计算mAP

计算mAP

"""
Mask R-CNN
Configurations and data loading code for MS COCO.

Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla

------------------------------------------------------------

Usage: import the module (see Jupyter notebooks for examples), or run from
       the command line as such:

    # Train a new model starting from pre-trained COCO weights
    python3 coco.py train --dataset=/path/to/coco/ --model=coco

    # Train a new model starting from ImageNet weights. Also auto download COCO dataset
    python3 coco.py train --dataset=/path/to/coco/ --model=imagenet --download=True

    # Continue training a model that you had trained earlier
    python3 coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5

    # Continue training the last model you trained
    python3 coco.py train --dataset=/path/to/coco/ --model=last

    # Run COCO evaluatoin on the last model you trained
    python3 coco.py evaluate --dataset=/path/to/coco/ --model=last
"""

import os
import sys
import time
import numpy as np
import imgaug  # https://github.com/aleju/imgaug (pip3 install imgaug)


from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from pycocotools import mask as maskUtils

import zipfile
import urllib.request
import shutil

# Root directory of the project
ROOT_DIR = "F:\TensorflowProject\Object_detection2104\Dataset"

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn.config import Config
from mrcnn import model as modellib, utils

# Path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "lh0050.h5")

# Directory to save logs and model checkpoints, if not provided
# through the command line argument --logs
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
DEFAULT_DATASET_YEAR = "2014"

############################################################
#  Configurations
############################################################


class CocoConfig(Config):
    """Configuration for training on MS COCO.
    Derives from the base Config class and overrides values specific
    to the COCO dataset.
    """
    # Give the configuration a recognizable name
    NAME = "zzt_test"

    # We use a GPU with 12GB memory, which can fit two images.
    # Adjust down if you use a smaller GPU.
    IMAGES_PER_GPU = 16

    # Uncomment to train on 8 GPUs (default is 1)
    GPU_COUNT = 1

    # Number of training steps per epoch
    STEPS_PER_EPOCH = 100

    IMAGE_MIN_DIM = 480
    IMAGE_MAX_DIM = 640

    # Number of classes (including background)
    NUM_CLASSES = 1 + 18  # COCO has 80 classes, now 18 classes


############################################################
#  Dataset
############################################################
class InferenceConfig(CocoConfig):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
    DETECTION_MIN_CONFIDENCE = 0
        
class CocoDataset(utils.Dataset):
    def load_coco(self, dataset_dir, subset, year=DEFAULT_DATASET_YEAR, class_ids=None,
                  class_map=None, return_coco=False, auto_download=False):
        

        if auto_download is True:
            self.auto_download(dataset_dir, subset, year)

        coco = COCO("{}/annotations/{}.json".format(dataset_dir, subset))
        image_dir = "{}/{}".format(dataset_dir, subset)

        # Load all classes or a subset?
        if not class_ids:
            # All classes
            class_ids = sorted(coco.getCatIds())

        # All images or a subset?
        if class_ids:
            image_ids = []
            for id in class_ids:
                image_ids.extend(list(coco.getImgIds(catIds=[id])))
            # Remove duplicates
            image_ids = list(set(image_ids))
        else:
            # All images
            image_ids = list(coco.imgs.keys())

        # Add classes
        for i in class_ids:
            self.add_class("coco", i, coco.loadCats(i)[0]["name"])

        # Add images
        for i in image_ids:
            self.add_image(
                "coco", image_id=i,
                path=os.path.join(image_dir, coco.imgs[i]['file_name']),
                width=coco.imgs[i]["width"],
                height=coco.imgs[i]["height"],
                annotations=coco.loadAnns(coco.getAnnIds(
                    imgIds=[i], catIds=class_ids, iscrowd=None)))
        if return_coco:
            return coco

        

    def load_mask(self, image_id):
        
        # If not a COCO image, delegate to parent class.
        image_info = self.image_info[image_id]
        if image_info["source"] != "coco":
            return super(CocoDataset, self).load_mask(image_id)

        instance_masks = []
        class_ids = []
        annotations = self.image_info[image_id]["annotations"]
        # Build mask of shape [height, width, instance_count] and list
        # of class IDs that correspond to each channel of the mask.
        for annotation in annotations:
            class_id = self.map_source_class_id(
                "coco.{}".format(annotation['category_id']))
            if class_id:
                m = self.annToMask(annotation, image_info["height"],
                                   image_info["width"])
                # Some objects are so small that they're less than 1 pixel area
                # and end up rounded out. Skip those objects.
                if m.max() < 1:
                    continue
                # Is it a crowd? If so, use a negative class ID.
                if annotation['iscrowd']:
                    # Use negative class ID for crowds
                    class_id *= -1
                    # For crowd masks, annToMask() sometimes returns a mask
                    # smaller than the given dimensions. If so, resize it.
                    if m.shape[0] != image_info["height"] or m.shape[1] != image_info["width"]:
                        m = np.ones([image_info["height"], image_info["width"]], dtype=bool)
                instance_masks.append(m)
                class_ids.append(class_id)

        # Pack instance masks into an array
        if class_ids:
            mask = np.stack(instance_masks, axis=2).astype(np.bool_)
            class_ids = np.array(class_ids, dtype=np.int32)
            return mask, class_ids
        else:
            # Call super class to return an empty mask
            return super(CocoDataset, self).load_mask(image_id)

    def image_reference(self, image_id):
        """Return a link to the image in the COCO Website."""
      
        return "%d.jpg"%image_id
       

    # The following two functions are from pycocotools with a few changes.

    def annToRLE(self, ann, height, width):
        """
        Convert annotation which can be polygons, uncompressed RLE to RLE.
        :return: binary mask (numpy 2D array)
        """
        segm = ann['segmentation']
        if isinstance(segm, list):
            # polygon -- a single object might consist of multiple parts
            # we merge all parts into one mask rle code
            rles = maskUtils.frPyObjects(segm, height, width)
            rle = maskUtils.merge(rles)
        elif isinstance(segm['counts'], list):
            # uncompressed RLE
            rle = maskUtils.frPyObjects(segm, height, width)
        else:
            # rle
            rle = ann['segmentation']
        return rle

    def annToMask(self, ann, height, width):
        """
        Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
        :return: binary mask (numpy 2D array)
        """
        rle = self.annToRLE(ann, height, width)
        m = maskUtils.decode(rle)
        return m


############################################################
#  COCO Evaluation
############################################################

def build_coco_results(dataset, image_ids, rois, class_ids, scores, masks):
    """Arrange resutls to match COCO specs in http://cocodataset.org/#format
    """
    # If no results, return an empty list
    if rois is None:
        return []

    results = []
    for image_id in image_ids:
        # Loop through detections
        for i in range(rois.shape[0]):
            class_id = class_ids[i]
            score = scores[i]
            bbox = np.around(rois[i], 1)
            mask = masks[:, :, i]

            print("class_id:",class_id)

            result = {
                "image_id": image_id,
                "category_id": dataset.get_source_class_id(class_id, "coco"),
                "bbox": [bbox[1], bbox[0], bbox[3] - bbox[1], bbox[2] - bbox[0]],
                "score": score,
                "segmentation": maskUtils.encode(np.asfortranarray(mask))
            }
            results.append(result)
    return results


#

#
def evaluate_coco(model, dataset, coco,inference_config, eval_type="segm", limit=0, image_ids=None):
    """Runs official COCO evaluation.
    dataset: A Dataset object with valiadtion data
    eval_type: "bbox" or "segm" for bounding box or segmentation evaluation
    limit: if not 0, it's the number of images to use for evaluation
    """
    # Pick COCO images from the dataset
    image_ids = image_ids or dataset.image_ids

    # Limit to a subset
    if limit:
        image_ids = image_ids[:limit]

    # Get corresponding COCO image IDs.
    coco_image_ids = [dataset.image_info[id]["id"] for id in image_ids]

    t_prediction = 0
    t_start = time.time()

    APs = []
    #print(image_ids)
    results = []
    total = len(image_ids)
    for i, image_id in enumerate(image_ids):
        # Load image
        #image = dataset.load_image(image_id)

        image3, image_meta, gt_class_id, gt_bbox, gt_mask = modellib.load_image_gt(
            dataset, inference_config,image_id, use_mini_mask=False)

        # Run detection
        t = time.time()
        r = model.detect([image3], verbose=0)[0]
        t_prediction += (time.time() - t)

        # Convert results to COCO format
        # Cast masks to uint8 because COCO tools errors out on bool
        '''
        image_results = build_coco_results(dataset, coco_image_ids[i:i + 1],
                                           r["rois"], 
                                           r["class_ids"],
                                           r["scores"],
                                           r["masks"].astype(np.uint8))
        results.extend(image_results)
        '''

        AP, precisions, recalls, overlaps = utils.compute_ap(gt_bbox, gt_class_id, gt_mask,
                                          r["rois"], r["class_ids"], r["scores"], r['masks'],iou_threshold=0.5)
        APs.append(AP)


        
        
    #---------显示evaluation进度条---------##################
        print('
',end='')
        pro = int((i+1)/total*40)
        print('Testing images|'+'#'*pro+'-'*(40-pro)+'| {}/{} '.format(i+1,total),end='')

    print("AP: ", APs)
    print("mAP: ", np.mean(APs))
    print()
    #------------------------------------##################

    '''
    # Load results. This modifies results with additional attributes.
    coco_results = coco.loadRes(results)

    # Evaluate
    cocoEval = COCOeval(coco, coco_results, eval_type)
    cocoEval.params.imgIds = coco_image_ids
    cocoEval.params.iouType='segm'
    #cocoEval.params.maxDets=[1,10,200]
    cocoEval.params.catIds = [1,3]

    cocoEval.evaluate()
    cocoEval.accumulate()
    cocoEval.summarize()

    '''

    print("Prediction time: {}. Average {}/image".format(
        t_prediction, t_prediction / len(image_ids)))
    
    print("Total time: ", time.time() - t_start)


############################################################
#  Training
############################################################


if __name__ == '__main__':
    

    
    config = InferenceConfig()
    config.display()

    model_dir="F:\TensorflowProject\Object_detection2104\Dataset\mask_rcnn_coco.h5"
    #model = modellib.MaskRCNN(mode="training", config=config,model_dir=model_dir)
    model = modellib.MaskRCNN(mode="inference", config=config,
                                  model_dir=COCO_MODEL_PATH)
    

    
    ################################加载权重##################################################
    model_path = COCO_MODEL_PATH
    dataset_path = "F:\TensorflowProject\Object_detection2104\Dataset"
    
    # Load weights
    print("Loading weights ", model_path)
    
    #model.load_weights(model_path, by_name=True)
    model.load_weights(COCO_MODEL_PATH, by_name=True, exclude=["mrcnn_class_logits", "mrcnn_bbox_fc","mrcnn_bbox", "mrcnn_mask"])
    
    ##########################################################################################

    # Train or evaluate
    dataset_val = CocoDataset()
    val_type = "val" 
    coco = dataset_val.load_coco(dataset_path, val_type, return_coco=True)
    dataset_val.prepare()


    
    #print("Running COCO evaluation on {} images.".format(args.limit))
    evaluate_coco(model, dataset_val, coco,config, "bbox")

    
    

    ###

    

Testing images|########################################| 32/32 AP: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0031446541817683094, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
mAP: 9.827044318025967e-05

Prediction time: 78.99849963188171. Average 2.4687031134963036/image
Total time: 102.15886425971985

 https://blog.csdn.net/hesongzefairy/article/details/106746216

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