Python_MachineLearningModel_Test.py

import numpy as np
import cv2
import time
import torch

model = torch.hub.load('ultralytics/yolov5', 'custom', 'enson.engine',force_reload=True)
model1 = torch.hub.load('ultralytics/yolov5', 'custom', 'char.engine',force_reload=True)
cap = cv2.VideoCapture(0)
prev_frame_time = 0
new_frame_time = 0

while(cap.isOpened()):
	ret, frame = cap.read()
	if not ret:
		break
	gray = frame
	gray = cv2.resize(gray, (500, 300))

	results = model(gray)
	for _, det in enumerate(results.xyxy[0]):
		# convert from tensor to numpy
		box = det.detach().cpu().numpy()[:5]
		# convert from float to integer
		box = [int(x) for x in box]
		x1, y1, x2, y2,name = box  # crop the license plate image part
		cropped = gray[y1:y2, x1:x2].copy()
		label = "plate"
		color = (0, 255, 255)
		# draw a box on original image
		cv2.rectangle(gray, (x1, y1), (x2, y2), (0, 255, 255), 2)
		t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]

		img2 = cropped.copy()
	

		results1=model1(img2)
		results1.pandas().xyxy[0].sort_values('xmin')
		yeni = str(results1.pandas().xyxy[0].sort_values('xmin').name.values)
		clean_text2 = ""
		for char in yeni:
			if char in "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ":
				clean_text2 += char
		yeni = clean_text2
		yeni = yeni.replace(" ", "")
		font = cv2.FONT_HERSHEY_SIMPLEX
		new_frame_time = time.time()
		fps = 1/(new_frame_time-prev_frame_time)
		prev_frame_time = new_frame_time
		fps = int(fps)
		fps = str(fps)
		cv2.putText(gray, fps, (7, 70), font, 3, (100, 255, 0), 3, cv2.LINE_AA)
		cv2.imshow('frame', gray)
		if (len(yeni)<9) and (len(yeni)>6):
			print(yeni)
			print(len(yeni))
		else:
			continue
	if cv2.waitKey(1) & 0xFF == ord('q'):
		break
cap.release()
cv2.destroyAllWindows()

insert this into Vision2023 Folder and activate virtual environment