app.py

import torch
import pandas as pd
import numpy as np
import gradio as gr
from PIL import Image
from torch.nn import functional as F
from collections import OrderedDict
from torchvision import transforms
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_lightning import LightningModule, Trainer, seed_everything
import albumentations as A
from albumentations.pytorch import ToTensorV2
import torchvision.transforms as T
from model import YOLOv3
from train import YOLOTraining
import config
from utils import *
import numpy as np
import cv2
import albumentations as A
from utils import *
import random
from albumentations.pytorch import ToTensorV2

model = YOLOv3(num_classes=config.NUM_CLASSES)
model = YOLOTraining(model)
model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)
model.eval()

def yolo_predict(image: np.ndarray, iou_thresh: float = 0.5, thresh: float = 0.5):

    transforms =  A.Compose(
    [
        A.LongestMaxSize(max_size=config.IMAGE_SIZE),
        A.PadIfNeeded(
            min_height=config.IMAGE_SIZE, min_width=config.IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
        ),
        A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
        ToTensorV2(),
    ],
    )
    with torch.no_grad():
        transformed_image = transforms(image=image)["image"].unsqueeze(0).to(config.DEVICE)
        output = model(transformed_image)

        bboxes = [[] for _ in range(1)]
        for i in range(3):
            batch_size, A1, S, _, _ = output[i].shape
            anchor = config.SCALED_ANCHORS[i].to(config.DEVICE)
            boxes_scale_i = cells_to_bboxes(
                output[i].to(config.DEVICE), anchor, S=S, is_preds=True
            )
            for idx, (box) in enumerate(boxes_scale_i):
                bboxes[idx] += box

    nms_boxes = non_max_suppression(
        bboxes[0], iou_threshold=iou_thresh, threshold=thresh, box_format="midpoint",
    )
    plot_img = draw_predictions(image, nms_boxes, class_labels=config.PASCAL_CLASSES)
    
    return [plot_img]


def draw_predictions(image: np.ndarray, boxes: list[list], class_labels: list[str]) -> np.ndarray:
    """Plots predicted bounding boxes on the image"""

    colors = [[random.randint(0, 255) for _ in range(3)] for name in class_labels]

    im = np.array(image)
    height, width, _ = im.shape
    bbox_thick = int(0.6 * (height + width) / 600)

    # Create a Rectangle patch
    for box in boxes:
        assert len(box) == 6, "box should contain class pred, confidence, x, y, width, height"
        class_pred = box[0]
        conf = box[1]
        box = box[2:]
        upper_left_x = box[0] - box[2] / 2
        upper_left_y = box[1] - box[3] / 2

        x1  = int(upper_left_x * width)
        y1 = int(upper_left_y * height)

        x2 = x1 + int(box[2] * width)
        y2 = y1 + int(box[3] * height)

        cv2.rectangle(
            image,
            (x1, y1), (x2, y2),
            color=colors[int(class_pred)],
            thickness=bbox_thick
        )
        text = f"{class_labels[int(class_pred)]}: {conf:.2f}"
        t_size = cv2.getTextSize(text, 0, 0.7, thickness=bbox_thick // 2)[0]
        c3 = (x1 + t_size[0], y1 - t_size[1] - 3)

        cv2.rectangle(image, (x1, y1), c3, colors[int(class_pred)], -1)
        cv2.putText(
            image,
            text,
            (x1, y1 - 2),
            cv2.FONT_HERSHEY_SIMPLEX,
            0.7,
            (0, 0, 0),
            bbox_thick // 2,
            lineType=cv2.LINE_AA,
        )

    return image

demo = gr.Interface(
    fn=yolo_predict,
    inputs=[
        gr.Image(shape=(config.IMAGE_SIZE,config.IMAGE_SIZE), label="Input Image"),
        gr.Slider(0, 1, value=0.5, step=0.05, label="IOU Threshold"),
        gr.Slider(0, 1, value=0.5, step=0.05, label="Threshold")
    ],
    outputs=gr.Gallery(rows=1, columns=1),
    examples=[
       ["examples/000001.jpg", 0.5, 0.5],
       ["examples/000002.jpg", 0.5, 0.5],
       ["examples/000003.jpg", 0.5, 0.5],
       ["examples/000004.jpg", 0.5, 0.5],
       ["examples/000005.jpg", 0.5, 0.5],
       ["examples/000006.jpg", 0.5, 0.5],
       ["examples/000007.jpg", 0.5, 0.5],
       ["examples/000008.jpg", 0.5, 0.5],
       ["examples/000009.jpg", 0.5, 0.5],
       ["examples/000010.jpg", 0.5, 0.5]
       ]
)

demo.launch()