Chuanshuoge

Friday, June 4, 2021

Thursday, June 3, 2021

opencv 56 tensorflow detection model zoo 2

centernet_resnet101

6 frames/second

tfod.py

import os

import time

import tensorflow as tf

import tarfile

import urllib.request

from object_detection.utils import label_map_util

from object_detection.utils import config_util

from object_detection.utils import visualization_utils as viz_utils

from object_detection.builders import model_builder

import cv2

import numpy as np

DATA_DIR = os.path.join(os.getcwd(), 'googleNet')

MODELS_DIR = os.path.join(DATA_DIR, 'modelZoo')

print(MODELS_DIR)

for dir in [DATA_DIR, MODELS_DIR]:

if not os.path.exists(dir):

os.mkdir(dir)

# Download and extract model

MODEL_DATE = '20200711'

MODEL_NAME = 'centernet_resnet101_v1_fpn_512x512_coco17_tpu-8'

MODEL_TAR_FILENAME = MODEL_NAME + '.tar.gz'

MODELS_DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/tf2/'

MODEL_DOWNLOAD_LINK = MODELS_DOWNLOAD_BASE + MODEL_DATE + '/' + MODEL_TAR_FILENAME

PATH_TO_MODEL_TAR = os.path.join(MODELS_DIR, MODEL_TAR_FILENAME)

PATH_TO_CKPT = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'checkpoint/'))

PATH_TO_CFG = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, 'pipeline.config'))

if not os.path.exists(PATH_TO_CKPT):

print('Downloading model. This may take a while... ', end='')

urllib.request.urlretrieve(MODEL_DOWNLOAD_LINK, PATH_TO_MODEL_TAR)

tar_file = tarfile.open(PATH_TO_MODEL_TAR)

tar_file.extractall(MODELS_DIR)

tar_file.close()

os.remove(PATH_TO_MODEL_TAR)

print('Done')

# Download labels file

LABEL_FILENAME = 'mscoco_label_map.pbtxt'

LABELS_DOWNLOAD_BASE = \

'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'

PATH_TO_LABELS = os.path.join(MODELS_DIR, os.path.join(MODEL_NAME, LABEL_FILENAME))

if not os.path.exists(PATH_TO_LABELS):

print('Downloading label file... ', end='')

print(PATH_TO_LABELS)

urllib.request.urlretrieve(LABELS_DOWNLOAD_BASE + LABEL_FILENAME, PATH_TO_LABELS)

print('Done')

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging

tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)

# Enable GPU dynamic memory allocation

gpus = tf.config.experimental.list_physical_devices('GPU')

for gpu in gpus:

tf.config.experimental.set_memory_growth(gpu, True)

# Load pipeline config and build a detection model

configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)

model_config = configs['model']

detection_model = model_builder.build(model_config=model_config, is_training=False)

# Restore checkpoint

ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)

ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial()

@tf.function

def detect_fn(image):

"""Detect objects in image."""

image, shapes = detection_model.preprocess(image)

prediction_dict = detection_model.predict(image, shapes)

detections = detection_model.postprocess(prediction_dict, shapes)

return detections, prediction_dict, tf.reshape(shapes, [-1])

category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,

use_display_name=True)

# Using OpenCV to initialize the webcam

cap = cv2.VideoCapture("assets/Cairo.mp4")

# Define the codec and create VideoWriter object

fps = 25.175

frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))

frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

size = (int(frame_width), int(frame_height))

fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')

path = 'C:/Users/zchen/PycharmProjects/opencv/googleNet/record'

out = cv2.VideoWriter()

success = out.open(os.path.join(path, "cario.mov"), fourcc, fps, size, True)

frame_count = 0

t1 = time.time()

while cap.isOpened():

ret, image_np = cap.read()

input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)

detections, predictions_dict, shapes = detect_fn(input_tensor)

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'][0].numpy(),

(detections['detection_classes'][0].numpy() + label_id_offset).astype(int),

detections['detection_scores'][0].numpy(),

category_index,

use_normalized_coordinates=True,

max_boxes_to_draw=200,

min_score_thresh=.50,

line_thickness=1,

agnostic_mode=False)

cv2.imshow('centernet_resnet101', image_np_with_detections)

out.write(image_np_with_detections)

frame_count += 1

if cv2.waitKey(1) == 13: # 13 is the Enter Key

break

t2 = time.time()

# calculate FPS

fps = str(float(frame_count / float(t2 - t1))) + ' FPS'

print("Frames processed: {}".format(frame_count))

print("Elapsed time: {:.2f}".format(float(t2 - t1)))

print("FPS: {}".format(fps))

# Release camera and close windows

cap.release()

cv2.destroyAllWindows()

---------------------------

More models can be found in the TensorFlow 2 Detection Model Zoo <https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md>_. To use a different model you will need the URL name of the specific model. This can be done as follows:

Right click on the Model name of the model you would like to use;
Click on Copy link address to copy the download link of the model;
Paste the link in a text editor of your choice. You should observe a link similar to download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz;
Copy the XXXXXXXXX part of the link and use it to replace the value of the MODEL_NAME variable in the code shown below;
Copy the YYYYYYYY part of the link and use it to replace the value of the MODEL_DATE variable in the code shown below.

reference:

https://chuanshuoge3.blogspot.com/2021/06/opencv-55-tensorflow-2-detection-model.html

https://www.youtube.com/watch?v=K0eDKO13O_s&list=PLZoTAELRMXVNvTfHyJxPRcQkpV8ubBwHo&index=6