Duplicate from itsyoboieltr/anpr-yolov7

Co-authored-by: Norbert Elter <itsyoboieltr@users.noreply.huggingface.co>

.gitignore

@@ -0,0 +1,36 @@
+# Logs
+logs
+*.log
+*.log.mbtree
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+pnpm-lock.yaml
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
+__pycache__
+.ipynb_checkpoints
+gradio_cached_examples
+*.pt
+deep_sort
+yolov7
+*.mp4
+*.tar.gz
+examples

README.md

@@ -0,0 +1,11 @@
+---
+title: Automatic Number-Plate Recognition (YOLOV7)
+emoji: 🚘
+colorFrom: red
+colorTo: gray
+sdk: gradio
+sdk_version: 3.1.4
+app_file: app.py
+pinned: false
+duplicated_from: itsyoboieltr/anpr-yolov7
+---

app.py

@@ -0,0 +1,368 @@
+import fileinput
+import itertools
+import os
+import re
+from copy import deepcopy
+from operator import itemgetter
+from pathlib import Path
+from typing import Union
+
+import cv2  # type: ignore
+import gradio as gr  # type: ignore
+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()

requirements.txt

@@ -0,0 +1,9 @@
+numpy
+opencv-python
+paddlepaddle
+paddleocr
+deep-sort-realtime
+torch
+torchvision
+tensorflow
+seaborn