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import torch
import cv2
import numpy as np
import gradio as gr
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions, read_image
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
model.conf = 0.25
model.iou = 0.45
model.agnostic = False
model.multi_label = False
model.max_det = 1000
def detect(img):
results = model.predict(image, imgsz=image_size, return_outputs=True)
object_prediction_list = []
for _, image_results in enumerate(results):
if len(image_results)!=0:
image_predictions_in_xyxy_format = image_results['det']
for pred in image_predictions_in_xyxy_format:
x1, y1, x2, y2 = (
int(pred[0]),
int(pred[1]),
int(pred[2]),
int(pred[3]),
)
bbox = [x1, y1, x2, y2]
score = pred[4]
category_name = model.model.names[int(pred[5])]
category_id = pred[5]
object_prediction = ObjectPrediction(
bbox=bbox,
category_id=int(category_id),
score=score,
category_name=category_name,
)
object_prediction_list.append(object_prediction)
image = read_image(image)
output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
return output_image['image']
def drawRectangles(image, dfResults):
for index, row in dfResults.iterrows():
print( (row['xmin'], row['ymin']))
image = cv2.rectangle(image, (row['xmin'], row['ymin']), (row['xmax'], row['ymax']), (255, 0, 0), 2)
return image
img = gr.inputs.Image(shape=(192, 192))
intf = gr.Interface(fn=detect, inputs=img, outputs='image')
intf.launch(inline=False) |