|
import io |
|
import gradio as gr |
|
import matplotlib.pyplot as plt |
|
import requests, validators |
|
import torch |
|
import pathlib |
|
from PIL import Image |
|
from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection |
|
from ultralyticsplus import YOLO, render_result |
|
|
|
import os |
|
|
|
|
|
COLORS = [ |
|
[0.000, 0.447, 0.741], |
|
[0.850, 0.325, 0.098], |
|
[0.929, 0.694, 0.125], |
|
[0.494, 0.184, 0.556], |
|
[0.466, 0.674, 0.188], |
|
[0.301, 0.745, 0.933] |
|
] |
|
|
|
YOLOV8_LABELS = ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor'] |
|
|
|
def make_prediction(img, feature_extractor, model): |
|
inputs = feature_extractor(img, return_tensors="pt") |
|
outputs = model(**inputs) |
|
img_size = torch.tensor([tuple(reversed(img.size))]) |
|
processed_outputs = feature_extractor.post_process(outputs, img_size) |
|
return processed_outputs |
|
|
|
def fig2img(fig): |
|
buf = io.BytesIO() |
|
fig.savefig(buf) |
|
buf.seek(0) |
|
img = Image.open(buf) |
|
return img |
|
|
|
|
|
def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None): |
|
keep = output_dict["scores"] > threshold |
|
boxes = output_dict["boxes"][keep].tolist() |
|
scores = output_dict["scores"][keep].tolist() |
|
labels = output_dict["labels"][keep].tolist() |
|
if id2label is not None: |
|
labels = [id2label[x] for x in labels] |
|
|
|
|
|
|
|
plt.figure(figsize=(16, 10)) |
|
plt.imshow(pil_img) |
|
ax = plt.gca() |
|
colors = COLORS * 100 |
|
for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors): |
|
ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3)) |
|
ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5)) |
|
plt.axis("off") |
|
return fig2img(plt.gcf()) |
|
|
|
def detect_objects(model_name,url_input,image_input,threshold): |
|
|
|
|
|
if 'yolov8' in model_name: |
|
|
|
|
|
|
|
model = YOLO(model_name) |
|
|
|
model.overrides['conf'] = 0.15 |
|
model.overrides['iou'] = 0.05 |
|
model.overrides['agnostic_nms'] = False |
|
model.overrides['max_det'] = 1000 |
|
|
|
results = model.predict(image_input) |
|
|
|
render = render_result(model=model, image=image_input, result=results[0]) |
|
|
|
final_str = "" |
|
final_str_abv = "" |
|
final_str_else = "" |
|
|
|
for result in results: |
|
boxes = result.boxes.cpu().numpy() |
|
for i, box in enumerate(boxes): |
|
|
|
coordinates = box.xyxy[0].astype(int) |
|
try: |
|
label = YOLOV8_LABELS[int(box.cls)] |
|
except: |
|
label = "ERROR" |
|
try: |
|
confi = float(box.conf) |
|
except: |
|
confi = 0.0 |
|
|
|
if confi >= threshold: |
|
final_str_abv += f"Detected `{label}` with confidence `{confi}` at location `{coordinates}`\n" |
|
else: |
|
final_str_else += f"Detected `{label}` with confidence `{confi}` at location `{coordinates}`\n" |
|
|
|
final_str = "{:*^50}\n".format("ABOVE THRESHOLD OR EQUAL") + final_str_abv + "\n{:*^50}\n".format("BELOW THRESHOLD")+final_str_else |
|
|
|
return render, final_str |
|
else: |
|
|
|
|
|
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) |
|
if 'detr' in model_name: |
|
|
|
model = DetrForObjectDetection.from_pretrained(model_name) |
|
|
|
elif 'yolos' in model_name: |
|
|
|
model = YolosForObjectDetection.from_pretrained(model_name) |
|
|
|
tb_label = "" |
|
if validators.url(url_input): |
|
image = Image.open(requests.get(url_input, stream=True).raw) |
|
tb_label = "Confidence Values URL" |
|
|
|
elif image_input: |
|
image = image_input |
|
tb_label = "Confidence Values Upload" |
|
|
|
|
|
processed_output_list = make_prediction(image, feature_extractor, model) |
|
print("After make_prediction" + str(processed_output_list)) |
|
processed_outputs = processed_output_list[0] |
|
|
|
|
|
viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label) |
|
|
|
|
|
|
|
|
|
final_str_abv = "" |
|
final_str_else = "" |
|
for score, label, box in sorted(zip(processed_outputs["scores"], processed_outputs["labels"], processed_outputs["boxes"]), key = lambda x: x[0].item(), reverse=True): |
|
box = [round(i, 2) for i in box.tolist()] |
|
if score.item() >= threshold: |
|
final_str_abv += f"Detected `{model.config.id2label[label.item()]}` with confidence `{round(score.item(), 3)}` at location `{box}`\n" |
|
else: |
|
final_str_else += f"Detected `{model.config.id2label[label.item()]}` with confidence `{round(score.item(), 3)}` at location `{box}`\n" |
|
|
|
|
|
final_str = "{:*^50}\n".format("ABOVE THRESHOLD OR EQUAL") + final_str_abv + "\n{:*^50}\n".format("BELOW THRESHOLD")+final_str_else |
|
|
|
return viz_img, final_str |
|
|
|
def set_example_image(example: list) -> dict: |
|
return gr.Image.update(value=example[0]) |
|
|
|
def set_example_url(example: list) -> dict: |
|
return gr.Textbox.update(value=example[0]) |
|
|
|
|
|
title = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>""" |
|
|
|
description = """ |
|
Links to HuggingFace Models: |
|
|
|
- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) |
|
- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) |
|
- [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) |
|
- [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) |
|
- [facebook/detr-resnet-101-dc5](https://huggingface.co/facebook/detr-resnet-101-dc5) |
|
- [hustvl/yolos-small-300](https://huggingface.co/hustvl/yolos-small-300) |
|
- [mshamrai/yolov8x-visdrone](https://huggingface.co/mshamrai/yolov8x-visdrone) |
|
|
|
""" |
|
|
|
models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small','hustvl/yolos-tiny','facebook/detr-resnet-101-dc5', 'hustvl/yolos-small-300', 'mshamrai/yolov8x-visdrone'] |
|
urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"] |
|
|
|
|
|
|
|
|
|
|
|
css = ''' |
|
h1#title { |
|
text-align: center; |
|
} |
|
''' |
|
demo = gr.Blocks(css=css) |
|
|
|
|
|
def changing(b1, b2, inVal, outBox): |
|
if inVal: |
|
b1.interactive = "True" |
|
b2.interactive = "True" |
|
else: |
|
outBox.value = "Select Dropdown" |
|
|
|
|
|
|
|
with demo: |
|
gr.Markdown(title) |
|
gr.Markdown(description) |
|
|
|
options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True) |
|
|
|
slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold') |
|
|
|
|
|
|
|
with gr.Tabs(): |
|
with gr.TabItem('Image URL'): |
|
with gr.Row(): |
|
url_input = gr.Textbox(lines=2,label='Enter valid image URL here..') |
|
img_output_from_url = gr.Image(shape=(650,650)) |
|
|
|
with gr.Row(): |
|
example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls]) |
|
|
|
url_but = gr.Button('Detect', interactive="False") |
|
|
|
with gr.TabItem('Image Upload'): |
|
with gr.Row(): |
|
img_input = gr.Image(type='pil') |
|
img_output_from_upload= gr.Image(shape=(650,650)) |
|
|
|
with gr.Row(): |
|
example_images = gr.Dataset(components=[img_input], |
|
samples=[[path.as_posix()] |
|
for path in sorted(pathlib.Path('images').rglob('*.JPG'))]) |
|
|
|
img_but = gr.Button('Detect', interactive="False") |
|
|
|
|
|
|
|
output_text1 = gr.components.Textbox(label="Confidence Values") |
|
|
|
|
|
options.change(fn=changing, inputs=[img_but, url_but, options, output_text1]) |
|
|
|
|
|
url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, output_text1],queue=True) |
|
img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, output_text1],queue=True) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input]) |
|
example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input]) |
|
|
|
|
|
|
|
|
|
|
|
|
|
demo.launch() |