app.py

import fileinput
import itertools
import os
import re
from copy import deepcopy
from operator import itemgetter
from pathlib import Path
from typing import Union

!pip install opencv-python
!pip install --quiet gradio==2.9.0b0
import cv2  # type: ignore
import gradio as gr  # type: ignore
gr.Interface(lambda x:x, "textbox", "textbox").launch()
import numpy as np
import torch
from deep_sort_realtime.deepsort_tracker import DeepSort  # type: ignore
from paddleocr import PaddleOCR  # type: ignore

if not os.path.isfile("weights.pt"):
    weights_url = "https://archive.org/download/anpr_weights/weights.pt"
    os.system(f"wget {weights_url}")

if not os.path.isdir("examples"):
    examples_url = "https://archive.org/download/anpr_examples_202208/examples.tar.gz"
    os.system(f"wget {examples_url}")
    os.system("tar -xvf examples.tar.gz")
    os.system("rm -rf examples.tar.gz")


def prepend_text(filename: Union[str, Path], text: str):
    with fileinput.input(filename, inplace=True) as file:
        for line in file:
            if file.isfirstline():
                print(text)
            print(line, end="")


if not os.path.isdir("yolov7"):
    yolov7_repo_url = "https://github.com/WongKinYiu/yolov7"
    os.system(f"git clone {yolov7_repo_url}")
    # Fix import errors
    for file in [
        "yolov7/models/common.py",
        "yolov7/models/experimental.py",
        "yolov7/models/yolo.py",
        "yolov7/utils/datasets.py",
    ]:
        prepend_text(file, "import sys\nsys.path.insert(0, './yolov7')")

from yolov7.models.experimental import attempt_load  # type: ignore
from yolov7.utils.datasets import letterbox  # type: ignore
from yolov7.utils.general import check_img_size  # type: ignore
from yolov7.utils.general import non_max_suppression  # type: ignore
from yolov7.utils.general import scale_coords  # type: ignore
from yolov7.utils.plots import plot_one_box  # type: ignore
from yolov7.utils.torch_utils import TracedModel, select_device  # type: ignore

weights = "weights.pt"
device_id = "cpu"
image_size = 640
trace = True

# Initialize
device = select_device(device_id)
half = device.type != "cpu"  # half precision only supported on CUDA

# Load model
model = attempt_load(weights, map_location=device)  # load FP32 model
stride = int(model.stride.max())  # model stride
imgsz = check_img_size(image_size, s=stride)  # check img_size

if trace:
    model = TracedModel(model, device, image_size)

if half:
    model.half()  # to FP16

if device.type != "cpu":
    model(
        torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))
    )  # run once

model.eval()

# Load OCR

paddle = PaddleOCR(lang="en")


def detect_plate(source_image):
    # Padded resize
    img_size = 640
    stride = 32
    img = letterbox(source_image, img_size, stride=stride)[0]

    # Convert
    img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
    img = np.ascontiguousarray(img)
    img = torch.from_numpy(img).to(device)
    img = img.half() if half else img.float()  # uint8 to fp16/32
    img /= 255.0  # 0 - 255 to 0.0 - 1.0
    if img.ndimension() == 3:
        img = img.unsqueeze(0)

    with torch.no_grad():
        # Inference
        pred = model(img, augment=True)[0]

    # Apply NMS
    pred = non_max_suppression(pred, 0.25, 0.45, classes=0, agnostic=True)

    plate_detections = []
    det_confidences = []

    # Process detections
    for i, det in enumerate(pred):  # detections per image
        if len(det):
            # Rescale boxes from img_size to source image size
            det[:, :4] = scale_coords(
                img.shape[2:], det[:, :4], source_image.shape
            ).round()

            # Return results
            for *xyxy, conf, cls in reversed(det):
                coords = [
                    int(position)
                    for position in (torch.tensor(xyxy).view(1, 4)).tolist()[0]
                ]
                plate_detections.append(coords)
                det_confidences.append(conf.item())

    return plate_detections, det_confidences


def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=2.0, threshold=0):
    blurred = cv2.GaussianBlur(image, kernel_size, sigma)
    sharpened = float(amount + 1) * image - float(amount) * blurred
    sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
    sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
    sharpened = sharpened.round().astype(np.uint8)
    if threshold > 0:
        low_contrast_mask = np.absolute(image - blurred) < threshold
        np.copyto(sharpened, image, where=low_contrast_mask)
    return sharpened


def crop(image, coord):
    cropped_image = image[int(coord[1]) : int(coord[3]), int(coord[0]) : int(coord[2])]
    return cropped_image


def ocr_plate(plate_region):
    # Image pre-processing for more accurate OCR
    rescaled = cv2.resize(
        plate_region, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC
    )
    grayscale = cv2.cvtColor(rescaled, cv2.COLOR_BGR2GRAY)
    kernel = np.ones((1, 1), np.uint8)
    dilated = cv2.dilate(grayscale, kernel, iterations=1)
    eroded = cv2.erode(dilated, kernel, iterations=1)
    sharpened = unsharp_mask(eroded)

    # OCR the preprocessed image
    results = paddle.ocr(sharpened, det=False, cls=False)
    flattened = list(itertools.chain.from_iterable(results))
    plate_text, ocr_confidence = max(flattened, key=itemgetter(1), default=("", 0))

    # Filter out anything but uppercase letters, digits, hypens and whitespace.
    plate_text = re.sub(r"[^-A-Z0-9 ]", r"", plate_text).strip()

    if ocr_confidence == "nan":
        ocr_confidence = 0

    return plate_text, ocr_confidence


def get_plates_from_image(input):
    if input is None:
        return None
    plate_detections, det_confidences = detect_plate(input)
    plate_texts = []
    ocr_confidences = []
    detected_image = deepcopy(input)
    for coords in plate_detections:
        plate_region = crop(input, coords)
        plate_text, ocr_confidence = ocr_plate(plate_region)
        if ocr_confidence == 0:  # If OCR confidence is 0, skip this detection
            continue
        plate_texts.append(plate_text)
        ocr_confidences.append(ocr_confidence)
        plot_one_box(
            coords,
            detected_image,
            label=plate_text,
            color=[0, 150, 255],
            line_thickness=2,
        )
    return detected_image


def pascal_voc_to_coco(x1y1x2y2):
    x1, y1, x2, y2 = x1y1x2y2
    return [x1, y1, x2 - x1, y2 - y1]


def get_best_ocr(preds, rec_conf, ocr_res, track_id):
    for info in preds:
        # Check if it is current track id
        if info["track_id"] == track_id:
            # Check if the ocr confidenence is maximum or not
            if info["ocr_conf"] < rec_conf:
                info["ocr_conf"] = rec_conf
                info["ocr_txt"] = ocr_res
            else:
                rec_conf = info["ocr_conf"]
                ocr_res = info["ocr_txt"]
            break
    return preds, rec_conf, ocr_res


def get_plates_from_video(source):
    if source is None:
        return None

    # Create a VideoCapture object
    video = cv2.VideoCapture(source)

    # Default resolutions of the frame are obtained. The default resolutions are system dependent.
    # We convert the resolutions from float to integer.
    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = video.get(cv2.CAP_PROP_FPS)

    # Define the codec and create VideoWriter object.
    temp = f"{Path(source).stem}_temp{Path(source).suffix}"
    export = cv2.VideoWriter(
        temp, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)
    )

    # Intializing tracker
    tracker = DeepSort(embedder_gpu=False)

    # Initializing some helper variables.
    preds = []
    total_obj = 0

    while True:
        ret, frame = video.read()
        if ret == True:
            # Run the ANPR algorithm
            bboxes, scores = detect_plate(frame)
            # Convert Pascal VOC detections to COCO
            bboxes = list(map(lambda bbox: pascal_voc_to_coco(bbox), bboxes))

            if len(bboxes) > 0:
                # Storing all the required info in a list.
                detections = [
                    (bbox, score, "number_plate") for bbox, score in zip(bboxes, scores)
                ]

                # Applying tracker.
                # The tracker code flow: kalman filter -> target association(using hungarian algorithm) and appearance descriptor.
                tracks = tracker.update_tracks(detections, frame=frame)

                # Checking if tracks exist.
                for track in tracks:
                    if not track.is_confirmed() or track.time_since_update > 1:
                        continue

                    # Changing track bbox to top left, bottom right coordinates
                    bbox = [int(position) for position in list(track.to_tlbr())]

                    for i in range(len(bbox)):
                        if bbox[i] < 0:
                            bbox[i] = 0

                    # Cropping the license plate and applying the OCR.
                    plate_region = crop(frame, bbox)
                    plate_text, ocr_confidence = ocr_plate(plate_region)

                    # Storing the ocr output for corresponding track id.
                    output_frame = {
                        "track_id": track.track_id,
                        "ocr_txt": plate_text,
                        "ocr_conf": ocr_confidence,
                    }

                    # Appending track_id to list only if it does not exist in the list
                    # else looking for the current track in the list and updating the highest confidence of it.
                    if track.track_id not in list(
                        set(pred["track_id"] for pred in preds)
                    ):
                        total_obj += 1
                        preds.append(output_frame)
                    else:
                        preds, ocr_confidence, plate_text = get_best_ocr(
                            preds, ocr_confidence, plate_text, track.track_id
                        )

                    # Plotting the prediction.
                    plot_one_box(
                        bbox,
                        frame,
                        label=f"{str(track.track_id)}. {plate_text}",
                        color=[255, 150, 0],
                        line_thickness=3,
                    )

            # Write the frame into the output file
            export.write(frame)
        else:
            break

    # When everything done, release the video capture and video write objects

    video.release()
    export.release()

    # Compressing the output video for smaller size and web compatibility.
    output = f"{Path(source).stem}_detected{Path(source).suffix}"
    os.system(
        f"ffmpeg -y -i {temp} -c:v libx264 -b:v 5000k -minrate 1000k -maxrate 8000k -pass 1 -c:a aac -f mp4 /dev/null && ffmpeg -i {temp} -c:v libx264 -b:v 5000k -minrate 1000k -maxrate 8000k -pass 2 -c:a aac -movflags faststart {output}"
    )
    os.system(f"rm -rf {temp} ffmpeg2pass-0.log ffmpeg2pass-0.log.mbtree")

    return output


with gr.Blocks() as demo:
    gr.Markdown('### <h3 align="center">Automatic Number Plate Recognition</h3>')
    gr.Markdown(
        "This AI was trained to detect and recognize number plates on vehicles."
    )
    with gr.Tabs():
        with gr.TabItem("Image"):
            with gr.Row():
                image_input = gr.Image()
                image_output = gr.Image()
                image_input.change(
                    get_plates_from_image, inputs=image_input, outputs=image_output
                )
            gr.Examples(
                [
                    ["examples/test_image_1.jpg"],
                    ["examples/test_image_2.jpg"],
                    ["examples/test_image_3.png"],
                    ["examples/test_image_4.jpeg"],
                ],
                [image_input],
                image_output,
                get_plates_from_image,
                cache_examples=True,
            )
        with gr.TabItem("Video"):
            with gr.Row():
                video_input = gr.Video(format="mp4")
                video_output = gr.Video(format="mp4")
                video_input.change(
                    get_plates_from_video, inputs=video_input, outputs=video_output
                )
            gr.Examples(
                [["examples/test_video_1.mp4"]],
                [video_input],
                video_output,
                get_plates_from_video,
                cache_examples=True,
            )
    gr.Markdown("[@itsyoboieltr](https://github.com/itsyoboieltr)")

demo.launch()