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rgb: false
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classes: "classification_classes_ILSVRC2012.txt"
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sample: "classification"
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# Googlenet from https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
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googlenet:
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load_info:
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url: "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel"
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sha1: "405fc5acd08a3bb12de8ee5e23a96bec22f08204"
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model: "bvlc_googlenet.caffemodel"
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config: "bvlc_googlenet.prototxt"
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mean: [104, 117, 123]
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scale: 1.0
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width: 224
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height: 224
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rgb: false
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classes: "classification_classes_ILSVRC2012.txt"
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sample: "classification"
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################################################################################
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# Semantic segmentation models.
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################################################################################
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# ENet road scene segmentation network from https://github.com/e-lab/ENet-training
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# Works fine for different input sizes.
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enet:
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load_info:
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url: "https://www.dropbox.com/s/tdde0mawbi5dugq/Enet-model-best.net?dl=1"
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sha1: "b4123a73bf464b9ebe9cfc4ab9c2d5c72b161315"
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model: "Enet-model-best.net"
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mean: [0, 0, 0]
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scale: 0.00392
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width: 512
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height: 256
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rgb: true
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classes: "enet-classes.txt"
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sample: "segmentation"
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fcn8s:
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load_info:
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url: "http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel"
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sha1: "c449ea74dd7d83751d1357d6a8c323fcf4038962"
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model: "fcn8s-heavy-pascal.caffemodel"
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config: "fcn8s-heavy-pascal.prototxt"
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mean: [0, 0, 0]
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scale: 1.0
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width: 500
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height: 500
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rgb: false
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sample: "segmentation"
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