sibi_detection.py

APIMODEL_PATH = 'models'
ANNOTATION_PATH = 'data'
IMAGE_PATH = 'images'
CONFIG_PATH = 'training/pipeline.config'
CHECKPOINT_PATH = 'training'
PRETRAINED_MODEL_PATH = 'ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8'
MODEL_PATH = 'training'

import tensorflow as tf
from object_detection.utils import config_util
from object_detection.protos import pipeline_pb2
from google.protobuf import text_format

CONFIG_PATH = 'training/pipeline.config'

import os
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.builders import model_builder

# Load pipeline config and build a detection model
configs = config_util.get_configs_from_pipeline_file(CONFIG_PATH)
detection_model = model_builder.build(model_config=configs['model'], is_training=False)

# Restore checkpoint
ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
ckpt.restore(os.path.join(CHECKPOINT_PATH, 'ckpt-106')).expect_partial()

@tf.function
def detect_fn(image):
    image, shapes = detection_model.preprocess(image)
    prediction_dict = detection_model.predict(image, shapes)
    detections = detection_model.postprocess(prediction_dict, shapes)
    return detections

import cv2 
import numpy as np

category_index = label_map_util.create_category_index_from_labelmap(ANNOTATION_PATH+'/label_map.pbtxt')

# Setup capture
cap = cv2.VideoCapture(0)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

while True: 
    ret, frame = cap.read()
    image_np = np.array(frame)
    
    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
    detections = detect_fn(input_tensor)
    
    num_detections = int(detections.pop('num_detections'))
    detections = {key: value[0, :num_detections].numpy()
                  for key, value in detections.items()}
    detections['num_detections'] = num_detections

    # detection_classes should be ints.
    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

    label_id_offset = 1
    image_np_with_detections = image_np.copy()

    viz_utils.visualize_boxes_and_labels_on_image_array(
                image_np_with_detections,
                detections['detection_boxes'],
                detections['detection_classes']+label_id_offset,
                detections['detection_scores'],
                category_index,
                use_normalized_coordinates=True,
                max_boxes_to_draw=20,
                min_score_thresh=0.85,
                agnostic_mode=False)

    cv2.imshow('object detection',  cv2.resize(image_np_with_detections, (800, 600)))
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        cap.release()
        break

detections = detect_fn(input_tensor)
from matplotlib import pyplot as plt

# python models/research/object_detection/model_main_tf2.py --model_dir=training --pipeline_config_path=training/pipeline.config --num_train_steps=105000
# python models/research/object_detection/model_main_tf2.py --pipeline_config_path=training/pipeline.config  --model_dir=training --alsologtostderr --checkpoint_dir=training
# python C:\Users\7373\AppData\Roaming\Python\Python37\site-packages\tensorboard\main.py --logdir=training