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from typing import List
import cv2
import torch
import numpy as np
import gradio as gr
import config as modelConfig
from pytorch_grad_cam.utils.image import show_cam_on_image
from yolov3 import YOLOv3
import utils
from utils import cells_to_bboxes, non_max_suppression, draw_bounding_boxes, YoloGradCAM
model = YOLOv3(num_classes=len(modelConfig.PASCAL_CLASSES))
optimizer = torch.optim.Adam(model.parameters(), lr=0.00072/100, weight_decay=1e-4)
utils.load_checkpoint("checkpoint.pth.tar", model, optimizer, 0.00072/100)
scaled_anchors = (
torch.tensor(modelConfig.ANCHORS)
* torch.tensor(modelConfig.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
).to(modelConfig.DEVICE)
yolo_grad_cam = YoloGradCAM(model=model, target_layers=[model.layers[-2]], use_cuda=False)
@torch.inference_mode()
def detect_objects(image: np.ndarray, iou_thresh: float = 0.5, thresh: float = 0.4, enable_grad_cam: bool = False, transparency: float = 0.5) -> List[np.ndarray]:
transformed_image = modelConfig.transforms(image=image)["image"].unsqueeze(0)
#transformed_image = transformed_image.cuda()
output = model(transformed_image)
bboxes = [[] for _ in range(1)]
for i in range(3):
batch_size, A, S, _, _ = output[i].shape
anchor = scaled_anchors[i]
boxes_scale_i = cells_to_bboxes(
output[i], 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_with_bboxes = draw_bounding_boxes(image.copy(), nms_boxes, class_labels=modelConfig.PASCAL_CLASSES)
if not enable_grad_cam:
return [plot_img_with_bboxes]
grayscale_cam = yolo_grad_cam(transformed_image, scaled_anchors)[0, :, :]
img = cv2.resize(image, (416, 416))
img = np.float32(img) / 255
grad_cam_image = show_cam_on_image(img, grayscale_cam, use_rgb=True, image_weight=transparency)
return [plot_img_with_bboxes, grad_cam_image]
def inference(
image: np.ndarray,
iou_thresh: float, thresh: float,
enable_grad_cam: str,
transparency: float,
):
results = detect_objects(image, iou_thresh, thresh, enable_grad_cam, transparency)
return results
title = "Object detection application using YoloV3 Model"
description = f"Object detection application using pre-trained YoloV3 model for Pascal VOC dataset. This app has GradCAM option also. \n The 20 classes in Pascal voc dataset are : {', '.join(modelConfig.PASCAL_CLASSES)}"
examples = [
["images/000811.jpg", 0.6, 0.6, True, 0.6],
["images/000830.jpg", 0.5, 0.5, True, 0.6],
["images/000842.jpg", 0.6, 0.6, True, 0.6],
["images/001114.jpg", 0.4, 0.5, True, 0.6],
["images/001133.jpg", 0.7, 0.7, True, 0.6],
["images/001155.jpg", 0.7, 0.69, True, 0.6],
["images/000008.jpg", 0.66, 0.69, True, 0.6],
["images/000031.jpg", 0.6, 0.6, True, 0.6],
["images/000175.jpg", 0.6, 0.6, True, 0.6],
]
demo = gr.Interface(
inference,
inputs=[
gr.Image(label="Input Image"),
gr.Slider(0, 1, value=0.5, label="IOU Threshold"),
gr.Slider(0, 1, value=0.4, label="Threshold"),
gr.Checkbox(label="Show Grad Cam"),
gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"),
],
outputs=[
gr.Gallery(rows=2, columns=1),
],
title=title,
description=description,
examples=examples,
)
demo.launch(debug=True) |