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a176aa6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import gradio as gr
import cv2
import numpy as np
from ultralytics import YOLO
import Backend.Fruit_Freshness.Banana.run as brn
import Backend.Fruit_Freshness.Apples.run as arn
# Define the freshness function
def freshness(img):
# Convert Gradio image (PIL.Image) to OpenCV format
img = np.array(img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# Load the model
model = YOLO("Weights/freshness.pt", verbose=False)
# Run inference
results = model(img)
# Class names mapping
class_names = {
0: 'fresh apple', 1: 'fresh banana', 2: 'fresh bell pepper', 3: 'fresh carrot', 4: 'fresh cucumber',
5: 'fresh mango', 6: 'fresh orange', 7: 'fresh potato', 8: 'fresh strawberry', 9: 'fresh tomato',
10: 'rotten apple', 11: 'rotten banana', 12: 'rotten bell pepper', 13: 'rotten carrot', 14: 'rotten cucumber',
15: 'rotten mango', 16: 'rotten orange', 17: 'rotten potato', 18: 'rotten strawberry', 19: 'rotten tomato'
}
output_text = ""
for i in range(len(results)):
# Get detected class and bounding box
detected_class = int(results[i].boxes.cls[0])
class_name = class_names[detected_class]
coordinates = results[i].boxes.xyxy
# Process each bounding box
for j, box in enumerate(coordinates):
x1, y1, x2, y2 = map(int, box[:4])
crop_img = img[y1:y2, x1:x2]
if detected_class in {1, 11}: # Banana classes
score = 0
result = brn.run(crop_img, score)
output_text += f"Detected: {class_name}, Banana Freshness: {result}\n"
elif detected_class in {0, 10}: # Apple classes
score = 10
fresh_flag = False
stale_flag = False
result = arn.run(crop_img, stale_flag, fresh_flag, score)
output_text += f"Detected: {class_name}, Apple Freshness: {result}\n"
return output_text
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