added mask RCNN script (#780)
Ben Hoff
6 years ago
committed by
Nikita Manovich
parent
dd821ca66f
commit
a435b410ed
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# mask_rcnn_inception_resnet_v2_atrous_coco
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## Use Case and High-Level Description
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Mask R-CNN Inception Resnet V2 Atrous is trained on COCO dataset and used for object instance segmentation.
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For details, see a [paper](https://arxiv.org/pdf/1703.06870.pdf).
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## Specification
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| Metric | Value |
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|---------------------------------|-------------------------------------------|
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| Type | Instance segmentation |
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| GFlops | 675.314 |
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| MParams | 92.368 |
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| Source framework | TensorFlow\* |
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## Legal Information
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[https://raw.githubusercontent.com/tensorflow/models/master/LICENSE]()
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## OpenVINO Conversion Notes
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In order to convert the code into the openvino format, please see the [following link](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html#mask_r_cnn_topologies).
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The conversion command from the command line prompt will look something like the following.
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```shell
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$ python /opt/intel/openvino/deployment_tools/model_optimizer/mo_tf.py \
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--input_model /path/to/frozen_inference_graph.pb \
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--tensorflow_use_custom_operations_config /opt/intel/openvino/deployment_tools/model_optimizer/extensions/front/tf/mask_rcnn_support.json \
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--tensorflow_object_detection_api_pipeline_config /path/to/pipeline.config
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```
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import numpy as np
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import cv2
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MASK_THRESHOLD = .5
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PROBABILITY_THRESHOLD = 0.2
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# Ref: https://software.intel.com/en-us/forums/computer-vision/topic/804895
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def segm_postprocess(box: list, raw_cls_mask, im_h, im_w, threshold):
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ymin, xmin, ymax, xmax = box
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width = int(abs(xmax - xmin))
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height = int(abs(ymax - ymin))
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result = np.zeros((im_h, im_w), dtype=np.uint8)
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resized_mask = cv2.resize(raw_cls_mask, dsize=(height, width), interpolation=cv2.INTER_CUBIC)
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# extract the ROI of the image
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ymin = int(round(ymin))
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xmin = int(round(xmin))
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ymax = ymin + height
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xmax = xmin + width
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result[xmin:xmax, ymin:ymax] = (resized_mask>threshold).astype(np.uint8) * 255
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return result
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for detection in detections:
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frame_number = detection['frame_id']
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height = detection['frame_height']
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width = detection['frame_width']
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detection = detection['detections']
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masks = detection['masks']
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boxes = detection['reshape_do_2d']
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for index, box in enumerate(boxes):
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label = int(box[1])
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obj_value = box[2]
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if obj_value >= PROBABILITY_THRESHOLD:
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x = box[3] * width
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y = box[4] * height
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right = box[5] * width
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bottom = box[6] * height
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mask = masks[index][label]
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mask = segm_postprocess((x, y, right, bottom),
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mask,
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height,
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width,
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MASK_THRESHOLD)
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contour, _ = cv2.findContours(mask,
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cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_TC89_KCOS)
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contour = contour[0]
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contour = contour.tolist()
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contour = [x[0] for x in contour]
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# NOTE: if you want to see the boxes, uncomment next line
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# results.add_box(x, y, right, bottom, label, frame_number)
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results.add_polygon(contour, label, frame_number)
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@ -0,0 +1,84 @@
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{
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"label_map": {
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"1": "person",
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"2": "bicycle",
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"3": "car",
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"4": "motorcycle",
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"5": "airplane",
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"6": "bus",
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"7": "train",
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"8": "truck",
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"9": "boat",
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"10": "traffic_light",
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"11": "fire_hydrant",
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"13": "stop_sign",
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"14": "parking_meter",
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"15": "bench",
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"16": "bird",
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"17": "cat",
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"18": "dog",
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"19": "horse",
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"20": "sheep",
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"21": "cow",
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"22": "elephant",
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"23": "bear",
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"24": "zebra",
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"25": "giraffe",
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"27": "backpack",
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"28": "umbrella",
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"31": "handbag",
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"32": "tie",
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"33": "suitcase",
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"34": "frisbee",
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"35": "skis",
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"36": "snowboard",
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"37": "sports_ball",
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"38": "kite",
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"39": "baseball_bat",
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"40": "baseball_glove",
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"41": "skateboard",
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"42": "surfboard",
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"43": "tennis_racket",
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"44": "bottle",
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"46": "wine_glass",
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"47": "cup",
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"48": "fork",
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"49": "knife",
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"50": "spoon",
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"51": "bowl",
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"52": "banana",
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"53": "apple",
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"54": "sandwich",
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"55": "orange",
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"56": "broccoli",
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"57": "carrot",
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"58": "hot_dog",
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"59": "pizza",
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"60": "donut",
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"61": "cake",
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"62": "chair",
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"63": "couch",
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"64": "potted_plant",
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"65": "bed",
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"67": "dining_table",
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"70": "toilet",
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"72": "tv",
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"73": "laptop",
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"74": "mouse",
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"75": "remote",
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"76": "keyboard",
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"77": "cell_phone",
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"78": "microwave",
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"79": "oven",
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"80": "toaster",
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"81": "sink",
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"83": "refrigerator",
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"84": "book",
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"85": "clock",
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"86": "vase",
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"87": "scissors",
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"88": "teddy_bear",
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"89": "hair_drier",
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"90": "toothbrush"
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}
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}
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