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f877283 daff899 389d74b e81ec5b 389d74b e81ec5b 389d74b e81ec5b 9332dcc 389d74b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | # Imports
import os
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import torch, torchvision
from torchvision import transforms
import numpy as np
import gradio as gr
from PIL import Image
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
import gradio as gr
import albumentations as A
from albumentations.pytorch import ToTensorV2
import config
import utils
import config
from torchvision import transforms
import torch.optim as optim
scaled_anchors = (
torch.tensor(config.ANCHORS)
* torch.tensor(config.S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
).to('cpu')
test_transforms_exp = A.Compose(
[
A.LongestMaxSize(max_size=config.IMAGE_SIZE),
A.PadIfNeeded(
min_height=config.IMAGE_SIZE, min_width=config.IMAGE_SIZE
),
A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
ToTensorV2(),
]
)
num_classes = 20
IMAGE_SIZE = 416
from model import YOLOv3
import config
import torch
model = YOLOv3(num_classes=num_classes)
model.load_state_dict(torch.load(r"./model_40.pth", map_location=torch.device('cpu')))
model.eval()
classes = config.PASCAL_CLASSES
def plot_image(image, boxes):
"""Plots predicted bounding boxes on the image"""
cmap = plt.get_cmap("tab20b")
class_labels = config.COCO_LABELS if config.DATASET=='COCO' else config.PASCAL_CLASSES
colors = [cmap(i) for i in np.linspace(0, 1, len(class_labels))]
im = np.array(image)
height, width, _ = im.shape
# Create figure and axes
fig, ax = plt.subplots(1)
# Display the image
ax.imshow(im)
# box[0] is x midpoint, box[2] is width
# box[1] is y midpoint, box[3] is height
# 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]
box = box[2:]
upper_left_x = box[0] - box[2] / 2
upper_left_y = box[1] - box[3] / 2
rect = patches.Rectangle(
(upper_left_x * width, upper_left_y * height),
box[2] * width,
box[3] * height,
linewidth=2,
edgecolor=colors[int(class_pred)],
facecolor="none",
)
# Add the patch to the Axes
ax.add_patch(rect)
plt.text(
upper_left_x * width,
upper_left_y * height,
s=class_labels[int(class_pred)],
color="white",
verticalalignment="top",
bbox={"color": colors[int(class_pred)], "pad": 0},
)
fig.canvas.draw()
image_array = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')
image_array = image_array.reshape(fig.canvas.get_width_height()[::-1] + (3,))
return image_array
class outCallBack:
def __init__(self):
pass
def __call__(self, out):
in_shape=np.unravel_index(torch.argmax(out[..., :1]), out[..., :1].shape)
arg_max = out[in_shape[:-1]][5:].max()
return arg_max
def inference(input_img, transparency = 0.5, iou = 0.5, threshold = 0.5):
transform = test_transforms_exp(image=input_img)
trans_img = transform['image'].unsqueeze(0)
with torch.no_grad():
out = model(trans_img)
bboxes = [[] for _ in range(trans_img.shape[0])]
for i in range(3):
batch_size, A, S, _, _ = out[i].shape
anchor = scaled_anchors[i]
boxes_scale_i = utils.cells_to_bboxes(
out[i], anchor, S=S, is_preds=True
)
for idx, (box) in enumerate(boxes_scale_i):
bboxes[idx] += box
nms_boxes = utils.non_max_suppression(
bboxes[0], iou_threshold=iou, threshold=threshold, box_format="midpoint",
)
out_fig = plot_image(trans_img.squeeze().permute(1,2,0).detach().cpu(), nms_boxes)
cam = GradCAM(model, [model.layers[12].conv], use_cuda=False)
grayscale_cam = cam(trans_img, targets=[outCallBack()])[0, :]
cam_image = show_cam_on_image(input_img.astype(np.float32)/255, grayscale_cam, use_rgb=True,image_weight=transparency)
return out_fig, cam_image
title = "YOLO V3 trained on PASCAL VOC Dataset"
description = "Gradio interface to show yoloV3 object detection and gradcam on outputs."
examples = [[f'examples/{i}'] for i in os.listdir("examples")]
demo = gr.Interface(
inference,
inputs = [gr.Image(shape=(416, 416), label="Input Image"), gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(0, 1, value = 0.5, label="IOU Value"), gr.Slider(0, 1, value = 0.5, label="Threshold Value")],
outputs = [gr.Image(label="YoloV3 Output", shape = (416, 416)), gr.Image(label="GradCam Output", shape = (416, 416))],
title = title,
description = description,
examples = examples,
)
demo.launch() |