Removing this file as the functionality is put in application file

detection.py

@@ -1,62 +0,0 @@
-from typing import List
-import cv2
-import torch
-import numpy as np
-import config as modelConfig
-from pytorch_grad_cam.utils.image import show_cam_on_image
-
-from yolo3 import YOLOv3
-from utils import cells_to_bboxes, non_max_suppression, draw_prediction_boxes, YoloGradCAM
-
-
-model = YOLOv3(num_classes=20)
-
-model.load_state_dict(torch.load("yolo3_model_trained1.pth", map_location="cpu"))
-model.eval()
-print("Yolov3 Model Loaded..")
-
-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 = draw_prediction_boxes(image.copy(), nms_boxes, class_labels=modelConfig.PASCAL_CLASSES)
-    if not enable_grad_cam:
-        return [plot_img]
-    
-    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, grad_cam_image]
-
-
-if __name__=="__main__":
-    image = cv2.imread("images/001155.jpg")
-    image = predict(image)
-
-    cv2.imshow("image", image)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()