Delete maskImage.py

Backend/maskImage.py

-import warnings
-warnings.filterwarnings('ignore')
-import os
-import sys
-import json
-import datetime
-import numpy as np
-import skimage.draw
-import cv2
-import random
-import math
-import re
-import time
-import tensorflow as tf
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-import matplotlib.image as mpimg
-
-from mrcnn import utils
-from mrcnn import visualize
-from mrcnn.visualize import display_images
-from mrcnn.visualize import display_instances
-import mrcnn.model as modellib
-from mrcnn.model import log
-from mrcnn.config import Config
-from mrcnn import model as modellib, utils
-
-# Root directory of the project
-ROOT_DIR = "samples"
-
-DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
-
-MODEL_DIR = os.path.join(ROOT_DIR, "logs")
-
-WEIGHTS_PATH = "D:/research -app/2021-129/Backend/mask_rcnn_object_0010.h5"
-
-class CustomConfig(Config):
-    """Configuration for training on the custom  dataset.
-    Derives from the base Config class and overrides some values.
-    """
-    # Give the configuration a recognizable name
-    NAME = "object"
-
-    IMAGES_PER_GPU = 1
-
-    NUM_CLASSES = 1 + 3  # Background + labels
-
-    # Number of training steps per epoch
-    STEPS_PER_EPOCH = 10
-
-    # Skip detections with < 90% confidence
-    DETECTION_MIN_CONFIDENCE = 0.9
-	
-config = CustomConfig()
-
-class InferenceConfig(config.__class__):
-    # Run detection on one image at a time
-    GPU_COUNT = 1
-    IMAGES_PER_GPU = 1
-    DETECTION_MIN_CONFIDENCE = 0.7
-
-def mask():
-    config = InferenceConfig()
-    config.display()
-
-    #LOAD MODEL. Create model in inference mode
-    model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
-
-    # Load COCO weights Or, load the last model you trained
-    weights_path = WEIGHTS_PATH
-    # Load weights
-    # print("Loading weights ", weights_path)
-    model.load_weights(weights_path, by_name=True)
-
-    class_names = ['BG', 'portrait','portrait_body', 'selfie_top']
-
-    # path = sys.argv[1]
-    path_to_new_image = 'static/theimage.jpg'
-    # path_to_new_image = path.split("/")[-1]
-    image1 = mpimg.imread(path_to_new_image)
-    # plt.imshow(image1)
-
-    results1 = model.detect([image1], verbose=1)
-
-    r1 = results1[0]
-    visualize.display_instances(image1, r1['rois'], r1['masks'], r1['class_ids'],
-                                class_names, r1['scores'], title="Predictions1")
-
-    return True
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