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parking_gate.py
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parking_gate.py
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import cv2
import numpy as np
from dynamikontrol import Module
CONFIDENCE = 0.9
THRESHOLD = 0.3
LABELS = ['Car', 'Plate']
CAR_WIDTH_TRESHOLD = 500
cap = cv2.VideoCapture(0)
net = cv2.dnn.readNetFromDarknet('cfg/yolov4-ANPR.cfg', 'yolov4-ANPR.weights')
module = Module()
while cap.isOpened():
ret, img = cap.read()
if not ret:
break
H, W, _ = img.shape
blob = cv2.dnn.blobFromImage(img, scalefactor=1/255., size=(416, 416), swapRB=True)
net.setInput(blob)
output = net.forward()
boxes, confidences, class_ids = [], [], []
for det in output:
box = det[:4]
scores = det[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > CONFIDENCE:
cx, cy, w, h = box * np.array([W, H, W, H])
x = cx - (w / 2)
y = cy - (h / 2)
boxes.append([int(x), int(y), int(w), int(h)])
confidences.append(float(confidence))
class_ids.append(class_id)
idxs = cv2.dnn.NMSBoxes(boxes, confidences, CONFIDENCE, THRESHOLD)
if len(idxs) > 0:
for i in idxs.flatten():
x, y, w, h = boxes[i]
cv2.rectangle(img, pt1=(x, y), pt2=(x + w, y + h), color=(0, 0, 255), thickness=2)
cv2.putText(img, text='%s %.2f %d' % (LABELS[class_ids[i]], confidences[i], w), org=(x, y - 10), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(0, 0, 255), thickness=2)
if class_ids[i] == 0:
if w > CAR_WIDTH_TRESHOLD:
module.motor.angle(80)
else:
module.motor.angle(0)
else:
module.motor.angle(0)
cv2.imshow('result', img)
if cv2.waitKey(1) == ord('q'):
break