Merge remote-tracking branch 'origin/IT18118346' into IT18118346

# Conflicts:
#	Backend/Blur.py
#	Backend/Cut.py
#	Backend/Splash.py
#	Backend/api.py
#	Backend/maskImage.py

Backend/maskImage - Copy - Copy.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
-
-
-
-import colorsys
-from skimage.measure import find_contours
-from matplotlib.patches import Polygon
-
-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
-
-#import custom
-
-# Root directory of the project
-ROOT_DIR = "D:/Research model 2 test - 2021.08.03"
-
-MODEL_DIR = os.path.join(ROOT_DIR, "logs")
-
-WEIGHTS_PATH = "D:/Research model 2 test/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
-
-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_to_new_image = 'D:/Research model 2 test/ex6.jpg'
-image1 = mpimg.imread(path_to_new_image)
-plt.imshow(image1)
-blurred_img = cv2.GaussianBlur(image1, (21, 21), 0)
-
-def random_colors(N, bright=True):
-    """
-    Generate random colors.
-    To get visually distinct colors, generate them in HSV space then
-    convert to RGB.
-    """
-    brightness = 1.0 if bright else 0.7
-    hsv = [(i / N, 1, brightness) for i in range(N)]
-    colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
-    random.shuffle(colors)
-    return colors
-    
-def apply_mask(image, mask, color, alpha=0.5):
-    """Apply the given mask to the image.
-    """
-    
-    for c in range(3):
-        image[:, :, c] = np.where(mask == 1,
-                                  image[:, :, c] *
-                                  (1 - alpha) + alpha * color[c] * 255,
-                                  image[:, :, c])
-    return image
-
-def display_instances(image, boxes, masks, class_ids, class_names,
-                      scores=None, title="",
-                      figsize=(16, 16), ax=None,
-                      show_mask=True, show_bbox=True,
-                      colors=None, captions=None):
-    """
-    boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
-    masks: [height, width, num_instances]
-    class_ids: [num_instances]
-    class_names: list of class names of the dataset
-    scores: (optional) confidence scores for each box
-    title: (optional) Figure title
-    show_mask, show_bbox: To show masks and bounding boxes or not
-    figsize: (optional) the size of the image
-    colors: (optional) An array or colors to use with each object
-    captions: (optional) A list of strings to use as captions for each object
-    """
-    # Number of instances
-    N = boxes.shape[0]
-    if not N:
-        print("\n*** No instances to display *** \n")
-    else:
-        assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
-
-    # If no axis is passed, create one and automatically call show()
-    auto_show = False
-    if not ax:
-        _, ax = plt.subplots(1, figsize=figsize)
-        auto_show = True
-
-    # Generate random colors
-    colors = colors or random_colors(N)
-
-    # Show area outside image boundaries.
-    height, width = image.shape[:2]
-    ax.set_ylim(height + 10, -10)
-    ax.set_xlim(-10, width + 10)
-    ax.axis('off')
-    ax.set_title(title)
-
-    masked_image = image.astype(np.uint32).copy()
-    for i in range(N):
-        color = colors[i]
-
-        # Bounding box
-        if not np.any(boxes[i]):
-            # Skip this instance. Has no bbox. Likely lost in image cropping.
-            continue
-        y1, x1, y2, x2 = boxes[i]
-        if show_bbox:
-            p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
-                                alpha=0.7, linestyle="dashed",
-                                edgecolor=color, facecolor='none')
-            ax.add_patch(p)
-
-        # Label
-        if not captions:
-            class_id = class_ids[i]
-            score = scores[i] if scores is not None else None
-            label = class_names[class_id]
-            caption = "{} {:.3f}".format(label, score) if score else label
-        else:
-            caption = captions[i]
-        ax.text(x1, y1 + 8, caption,
-                color='w', size=11, backgroundcolor="none")
-
-        # Mask
-        mask = masks[:, :, i]
-        if show_mask:
-            masked_image = apply_mask(masked_image, mask, color)
-
-        # Mask Polygon
-        # Pad to ensure proper polygons for masks that touch image edges.
-        padded_mask = np.zeros(
-            (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
-        padded_mask[1:-1, 1:-1] = mask
-        
-        
-        contours, hierarchy = cv2.findContours(padded_mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        
-        #contours = find_contours(padded_mask, 0.5)
-        
-        cv2.drawContours(padded_mask, contours, -1, (255,255,255),5)
-        output = np.where(padded_mask==np.array([255, 255, 255]), blurred_img, image)
-        
-        """for verts in contours:
-            # Subtract the padding and flip (y, x) to (x, y)
-            verts = np.fliplr(verts) - 1
-            p = Polygon(verts, facecolor="none", edgecolor=color)
-            ax.add_patch(p)"""
-            
-    #ax.imshow(masked_image.astype(np.uint8))
-    #plt.imsave("test1.jpg", masked_image.astype(np.uint8))
-    plt.imshow(mask)
-    plt.imsave("test12.jpg", output)
-    if auto_show:
-        plt.show()
-
-print(len([image1]))
-results1 = model.detect([image1], verbose=1)
-
-
-r1 = results1[0]
-
-display_instances(image1, r1['rois'], r1['masks'], r1['class_ids'],
-class_names, r1['scores'], title="Predictions1")
\ No newline at end of file