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det2_1.py
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"""
conda activate env2_det2
COCO DATA -- MS COCO -->> https://cocodataset.org/#detection-eval
COCO DATA Download -- SOURCE -->> https://www.neuralception.com/cocodatasetapi/
"""
import torch, detectron2
#!nvcc --version
TORCH_VERSION = ".".join(torch.__version__.split(".")[:2])
CUDA_VERSION = torch.__version__.split("+")[-1]
print("torch: ", TORCH_VERSION, "; cuda: ", CUDA_VERSION)
print("detectron2:", detectron2.__version__)
"""
torch: 1.10 ; cuda: 1.10.1
detectron2: 0.6
"""
#Some basic setup:
# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common libraries
import numpy as np
import os, json, cv2, random
#from google.colab.patches import cv2_imshow
# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog
## First steps with COCO Data
#!wget http://images.cocodataset.org/val2017/000000439715.jpg -q -O input.jpg
# coco_test_image = cv2.imread("./input.jpg")
# print("---type(coco_test_image",type(coco_test_image)) ## ---type(coco_test_image <class 'numpy.ndarray'>
## INIT ANNO
import json
path_validate = "./coco_val_images_2017/coco_train_2017/annotations/instances_val2017.json" ##/home/dhankar/temp/11_22/a___own_git_up/detect2/Obj_Detect_Detectron2
#path_train = "./coco_val_images_2017/coco_train_2017/annotations/instances_train2017.json" ##/home/dhankar/temp/11_22/a___own_git_up/detect2
# f_train_annos = open(path_train)
# anno_train = json.load(f_train_annos)
# print("---ANNO--Keys---",anno_train.keys())
# print("---ANNO--Keys---",anno_train["images"][1])
# print("---ANNO--Keys----annotations-",anno_train["annotations"][1])
from pycocotools.coco import COCO
coco_obj=COCO(path_validate)
#print(type(coco_obj)) ##<class 'pycocotools.coco.COCO'>
# Get list of category_ids, here [2] for bicycle
category_ids = coco_obj.getCatIds(['bicycle'])
# Get list of image_ids which contain bicycles
image_ids = coco_obj.getImgIds(catIds=[2])
print("--10 IMAGE ID's----",image_ids[0:10])
ls_image_ids_bikes = image_ids[0:10]
print("-ls_image_ids_bikes---",type(ls_image_ids_bikes))
"""
TRAIN SET
loading annotations into memory...
Done (t=9.13s)
creating index...
index created!
<class 'pycocotools.coco.COCO'>
[196610, 344067, 155652, 417797, 294918, 57353, 516105, 253965, 229391, 57361]
"""
"""
VALIDATION SET
[184324, 546823, 343561, 169996, 277005, 549390, 384527, 8211, 93717, 468505]
"""
# Get all bicycle annotations for TRAIN SET --image 0000 0019 6610.jpg
# Get all bicycle annotations for VAL SET -- image 000000093717.jpg
#image_id = 343561 #93717 #169996
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.patches as patches
for iter_image in range(len(ls_image_ids_bikes)):
#
print("---iter_image-",ls_image_ids_bikes[iter_image])
#annotation_ids = coco_obj.getAnnIds(imgIds=196610, catIds=[2])
annotation_ids = coco_obj.getAnnIds(imgIds=ls_image_ids_bikes[iter_image], catIds=[2])
print(len(annotation_ids))
ls_anno_ids = coco_obj.loadAnns(annotation_ids)
print("---ls_anno_ids----\n",ls_anno_ids)
# for anno in ls_anno_ids:
# print(anno['bbox'])
images_path_validate = "/home/dhankar/temp/11_22/a___own_git_up/detect2/Obj_Detect_Detectron2/coco_val_images_2017/val2017/"
image_name = str(ls_image_ids_bikes[iter_image]).zfill(12)+".jpg" # Image names are 12 characters long
image = Image.open(images_path_validate+image_name)
fig, ax = plt.subplots()
# ax.imshow(image)
# plt.show()
# Draw boxes and add label to each box
for anno in ls_anno_ids:
box = anno['bbox']
bb = patches.Rectangle((box[0],box[1]), box[2],box[3], linewidth=3, edgecolor="blue", facecolor="none")
ax.add_patch(bb)
ax.imshow(image)
plt.show()
"""
Explore the --standard-dataset-dicts -->>
https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html?highlight=bbox_mode#standard-dataset-dicts
"""