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import gradio as gr
import torch
import os
import requests
import json
import cv2
from PIL import Image
from timeit import default_timer as timer
import pathlib
import platform
import numpy as np
model = torch.hub.load('ultralytics/yolov5','yolov5s', pretrained=True)
cnt = 0
def LCR(bbox,x_img, y_img):
x1 = bbox[0]/x_img
x2 = bbox[2]/x_img
if x1 < 0.2 and x2 < 0.2 :
location = "Left"
elif x1 > 0.8 and x2 > 0.8:
location = "Right"
elif x1 < 0.2 and (x2 <= 0.8 and x2 >= 0.2):
if (x1 + x2) < 0.4:
location = "Left"
else:
location = "Center"
elif x2 > 0.8 and (x1 <= 0.8 and x1 >= 0.2):
if (x1 + x2) > 1.6:
location = "Right"
else:
location = "Center"
else:
location = "Center"
print(f"x1 {x1} x2 {x2} bbox0 {bbox[0]} bbox2 {bbox[2]} x_img {x_img} LocationLCR {location}")
return location
def ACB(bbox, x_img, y_img, location):
y1 = bbox[1]/y_img
y2 = bbox[3]/y_img
if location == "Center":
if y1 < 0.33333 and y2 < 0.33333 :
location = "Above"
elif y1 > 0.66667 and y2 > 0.66667:
location = "Below"
elif y1 < 0.33333 and (y2 <= 0.66667 and y2 >= 0.33333):
if (y1 + y2) < 0.66667:
location = "Above"
else:
location = "Center"
elif y2 > 0.66667 and (y1 <= 0.66667 and y1 >= 0.33333):
if (y1 + y2) > 1.33333:
location = "Below"
else:
location = "Center"
else:
location = "Center"
else:
pass
print(f"y1 {y1} y2 {y2} bbox1 {bbox[1]} bbox3 {bbox[3]} y_img {y_img} Location{location}")
return location
#print(bbox[0])
def turn_img_into_fileJSON(frame):
start_time = timer()
x_img, y_img = frame.size
print(x_img,y_img)
global cnt
objects = []
prediction = model(frame)
for det in prediction.xyxy[0]:
class_id = int(det[5])
class_name = model.names[class_id]
confidence = float(det[4])
bbox = det[:4].tolist()
if(confidence >= 0.5):
location = LCR(bbox, x_img, y_img)
location = ACB(bbox, x_img, y_img, location)
# Save the results to the list
objects.append({
'Class': class_name,
#'BoundingBox': bbox,
'Location': location,
'Confidence': confidence
})
with open('{:05d}.json'.format(cnt) , 'w') as f:
json.dump(objects, f)
cnt += 1
pred_time = round(timer() - start_time, 5)
json_str = json.dumps(objects)
return json_str, pred_time
#path = [["D:/cuoc_thi/object-detection/download.jpg"],["C:/Users/ACER/Pictures/mydestiny/273536337_788402492117531_8798195010554693138_n.jpg"]]
title = "Object-detection"
description = "An EfficientNetB2 feature extractor computer vision model to classify images of object."
article = "Created by Ryan"
# json_str1, pred_time1 = turn_img_into_fileJSON("C:/Users/ACER/Pictures/mydestiny/273536337_788402492117531_8798195010554693138_n.jpg")
# print(json_str1, pred_time1)
# json_str, pred_time = turn_img_into_fileJSON("D:/cuoc_thi/object-detection/download.jpg")
# print(json_str, pred_time)
# Create the Gradio demo
demo = gr.Interface(fn=turn_img_into_fileJSON, # mapping function from input to output
inputs="pil", # what are the inputs?
outputs=[gr.JSON(label="JSON Output"),
#gr.Label(num_top_classes=80, label="Predictions"),
gr.Number(label="Prediction time (s)")],
#gr.outputs.Label(num_top_classes= 80),
#examples=path,
title=title,
description=description,
article=article,
live = True)
#demo.launch()
demo.launch(share=True) |