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1d7b87c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | import gradio as gr
import cv2
from PIL import Image
import timm
from ultralytics import YOLO
import numpy as np
import torch
from PIL import Image as PILImage
from timm.utils.attention_extract import AttentionExtract
from glob import glob
import os
import argparse
from gradio_utils import classes, cropSquareCV, nms, classifier_transforms, get_sns_heatmap
from gradio_utils import make_thumbnail_gallery, annotate_bbox_on_image
from models import MCAClassifier
device = "cuda" if torch.cuda.is_available() else "cpu"
dirname = os.path.dirname(__file__)
detector = YOLO(os.path.join(dirname, 'models/MDV6-yolov10-e-1280.pt'))
classifier = timm.create_model("vit_large_patch14_dinov2.lvd142m", pretrained=False, num_classes=46, dynamic_img_size=True)
classifier.load_state_dict(torch.load(os.path.join(dirname, 'models/crop_classifier_half.pt'), map_location=device, weights_only=True))
classifier.to(device)
seq_model = MCAClassifier(depth=4)
seq_model.load_state_dict(torch.load(os.path.join(dirname, 'models/mca_classifier.pt'), map_location=device, weights_only=True))
seq_model.to(device)
extractor = AttentionExtract(seq_model, method='fx') # for the attention matrix
def show_preds_image(image1, image2=None):
image_paths = [image1, image2]
predictions = []
embeddings_seq = []
gallery_images = []
table_data = []
img_list = []
crop_size = (200, 200)
# detection and independent classifier + save embeddings for mca classsifer
for image_path in image_paths:
if image_path is None:
continue
image = cv2.imread(image_path)
outputs = detector.predict(source=image_path, imgsz=1280, verbose=True, conf=0.25, device=device)
detections = nms(outputs[0].cpu().numpy().boxes, 0.5)
for i, (box_cls, conf, box) in enumerate(zip(detections.cls, detections.conf, detections.xyxy)):
if conf < 0.25:
continue
x1, y1, x2, y2 = map(int, box)
croppedimagecv = cropSquareCV(image, box.copy())[:, :, (2, 1, 0)]
croppedimage = Image.fromarray(croppedimagecv) # for classifier
im = cv2.resize(croppedimagecv, crop_size, interpolation=cv2.INTER_CUBIC) # for visualization
gallery_images.append(PILImage.fromarray(im)) # for gallery
img_list.append(im) # for attention matrix
if box_cls == 0:
x = classifier_transforms(croppedimage).unsqueeze(dim=0).to(device)
with torch.no_grad():
embeddings = classifier.forward_features(x)
embeddings_seq.append(embeddings[:, 0:1, :])
pred = classifier.forward_head(embeddings).softmax(dim=1).cpu()
scores = {classes[j]: float(pred[0][j]) for j in range(len(classes))}
top_class = max(scores, key=scores.get)
label_text = f"{top_class}: {scores[top_class]*100:.0f}%"
else:
if box_cls == 1:
label_text = "human"
elif box_cls == 2:
label_text = "vehicle"
predictions.append([(x1, y1, x2, y2), label_text, image_path])
# sequence-aware classifier
if len(embeddings_seq) > 0:
x = torch.concat(embeddings_seq, 1)
with torch.no_grad():
embeddings = seq_model.forward_blocks(x)
pred = classifier.forward_head(embeddings.swapaxes(0, 1)).cpu().softmax(dim=1)
oo = extractor(x)
am = oo[f'blocks.{len(oo)-1}.attn.softmax'].cpu().numpy()[0]
print(am.shape)
else:
pred = []
am = None
n = 0
for i, ((x1, y1, x2, y2), orig_label, image_path) in enumerate(predictions):
if orig_label in ["human", "vehicle"]:
table_data.append([i, orig_label, orig_label, image_path])
else:
top_class = max({classes[j]: float(pred[n][j]) for j in range(len(classes))}, key=lambda k: pred[n][classes.index(k)])
top_score = float(pred[n][classes.index(top_class)])*100
seq_label = f"{top_class}: {top_score:.0f}%"
table_data.append([i, orig_label, seq_label, image_path])
n += 1
# permutate bbox order to make the attention matrix easier to read
sp = [table_data[i][2].split(":")[0] for i in range(len(table_data))]
perm = sorted(range(len(sp)), key=lambda k: sp[k])
gallery_images = [gallery_images[i] for i in perm]
img_list = [img_list[i] for i in perm]
table_data = [table_data[i] for i in perm]
if am is not None:
not_animal = [x in ["human", "vehicle"] for x in [sp[i] for i in perm]]
not_animal_idx = [perm[i] for i in range(len(perm)) if not_animal[i]]
am_perm = []
for i in range(len(perm)):
if not_animal[i]:
continue
else:
am_perm.append(perm[i] - sum([perm[i] > k for k in not_animal_idx]))
am = am[:, am_perm, :]
am = am[:, :, am_perm]
img_list = [img_list[i] for i in range(len(img_list)) if not not_animal[i]]
# annotate bounding boxes on images
annotated_image1 = cv2.imread(image1) if image1 else None
annotated_image2 = cv2.imread(image2) if image2 else None
for i, ((x1, y1, x2, y2), orig_label, image_path) in enumerate(predictions):
if image_path == image1:
annotate_bbox_on_image(annotated_image1, x1, y1, x2, y2, str(perm.index(i)+1))
else:
annotate_bbox_on_image(annotated_image2, x1, y1, x2, y2, str(perm.index(i)+1))
gallery_images = make_thumbnail_gallery(gallery_images, table_data)
# make attention matrix
img = np.concatenate(img_list, axis=1)
X, Y, _ = img.shape
am_img = np.zeros((X+Y, X+Y, 3), dtype=np.uint8)
if am is not None:
am_img[:X, X:X+Y] = img
am_img[X:X+Y, :X] = np.concatenate(img_list, axis=0)
am_img[X:, X:] = cv2.resize(get_sns_heatmap(am), (Y, Y), interpolation=cv2.INTER_NEAREST)
output_image1 = cv2.cvtColor(annotated_image1, cv2.COLOR_BGR2RGB) if image1 else None
output_image2 = cv2.cvtColor(annotated_image2, cv2.COLOR_BGR2RGB) if image2 else None
return output_image1, output_image2, gallery_images, am_img
with gr.Blocks(css=".block {margin: auto}") as demo:
args = dict(show_download_button=False, show_fullscreen_button=False) if False else dict()
with gr.Tab("One image"):
with gr.Row():
with gr.Column(scale=1):
input_image = gr.Image(type="filepath", label="Image")
path = [x for x in glob(os.path.join(dirname, 'images/gradio_examples/one_image/*')) if x.lower().split(".")[-1] in ["jpg", "jpeg", "png"]]
examples = gr.Examples(path, input_image)
btn = gr.Button("Run", variant="primary")
with gr.Column(scale=3):
with gr.Group():
with gr.Row():
output_image = gr.Image(type="numpy", label="Annotated Image", scale=16, show_label=False, **args)
output_image2 = gr.Image(type="numpy", label="Annotated Image 2", scale=0, render=False, show_label=False, **args)
am_image = gr.Image(type="numpy", label="Attention Map", scale=9, show_label=False, **args)
with gr.Row():
output_gallery = gr.Gallery(label="Detected Crops", columns=9, rows=2, height="auto", object_fit='scale-down', show_label=False)
btn.click(
fn=show_preds_image,
inputs=[input_image],
outputs=[output_image, output_image2, output_gallery, am_image]
)
with gr.Tab("Two images"):
with gr.Row():
with gr.Column(scale=1):
input_image = gr.Image(type="filepath", label="Image")
path2 = [x for x in glob(os.path.join(dirname, 'images/gradio_examples/two_images/*')) if x.lower().split(".")[-1] in ["jpg", "jpeg", "png"]]
path2.sort()
examples = gr.Examples(path2, input_image)
input_image2 = gr.Image(type="filepath", label="Second Image (optional)")
examples2 = gr.Examples(path2, input_image2)
btn = gr.Button("Run", variant="primary")
with gr.Column(scale=3):
with gr.Group():
with gr.Row():
output_image = gr.Image(type="numpy", label="Annotated Image", scale=4, show_label=False, **args)
output_image2 = gr.Image(type="numpy", label="Annotated Image 2", scale=4, show_label=False, **args)
am_image = gr.Image(type="numpy", label="Attention Map", scale=3, show_label=False, **args)
with gr.Row():
output_gallery = gr.Gallery(label="Detected Crops", columns=9, rows=2, height="auto", object_fit='scale-down', show_label=False)
btn.click(
fn=show_preds_image,
inputs=[input_image, input_image2],
outputs=[output_image, output_image2, output_gallery, am_image]
)
demo.launch(allowed_paths=[os.path.join(dirname, '../images/gradio_examples/')])
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