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| import os |
| import cv2 |
| import json |
| import glob |
| import math |
| import random |
| import argparse |
| import shutil |
| import numpy as np |
| from lxml import etree |
| from tqdm import tqdm |
| from PIL import Image, ImageDraw |
| import matplotlib.pyplot as plt |
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| vibrant_colors = [[0,0,255], [0,255,0], [0,255,255], [255,0,0], [255,0,255], [255,255,0]] |
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| def parse_anno_file(cvat_xml,image_name): |
| """ |
| Parses annotation file and returns the details of annotation |
| for the given image ID |
| """ |
| root = etree.parse(cvat_xml).getroot() |
| |
| anno = [] |
| image_name_attr = ".//image[@name='{}']".format(image_name) |
| for image_tag in root.iterfind(image_name_attr): |
| |
| image = {} |
| for key, value in image_tag.items(): |
| image[key] = value |
| image['shapes'] = [] |
| for poly_tag in image_tag.iter('polygon'): |
| polygon = {'type': 'polygon'} |
| for key, value in poly_tag.items(): |
| polygon[key] = value |
| image['shapes'].append(polygon) |
| for box_tag in image_tag.iter('box'): |
| box = {'type': 'box'} |
| for key, value in box_tag.items(): |
| box[key] = value |
| box['points'] = "{0},{1};{2},{1};{2},{3};{0},{3}".format( |
| box['xtl'], box['ytl'], box['xbr'], box['ybr']) |
| |
| image['shapes'].append(box) |
| image['shapes'].sort(key=lambda x: int(x.get('z_order', 0))) |
| anno.append(image) |
| return anno |
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| all_folders_root = 'Blood SmearAnalysis' |
| all_folders = glob.glob('{}/*'.format(all_folders_root)) |
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| for folders in tqdm(all_folders): |
| all_files_per_folder = glob.glob('{}/*'.format(folders)) |
| all_valid_files_per_folder = [] |
| for files in all_files_per_folder: |
| get_extension = files.split('.')[-1] |
| if get_extension == 'jpg' or get_extension == 'png': |
| all_valid_files_per_folder.append(files) |
| |
| file_name = folders+"/annotations.xml" |
| for valid_image_names in all_valid_files_per_folder: |
| try: |
|
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| valid_image_names_ = valid_image_names.split('/')[-1] |
| print("Image Name = ",valid_image_names_) |
| annot = parse_anno_file(file_name,valid_image_names_) |
| |
| print("Annotation = ",annot) |
| |
| annot = annot[0] |
| |
| im_height = annot['height'] |
| im_width = annot['width'] |
| im_id = annot['id'] |
| im_name = annot['name'] |
| im_shapes = annot['shapes'] |
| get_im_path = folders+"/"+im_name |
| get_image = cv2.imread(get_im_path,cv2.IMREAD_COLOR) |
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| name_ = im_name.split('.')[0] |
| |
| im = Image.open(valid_image_names).convert("RGBA") |
| imArray = np.asarray(im) |
| count = 0 |
| get_all_masks = [] |
| get_bbox_coords = [] |
| for shape in im_shapes: |
| count += 1 |
| |
| |
| |
| points = shape['points'] |
| label = shape['label'] |
| print(points) |
| all_points = points.split(';') |
| |
| x_y = [] |
| all_x = [] |
| all_y = [] |
| for point_ in all_points: |
| x = float(point_.split(',')[0]) |
| y = float(point_.split(',')[1]) |
| all_x.append(x) |
| all_y.append(y) |
| |
| x_y.append((x,y)) |
| |
| max_x = max(all_x) |
| min_x = min(all_x) |
| max_y = max(all_y) |
| min_y = min(all_y) |
| gap_x = max_x - min_x |
| gap_y = max_y - min_y |
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| maskIm = Image.new('L', (imArray.shape[1], imArray.shape[0]), 0) |
| ImageDraw.Draw(maskIm).polygon(x_y, outline=1, fill=1) |
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| maskIm = np.array(maskIm) * 255 |
| |
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| print(maskIm.max()) |
| act_mask = np.zeros_like(get_image) |
| act_mask[:,:,0] = maskIm |
| act_mask[:,:,1] = maskIm |
| act_mask[:,:,2] = maskIm |
| print("--"*40,get_image.shape,", ",act_mask.shape) |
| |
| green_mask = get_image.copy() |
| col = vibrant_colors[random.randint(0,5)] |
|
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| green_mask[(act_mask==255).all(-1)] = col |
| get_bbox_coords.append([min_x,min_y,gap_x,gap_y, label, col]) |
| |
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| get_all_masks.append(green_mask) |
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| |
| print("--"*40,len(get_all_masks)) |
| final_masked_im = np.zeros_like(get_image) |
| final_masked_im = float(1/(len(get_all_masks)+1))*get_image |
| print("fff",final_masked_im.max()) |
|
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| for image in get_all_masks: |
| print("max = ",image.max(),"min = ",image.min()) |
| final_masked_im = final_masked_im + float(1/(len(get_all_masks)+1))*image |
| print("Final max = ",final_masked_im.max(),"min = ",final_masked_im.min()) |
| |
| |
| print("fin = ",final_masked_im.max()) |
|
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| for items in get_bbox_coords: |
| x,y,w,h,name, col = int(items[0]), int(items[1]), int(items[2]), int(items[3]), items[4], items[5] |
| cropped_img = np.zeros((w,h,3)) |
| cropped_img = get_image[y:y+h,x:x+w] |
| plt.imshow(cropped_img[:,:,::-1]) |
| plt.show() |
| print(x,y,w,h,name) |
| |
| cv2.rectangle(final_masked_im, (x,y), (x+w,y+h), col,15) |
| |
| cv2.putText(final_masked_im, str(name), (x, y), 0, 3, [0,0,0], 10) |
| plt.imshow(final_masked_im[:,:,::-1]/255) |
| plt.show() |
| |
| |
| except: |
| print("PASS") |
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