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import gradio as gr
import cv2
import requests
import os
import random
from ultralytics import YOLO
file_urls = [
'https://www.dropbox.com/scl/fi/34yt1vrl4mc4n9ujdf9gm/all_76.jpg?rlkey=f7b6nq478r2m9yahcalzjzif5&dl=1',
'https://www.dropbox.com/scl/fi/lns6cewinp7rgf3v2g1n8/all_5.jpg?rlkey=20zvut81b829k9lg5yk8ve99z&dl=1',
'https://www.dropbox.com/scl/fi/13jr2f1znuzulmsyabl2f/long3.jpg?rlkey=jeyriw5a8c0t42e7y2986y53m&dl=1',
'https://www.dropbox.com/scl/fi/nglwcza7msjo1vu4kw27r/pot4.jpg?rlkey=1ynm35b4j100ta0p5g3fx7hx4&dl=1',
'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
]
# def download_file(url, save_name):
# url = url
# if not os.path.exists(save_name):
# file = requests.get(url)
# open(save_name, 'wb').write(file.content)
# for i, url in enumerate(file_urls):
# if 'mp4' in file_urls[i]:
# download_file(
# file_urls[i],
# f"video.mp4"
# )
# else:
# download_file(
# file_urls[i],
# f"image_{i}.jpg"
# )
model = YOLO('best.pt')
# path = [['image_0.jpg'], ['image_1.jpg'], ['image_2.jpg'], ['image_3.jpg']]
path = [['IMG_7612.JPG'], ['IMG_7678.JPG'], ['all_33.jpg'], ['all_80.jpg'],
['DSC02813.JPG'], ['DSC02373.JPG']]
# path = [['sc_1_0 (1) (1).JPG'], ['sc_1_0 (16) (1).JPG'],
# ['sc_1_0 (18) (1).JPG'], ['sc_1_0 (18).JPG']]
video_path = [['VID-20230809-WA0021.mp4'], ['VID-20230809-WA0022.mp4'],
['VID-20230809-WA0024.mp4'], ['VID-20230809-WA0032.mp4']]
classes = ['alligator_cracking', 'longitudinal_cracking', 'potholes', 'ravelling']
def show_preds_image(image_path):
image = cv2.imread(image_path)
outputs = model.predict(source=image_path, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=1024)
results = outputs[0].cpu().numpy()
re_boxes = results.boxes.data.tolist()
class_colors = {1 : (95, 255, 54), 2: (242, 210, 100), 3: (96, 7, 70), 4:(221, 59, 41)}
random.seed(42)
# class_colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for _ in range(4)]
for i, det in enumerate(results.boxes.xyxy):
x1, y1, x2, y2 = int(det[0]), int(det[1]), int(det[2]), int(det[3])
class_label = int(re_boxes[i][-1])
rectangle_color = class_colors.get(class_label)
# rectangle_color = class_colors[class_label]
text_color = rectangle_color
cv2.rectangle(
image,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=rectangle_color,
thickness=3,
lineType=cv2.LINE_AA
)
text_position = (x1, y1+100)
conf = re_boxes[i][-2]
class_name = classes[class_label]
# class_label = class_name.split('_')[0] + '\n' + class_name.split('_')[1] if '_' in class_name else class_name
cv2.putText(image, classes[class_label] + f' = {round(conf, 2)}',
text_position, cv2.FONT_HERSHEY_SIMPLEX, 1.5, text_color, 3)
# print(class_ids)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
inputs_image = [
gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="Asphalt Road Pavement Distresses Detector",
examples=path,
cache_examples=False,
description= 'This is a demo app that takes in images or videos of Asphalt pavement surfaces and \
\n detects the following pavement distresses: \
\n \
\n Alligator cracking \
\n Longitudinal cracking \
\n Potholes \
\n Ravelling \
\n \
\n This is specifically for Inference and educational purpose.\
\n \
\n The model might ocassionaly give false outputs'
)
def show_preds_video(video_path):
cap = cv2.VideoCapture(video_path)
while(cap.isOpened()):
ret, frame = cap.read()
if ret:
frame_copy = frame.copy()
outputs = model.predict(source=frame, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=1024)
results = outputs[0].cpu().numpy()
re_boxes = results.boxes.data.tolist()
class_colors = {1 : (95, 255, 54), 2: (242, 210, 100), 3: (96, 7, 70), 4:(221, 59, 41)}
random.seed(42)
# class_colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for _ in range(4)]
for i, det in enumerate(results.boxes.xyxy):
x1, y1, x2, y2 = int(det[0]), int(det[1]), int(det[2]), int(det[3])
class_label = int(re_boxes[i][-1])
rectangle_color = class_colors.get(class_label)
# rectangle_color = class_colors[class_label]
text_color = rectangle_color
cv2.rectangle(
frame_copy,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=rectangle_color,
thickness=2,
lineType=cv2.LINE_AA
)
text_position = (x1, y1+100)
conf = re_boxes[i][-2]
class_name = classes[class_label]
# class_label = class_name.split('_')[0] + '\n' + class_name.split('_')[1] if '_' in class_name else class_name
cv2.putText(frame_copy, classes[class_label] + f' = {round(conf, 2)}',
text_position, cv2.FONT_HERSHEY_SIMPLEX, 1.5, text_color, 3)
yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
inputs_video = [
gr.components.Video(type="filepath", label="Input Video"),
]
outputs_video = [
gr.components.Image(type="numpy", label="Output Video"),
]
interface_video = gr.Interface(
fn=show_preds_video,
inputs=inputs_video,
outputs=outputs_video,
title="Asphalt Road Pavement Distresses Detector",
examples=video_path,
cache_examples=False,
# live=True
)
gr.TabbedInterface(
[interface_image, interface_video],
tab_names=['Image inference', 'Video inference'],
).queue().launch()
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