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import cv2
from ultralytics import YOLO
classes = ['ain', 'al', 'aleff','bb','dal','dha','dhad','fa','gaaf','ghain','ha','haa','jeem','kaaf','khaa','la','laam',
'meem','nun','ra','saad','seen','sheen','ta','taa','thaa','thal','toot','waw','ya','yaa','zay']
TargetMapper = dict(zip(range(32),classes))
model = YOLO('best.pt')
def image_inference(image_path):
print(image_path)
image = cv2.imread(image_path)
outputs = model.predict(source=image_path)
results = outputs[0]
for i,det in enumerate(results.boxes.xyxy):
cls = TargetMapper[results.boxes.cls.numpy()[i]]
#det = results.boxes.xyxy[0]
cv2.rectangle(
image,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA
)
cv2.putText(image, cls, (int(det[0]), int(det[1])-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
def video_inference(video_path) :
cap = cv2.VideoCapture(video_path)
while(cap.isOpened()):
ret, frame = cap.read()
if ret:
frame_copy = frame.copy()
outputs = model.predict(source=frame)
results = outputs[0]#.cpu().numpy()
for i, det in enumerate(results.boxes.xyxy):
cls = TargetMapper[results.boxes.cls.numpy()[i]]
cv2.rectangle(
frame_copy,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA
)
cv2.putText(frame_copy, cls, (int(det[0]), int(det[1])-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2)
yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB) |