app.py

app.py

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+import gradio as gr
+from model import Lightning_YOLO
+import config
+from utils import non_max_suppression, cells_to_bboxes, draw_bounding_boxes
+import torch
+
+scaled_anchors = config.scaled_anchors
+
+model = Lightning_YOLO()
+model.load_state_dict(torch.load("yolov3.pth", map_location="cpu"), strict=False)
+model.eval()
+
+def inference(image, threst = 0.5, iou_tresh = 0.5):
+    transformed_image = config.transforms(image=image)["image"].unsqueeze(0)
+    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_tresh, threshold=threst, box_format="midpoint",
+    )
+    plot_img = draw_bounding_boxes(image.copy(), nms_boxes, class_labels=config.PASCAL_CLASSES)
+
+    return plot_img
+
+def visualize_gradcam(image, target_layer=-5, show_cam=True, transparency=0.5):
+  # if show_cam:
+  #     cam = YoloCAM(model=model, target_layers=[model.layers[target_layer]], use_cuda=False)
+  #     transformed_image = config.transforms(image=image)["image"].unsqueeze(0)
+  #     grayscale_cam = cam(transformed_image, scaled_anchors)[0, :, :]
+  #     img = cv2.resize(image, (416, 416))
+  #     img = np.float32(img) / 255
+  #     cam_image = show_cam_on_image(img, grayscale_cam, use_rgb=True, image_weight=transparency)
+  # else:
+  #     cam_image = image
+      
+  # return cam_image
+  pass
+
+window1 = gr.Interface(
+    inference,
+    inputs=[
+        gr.Image(label="Input Image"),
+        gr.Slider(0, 1, value=0.5, step=0.1, label="Threshold", info="Set Threshold value"),
+        gr.Slider(0, 1, value=0.5, step=0.1, label="IOU Threshold", info="Set IOU Threshold value"),
+    ],
+    outputs=[
+        gr.Image(label="YoloV3 Object Detection"),
+    ],
+    # examples=ex1,
+)
+
+
+window2 = gr.Interface(
+    visualize_gradcam,
+    inputs=[
+        gr.Image(label="Input Image"),
+        gr.Slider(-5, -2, value=-3, step=-1, label="Network Layer", info="GRAD-CAM Layer to visualize?"),
+        gr.Checkbox(label="GradCAM", value=True, info="Visualize Class Activation Maps ??"), 
+        gr.Slider(0, 1, value=0.5, step=0.1, label="Transparency", info="Set Transparency of GRAD-CAMs"), 
+    ],
+    outputs=[
+        gr.Image(label="Grad-CAM Visualization"),
+    ],
+    # examples=ex2,
+)
+
+
+app = gr.TabbedInterface([window1, window2], ["YOLO V3 Detection", "GradCAM Visualization"])
+app.launch()