detection_custom.py

#================================================================
#
#   File name   : detection_custom.py
#   Author      : PyLessons
#   Created date: 2020-08-14
#   Website     : https://pylessons.com/
#   GitHub      : https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3
#   Description : object detection image and video example
#
#================================================================
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.python.saved_model import tag_constants
from yolov3.yolov4 import Create_Yolo
from yolov3.utils import load_yolo_weights, detect_image, detect_video, detect_realtime
from yolov3.configs import *
import time
import mss
sct = mss.mss()
monitor = {"top": 40, "left": 0, "width": 1400, "height": 800}

image_path   = "./IMAGES/plate_2.jpg"
video_path   = "./IMAGES/test.mp4"

if YOLO_FRAMEWORK == "tf": # TensorFlow detection
    if YOLO_TYPE == "yolov4":
        Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS
    if YOLO_TYPE == "yolov3":
        Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS
    yolo = Create_Yolo(input_size=YOLO_INPUT_SIZE, CLASSES=TRAIN_CLASSES)#YOLO_COCO_CLASSES)
    yolo.load_weights(YOLO_CUSTOM_WEIGHTS) # use custom weights
    
elif YOLO_FRAMEWORK == "trt": # TensorRT detection
    saved_model_loaded = tf.saved_model.load(YOLO_CUSTOM_WEIGHTS, tags=[tag_constants.SERVING])
    signature_keys = list(saved_model_loaded.signatures.keys())
    yolo = saved_model_loaded.signatures['serving_default']

#detect_image(yolo, image_path, "./IMAGES/plate_1_detect.jpg", input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
#detect_video(yolo, video_path, './IMAGES/detected.mp4', input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
#detect_realtime(yolo, '', input_size=YOLO_INPUT_SIZE, show=True, CLASSES=TRAIN_CLASSES, rectangle_colors=(255, 0, 0))
times = []
while True:
    t1 = time.time()
    img = np.array(sct.grab(monitor))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = detect_image(yolo, img, "", input_size=YOLO_INPUT_SIZE, show=False, CLASSES=TRAIN_CLASSES, rectangle_colors=(255,0,0))
    cv2.imshow("title", img)
    if cv2.waitKey(25) & 0xFF == ord("q"):
        cv2.destroyAllWindows()
        #break
    t2 = time.time()
    times.append(t2-t1)
    times = times[-20:]
    ms = sum(times)/len(times)*1000
    fps = 1000 / ms
    
    print("Time: {:.2f}ms, {:.1f} FPS".format(ms, fps))