import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))




#Importing Libraries
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import Image

import keras_cv
import keras_core as keras



from collections import defaultdict
import json

class COCOParser:
    def __init__(self, anns_file, imgs_dir):
        with open(anns_file, 'r') as f:
            coco = json.load(f)
            
        self.annIm_dict = defaultdict(list)        
        self.cat_dict = {} 
        self.annId_dict = {}
        self.im_dict = {}
        self.licenses_dict = {}
       
        for ann in coco['annotations']:           
            self.annIm_dict[ann['image_id']].append(ann) 
            self.annId_dict[ann['id']]=ann
        for img in coco['images']:
            self.im_dict[img['id']] = img
        for cat in coco['categories']:
            self.cat_dict[cat['id']] = cat
        for license in coco['licenses']:
            self.licenses_dict[license['id']] = license
    
    def get_imgIds(self):
        return list(self.im_dict.keys())
    def get_annIds(self, im_ids):
        im_ids=im_ids if isinstance(im_ids, list) else [im_ids]
        return [ann['id'] for im_id in im_ids for ann in self.annIm_dict[im_id]]
    def load_anns(self, ann_ids):
        im_ids=ann_ids if isinstance(ann_ids, list) else [ann_ids]
        return [self.annId_dict[ann_id] for ann_id in ann_ids]        
    def load_cats(self, class_ids):
        class_ids=class_ids if isinstance(class_ids, list) else [class_ids]
        return [self.cat_dict[class_id] for class_id in class_ids]
    def get_imgLicenses(self,im_ids):
        im_ids=im_ids if isinstance(im_ids, list) else [im_ids]
        lic_ids = [self.im_dict[im_id]["license"] for im_id in im_ids]
        return [self.licenses_dict[lic_id] for lic_id in lic_ids]
coco_images_dir = "/kaggle/input/coco-2017-dataset/coco2017/train2017"
annot_file = "/kaggle/input/coco-2017-dataset/coco2017/annotations/instances_train2017.json"

coco = COCOParser(annot_file, coco_images_dir)
from PIL import Image
import numpy as np

# define a list of colors for drawing bounding boxes
color_list = ["pink", "red", "teal", "blue", "orange", "yellow", "black", "magenta","green","aqua"]*10
num_imgs_to_disp = 4
total_images = len(coco.get_imgIds()) # total number of images
sel_im_idxs = np.random.permutation(total_images)[:num_imgs_to_disp]
img_ids = coco.get_imgIds()
selected_img_ids = [img_ids[i] for i in sel_im_idxs]
ann_ids = coco.get_annIds(selected_img_ids)
im_licenses = coco.get_imgLicenses(selected_img_ids)

fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(15,10))
ax = ax.ravel()

#Loading and visualizing the dataset
for i, im in enumerate(selected_img_ids):
    image = Image.open(f"{coco_images_dir}/{str(im).zfill(12)}.jpg")
    ann_ids = coco.get_annIds(im)
    annotations = coco.load_anns(ann_ids)
    
    for ann in annotations:
        bbox = ann['bbox']
        x, y, w, h = [int(b) for b in bbox]
        class_id = ann["category_id"]
        class_name = coco.load_cats(class_id)[0]["name"]
        license = coco.get_imgLicenses(im)[0]["name"]
        color_ = color_list[class_id]
        rect = plt.Rectangle((x, y), w, h, linewidth=2, edgecolor=color_, facecolor='none')
        t_box=ax[i].text(x, y, class_name,  color='red', fontsize=10)
        t_box.set_bbox(dict(boxstyle='square, pad=0',facecolor='white', alpha=0.6, edgecolor='blue'))
        ax[i].add_patch(rect)
    
    ax[i].axis('off')
    ax[i].imshow(image)
    ax[i].set_xlabel('Longitude')
    ax[i].set_title(f"License: {license}")
plt.tight_layout()
plt.show()