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# Import library
import cv2
import glob
import numpy as np
from PIL import Image
import streamlit as st
from src.detection_keypoint import DetectKeypoint
from src.classification_keypoint import KeypointClassification
detection_keypoint = DetectKeypoint()
classification_keypoint = KeypointClassification(
'./models/pose_classification.pth'
)
def pose_classification(img, col=None):
image = Image.open(img)
image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
image_rgb = cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB)
# show image col 1
col1.write("Original Image :")
col1.image(image_rgb)
# detection keypoint
results = detection_keypoint(image_cv)
results_keypoint = detection_keypoint.get_xy_keypoint(results)
# classification keypoint
input_classification = results_keypoint[10:]
results_classification = classification_keypoint(input_classification)
# visualize result
image_draw = results.plot(boxes=False)
x_min, y_min, x_max, y_max = results.boxes.xyxy[0].numpy()
image_draw = cv2.rectangle(
image_draw,
(int(x_min), int(y_min)),(int(x_max), int(y_max)),
(0,0,255), 2
)
(w, h), _ = cv2.getTextSize(
results_classification.upper(),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2
)
image_draw = cv2.rectangle(
image_draw,
(int(x_min), int(y_min)-20),(int(x_min)+w, int(y_min)),
(0,0,255), -1
)
image_draw = cv2.putText(image_draw,
f'{results_classification.upper()}',
(int(x_min), int(y_min)-4),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (255, 255, 255),
thickness=2
)
image_draw = cv2.cvtColor(image_draw, cv2.COLOR_BGR2RGB)
col2.write("Keypoint Result :wrench:")
col2.image(image_draw)
col2.text(f'Pose Classification : {results_classification}')
return image_draw, results_classification
st.set_page_config(
layout="wide",
page_title="YoloV8 Keypoint Classification"
)
st.write(
"## YoloV8 Keypoint Yoga Pose Classification"
)
st.write(
":dog: Try uploading an image to Classification Yoga Basic Pose like a Downdog, Goddess, Plank, Tree, Warrior2 :grin:"
)
st.sidebar.write(
"## Upload Image :gear:"
)
col1, col2 = st.columns(2)
img_upload = st.sidebar.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
if img_upload is not None:
pose_classification(img=img_upload)
# show sample image
st.write('## Sample Image')
images = glob.glob('./images/*.jpeg')
row_size = len(images)
grid = st.columns(row_size)
col = 0
for image in images:
with grid[col]:
st.image(f'{image}')
st.button(label='RUN', key=f'run_{image}',
on_click=pose_classification, args=(image, 'run'))
col = (col + 1) % row_size
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