import  os
import  sys
import  random
import  math
import  numpy  as  np
import  skimage.io 
import matplotlib
import matplotlib.pyplot as plt
import time
# Root directory of the project
ROOT_DIR = os.path.abspath("../")
import argparse

# Parse command line arguments
parser = argparse.ArgumentParser(
        description='Train Mask R-CNN on MS COCO.')
#parser.add_argument("command",
#                        metavar="<command>",
#                        help="'train' or 'evaluate' on MS COCO")
parser.add_argument('--model', required=False, default="mask_rcnn_coco.h5",
                        metavar="mask_rcnn_coco.h5",
                        help="Path to weights .h5 file or 'coco'")
args = parser.parse_args()
print("model: ", args.model)
# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
# Import COCO config
sys.path.append(os.path.join(ROOT_DIR, "samples/coco/"))  # To find local version
import coco

#%matplotlib inline 

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, args.model)
#COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
print('model path: ', COCO_MODEL_PATH)
#quit()
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")
class InferenceConfig(coco.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

config = InferenceConfig()
config.display()

# Create model object in inference mode.# Creat 
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)
print(model.keras_model.summary())
print('number of layers: ', len(model.keras_model.layers))
# COCO Class names# COCO  
# Index of the class in the list is its ID. For example, to get ID of
# the teddy bear class, use: class_names.index('teddy bear')
class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
               'bus', 'train', 'truck', 'boat', 'traffic light',
               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
               'kite', 'baseball bat', 'baseball glove', 'skateboard',
               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
               'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
               'teddy bear', 'hair drier', 'toothbrush']

# Load a random image from the images folder# Load  
file_names = next(os.walk(IMAGE_DIR))[2]
for i in range(5):
  t1=time.time()
  image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))

# Run detection
  results = model.detect([image], verbose=1)

# Visualize results
  r = results[0]
  print('detect time: ', time.time()-t1, ' results[0] ', r)
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], 
                            class_names, r['scores'])
