File size: 4,466 Bytes
4258cd5 f375838 4258cd5 f375838 4258cd5 f375838 4258cd5 f375838 4258cd5 f375838 4258cd5 f375838 4258cd5 f375838 4258cd5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
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() |