Update app.py

app.py

@@ -1,36 +1,136 @@
-# Gradio app for YOLOv3
-
+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 display import inference, draw_predictions
+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()
 
-gr.Interface(
-    inference,
-    inputs=[
-        gr.Image(label="Input Image"),
-        gr.Slider(0, 1, value=0.75, label="IOU Threshold"),
-        gr.Slider(0, 1, value=0.75, label="Threshold"),      
+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(),
     ],
-    outputs=gr.Gallery(rows=2, columns=1),
-    title = "Object Detection : YoloV3 on PASCAL VOC Dataset From Scratch" 
+    )
+    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)
     
-    ,examples=[
-       ["Examples/000001.jpg", 0.75, 0.75, True, 0.5],
-       ["Examples/000002.jpg", 0.75, 0.75, True, 0.5],
-       ["Examples/000003.jpg", 0.75, 0.75, True, 0.5],
-       ["Examples/000004.jpg", 0.75, 0.75, True, 0.5],
-       ["Examples/000005.jpg", 0.75, 0.75, True, 0.5],
-       ["Examples/000006.jpg", 0.75, 0.75, True, 0.5],
-       ["Examples/000007.jpg", 0.75, 0.75, True, 0.5],
-       ["Examples/000008.jpg", 0.75, 0.75, True, 0.5],
-       ["Examples/000009.jpg", 0.75, 0.75, True, 0.5],
-       ["Examples/000010.jpg", 0.75, 0.75, True, 0.5]
+    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]
        ]
-      
-    ,
-    layout="horizontal"
-).launch()
+)
+
+demo.launch()