pythonocreasyocr

Real time OCR lag

im trying to capture position of license plate with webcam feed using YOLOv4 tiny then input the result to easyOCR to extract the characters. The detection works well in real time, however when i apply the OCR the webcam stream become really laggy. Is there anyway i can improve this code to make it make it less laggy?

my YOLOv4 detection

#detection
while 1:
    #_, pre_img = cap.read()
    #pre_img= cv2.resize(pre_img, (640, 480))
    _, img = cap.read()
    #img = cv2.flip(pre_img,1)
    hight, width, _ = img.shape
    blob = cv2.dnn.blobFromImage(img, 1 / 255, (416, 416), (0, 0, 0), swapRB=True, crop=False)

    net.setInput(blob)

    output_layers_name = net.getUnconnectedOutLayersNames()

    layerOutputs = net.forward(output_layers_name)

    boxes = []
    confidences = []
    class_ids = []

    for output in layerOutputs:
        for detection in output:
            score = detection[5:]
            class_id = np.argmax(score)
            confidence = score[class_id]
            if confidence > 0.7:
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * hight)
                w = int(detection[2] * width)
                h = int(detection[3] * hight)
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)
                boxes.append([x, y, w, h])
                confidences.append((float(confidence)))
                class_ids.append(class_id)

    indexes = cv2.dnn.NMSBoxes(boxes, confidences, .5, .4)

    boxes = []
    confidences = []
    class_ids = []

    for output in layerOutputs:
        for detection in output:
            score = detection[5:]
            class_id = np.argmax(score)
            confidence = score[class_id]
            if confidence > 0.5:
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * hight)
                w = int(detection[2] * width)
                h = int(detection[3] * hight)

                x = int(center_x - w / 2)
                y = int(center_y - h / 2)

                boxes.append([x, y, w, h])
                confidences.append((float(confidence)))
                class_ids.append(class_id)

    indexes = cv2.dnn.NMSBoxes(boxes, confidences, .8, .4)
    font = cv2.FONT_HERSHEY_PLAIN
    colors = np.random.uniform(0, 255, size=(len(boxes), 3))
    if len(indexes) > 0:
        for i in indexes.flatten():
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            confidence = str(round(confidences[i], 2))
            color = colors[i]
            cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
           # detection= cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
            detected_image = img[y:y+h, x:x+w]
            cv2.putText(img, label + " " + confidence, (x, y + 400), font, 2, color, 2)
            #print(detected_image)
            cv2.imshow('detection',detected_image)

            cv2.imwrite('lp5.jpg',detected_image)
            cropped_image = cv2.imread('lp5.jpg')
            cv2.waitKey(5000)
            print("system is waiting")
            result = OCR(cropped_image)
            print(result)

easy OCR function

def OCR(cropped_image):
    reader = easyocr.Reader(['en'], gpu=False)  # what the reader expect from  the image
    result = reader.readtext(cropped_image)
    text = ''
    for result in result:
        text += result[1] + ' '

    spliced = (remove(text))
    return spliced
Solution

  • There are several points.

    1. cv2.waitKey(5000) in your loop causes some delay even though you keep pressing a key. So remove it if you are not debugging.

    2. You are saving a detected region into a JPEG image file and loading it each time. So pass the region(Numpy array) on the OCR module directly.

    3. EasyOCR is a DNN model based on ResNet, but you are not using a GPU(gpu=False). So use GPU.(See this benchmark by Liao.)

    4. You are creating an easyocr.Reader instance each time in a loop. Creating it requires to load and initialize a DNN model. This is a huge workload causing the major bottleneck. So create only single instance before the loop and reuse it inside a loop.