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import gradio as gr
import matplotlib.pyplot as plt
from PIL import Image
from ultralytics import YOLO
import cv2
import numpy as np
def image_preprocess(image):
img_height, img_width = image.shape[0:2]
image_converted = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
ih, iw = [input_size, input_size] # [input_size, input_size] = [640, 640]
h, w, _ = image.shape # [1944, 2592]
scale = min(iw/w, ih/h) # min(0.2469, 0.3292) = 0.2469
nw, nh = int(scale * w), int(scale * h) # [640, 480]
image_resized = cv2.resize(image_converted, (nw, nh))
image_padded = np.full(shape=[ih, iw, 3], fill_value=128.0)
dw, dh = (iw - nw) // 2, (ih-nh) // 2 # [0, 80]
image_padded[dh:nh+dh, dw:nw+dw, :] = image_resized # image_padded[80:256, 32:224]
image_padded = image_padded / 255.
# image_resized = image_resized / 255.
image_padded = image_padded[np.newaxis, ...].astype(np.float32)
image_padded = np.moveaxis(image_padded, -1, 1)
return image_padded, img_width, img_height, image
model = YOLO('best (1).pt')
def response(image):
print(image)
results = model(image)
for i, r in enumerate(results):
# Plot results image
im_bgr = r.plot() # BGR-order numpy array
im_rgb = im_bgr[..., ::-1] # Convert BGR to RGB
# im_rgb = Image.fromarray(im_rgb)
return im_rgb
iface = gr.Interface(fn=response, inputs="image", outputs="image")
iface.launch() |