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import gradio as gr
from roboflow import Roboflow
import os
import tempfile
from PIL import Image, ImageDraw, ImageFont
import cv2
import numpy as np
# Initialize Roboflow
rf = Roboflow(api_key="E5qhgf3ZimDoTx5OfgZ8")
project = rf.workspace().project("newhassae")
def get_model(version):
return project.version(version).model
def preprocess_image(img, version):
# Initial crop for all images
img = img.crop((682, 345, 682+2703, 345+1403))
# Model specific processing
if version == 1:
return img.resize((640, 640))
elif version == 2:
return img
elif version == 3:
width, height = img.size
left = (width - 640) // 2
top = (height - 640) // 2
right = left + 640
bottom = top + 640
return img.crop((left, top, right, bottom))
return img
def process_images(image_files, version):
model = get_model(version)
results = []
if not isinstance(image_files, list):
image_files = [image_files]
for image_file in image_files:
try:
with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_file:
temp_file.write(image_file)
temp_path = temp_file.name
img = Image.open(temp_path)
processed_img = preprocess_image(img, version)
processed_temp = tempfile.NamedTemporaryFile(suffix='.jpg', delete=False)
processed_img.save(processed_temp.name)
try:
prediction = model.predict(processed_temp.name).json()
predicted_class = prediction["predictions"][0]["predictions"][0]["class"]
confidence = f"{float(prediction['predictions'][0]['predictions'][0]['confidence']) * 100:.1f}%"
except Exception as e:
predicted_class = "Error"
confidence = "N/A"
if processed_img.mode != 'RGB':
processed_img = processed_img.convert('RGB')
labeled_img = add_label_to_image(processed_img, predicted_class, confidence)
top_result = {
"predicted_class": predicted_class,
"confidence": confidence
}
results.append((labeled_img, top_result))
except Exception as e:
gr.Warning(f"Error processing image: {str(e)}")
continue
finally:
if 'temp_path' in locals():
os.unlink(temp_path)
if 'processed_temp' in locals():
os.unlink(processed_temp.name)
return results if results else [(Image.new('RGB', (400, 400), 'grey'), {"predicted_class": "Error", "confidence": "N/A"})]
def add_label_to_image(image, prediction, confidence):
# Convert PIL image to OpenCV format
img_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
# Image dimensions
img_height, img_width = img_cv.shape[:2]
padding = int(img_width * 0.02)
# Rectangle dimensions
rect_height = int(img_height * 0.15)
rect_width = img_width - (padding * 2)
# Draw red rectangle
cv2.rectangle(img_cv,
(padding, padding),
(padding + rect_width, padding + rect_height),
(0, 0, 255),
-1)
text = f"{prediction}: {confidence}"
# Text settings
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 3.0
thickness = 8
# Get text size and position
(text_width, text_height), _ = cv2.getTextSize(text, font, font_scale, thickness)
text_x = padding + (rect_width - text_width) // 2
text_y = padding + (rect_height + text_height) // 2
# Draw white text
cv2.putText(img_cv, text, (text_x, text_y), font, font_scale, (255, 255, 255), thickness)
# Convert back to PIL
img_pil = Image.fromarray(cv2.cvtColor(img_cv, cv2.COLOR_BGR2RGB))
return img_pil
def display_results(image_files, version):
results = process_images(image_files, version)
output_images = [res[0] for res in results]
predictions = [res[1] for res in results]
return output_images, predictions
# Create Gradio interface
with gr.Blocks() as demo:
gr.HTML("""
<div style="text-align: center; margin-bottom: 1rem">
<img src="https://haeab.se/wp-content/uploads/2023/12/ad.png" alt="Logo" style="height: 100px;">
</div>
""")
gr.Markdown("Hans Andersson Entrepenad")
with gr.Row():
with gr.Column():
model_version = gr.Slider(
minimum=1,
maximum=4,
step=1,
value=1,
label="Model Version",
interactive=True
)
image_input = gr.File(
label="Upload Image(s)",
file_count="multiple",
type="binary"
)
with gr.Column():
image_output = gr.Gallery(label="Processed Images")
text_output = gr.JSON(
label="Top Predictions",
height=400, # Increases height
container=True, # Adds a container around the JSON
show_label=True
)
submit_btn = gr.Button("Analyze Images")
submit_btn.click(
fn=display_results,
inputs=[image_input, model_version],
outputs=[image_output, text_output]
)
demo.launch(share=True, debug=True, show_error=True)
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