def run_lpr():
logging.info(f'Debug mode {app.debug}')
if request.method == 'POST':
file = request.files['file']
img_bytes = file.read()
file.close()
if(img_bytes is not None):
nparr = np.fromstring(img_bytes, np.uint8)
inputImage = cv.imdecode(nparr, cv.IMREAD_COLOR)
# TODO: state management: avoid loading net for every request
yolo = Yolo(img_width=1056, img_height=576,
debug=DEBUG, confidence_threshold=0.6, non_max_supress_theshold=0.4,
classes_filename='../config/classes.names',
model_architecture_filename="../config/yolov3_license_plates.cfg",
model_weights_filename="../config/yolov3_license_plates_last.weights",
output_directory='../debug/')
roi_imgs = yolo.detect(inputImage)
ocr = OCR(model_filename="../config/emnist_net_custom.pt", num_classes=36, use_cuda=False, debug=DEBUG)
index = 0
for roi_img in roi_imgs:
logging.info(f'\n\nProcessing ROI {index}')
box = [yolo.bounding_boxes[index][0], yolo.bounding_boxes[index][1], yolo.bounding_boxes[index][2], yolo.bounding_boxes[index][3]]
predict(yolo.img, roi_img, box, str(index), (0,255,0), ocr)
index += 1
# API response: the highest confidence one
logging.info(f'\n\n---Processing the Highest Confidence ROI---\n')
bounding_box = None
emnist_net_preds = None
tesseract_preds = None
if(yolo.highest_object_confidence > 0 and yolo.roi_img is not None):
bounding_box = {
'x': yolo.box_x,
'y': yolo.box_y,
'w': yolo.box_w,
'h': yolo.box_h
}
_, emnist_net_preds, tesseract_preds = predict(yolo.img, yolo.roi_img, [yolo.box_x, yolo.box_y, yolo.box_w, yolo.box_h], "", (255,255,0), ocr)
if(DEBUG):
cv.imwrite("../debug/result.jpg", yolo.img.astype(np.uint8))
data = {
'bounding_box': bounding_box,
'confidence': yolo.highest_object_confidence,
'classId': str(yolo.classId_highest_object),
'emnist_net_preds': emnist_net_preds,
'tesseract_preds': tesseract_preds
}
response = jsonify(data)
response.status_code = 200
return response
t0 = time.time()
while True:
count = src_skip
while count>=1:
count -= 1
grabbed, image = vid.read()
if not grabbed: break
if src_mirror:
image = cv2.flip(image, 1)
if src_scale!=1:
image = cv2.resize(image,(0,0),fx=src_scale,fy=src_scale)
# Apply yolo
yolo.detect(image,0.25,0.3)
yolo.annotate(image)
list = []
if len(yolo.idxs) > 0:
for i in yolo.idxs.flatten():
list.append( yolo.LABELS[yolo.classIDs[i]] )
print ( list )
t1 = time.time()
elap = t1-t0
t0 = t1
fps = round(1/elap)
text = ("%d fps"%fps)
cv2.putText(image,text,(10,20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, [0,128,255], 2)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Testing YOLO...')
parser.add_argument('--image', help='Path to image file.')
args = parser.parse_args()
yolo = Yolo(
img_width=1056,
img_height=576,
debug=True,
confidence_threshold=0.6,
non_max_supress_theshold=0.4,
classes_filename='../config/classes.names',
model_architecture_filename="../config/yolov3_license_plates.cfg",
model_weights_filename="../config/yolov3_license_plates_last.weights",
output_directory='../debug/')
# Open the image file
if not os.path.isfile(args.image):
print("Input image file ", args.image, " doesn't exist")
sys.exit(1)
cap = cv.VideoCapture(args.image)
# get frame
hasFrame, frame = cap.read()
if hasFrame:
yolo.detect(frame)
else:
print("Frame not found!")
classes_filename='../config/classes.names',
model_architecture_filename="../config/yolov3_license_plates.cfg",
model_weights_filename="../config/yolov3_license_plates_last.weights",
output_directory='../debug/',
output_image=False)
ocr = OCR(model_filename="../config/attention_ocr_model.pth",
use_cuda=False,
threshold=0.7)
cap = cv.VideoCapture(args.input)
while (cap.isOpened()):
hasFrame, frame = cap.read()
if hasFrame:
roi_imgs = yolo.detect(frame)
index = 0
for roi_img in roi_imgs:
box = [
yolo.bounding_boxes[index][0],
yolo.bounding_boxes[index][1],
yolo.bounding_boxes[index][2],
yolo.bounding_boxes[index][3]
]
pred = ocr.predict(roi_img)
draw_bounding_box(input_image=frame,
bounding_box=box,
label=pred,
background_color=(0, 255, 0),
ocr=ocr)
index += 1
from yolo import Yolo
import cv2
imagepath = "images/dining_table.jpg"
yolofiles = {
"config": "yolo-coco/yolov3.cfg",
"weights": "yolo-coco/yolov3.weights",
"names": "yolo-coco/coco.names"
}
yolo = Yolo(yolofiles)
# load our input image and grab its spatial dimensions
image = cv2.imread(imagepath)
yolo.detect(image) #,confidence=0.0,threshold=0.0)
yolo.annotate(image)
# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)
