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import cv2
import numpy as np
import onnxruntime as rt
import gradio as gr
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
def inference(image_data):
detections = sess.run(output_names, {input_name: image_data})
return detections
def draw_detections(img, box, score, class_id):
# Extract the coordinates of the bounding box
x1, y1, x2, y2 = box
# Retrieve the color for the class ID
color = color_palette_pred[class_id]
# Draw the bounding box on the image
cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), color, 3)
# Create the label text with class name and score
label = f'{classes[class_id]}: {score:.2f}'
# Calculate the dimensions of the label text
(label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
# Calculate the position of the label text
label_x = x1
label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10
# Draw a filled rectangle as the background for the label text
label_x = round(label_x)
label_y = round(label_y)
cv2.rectangle(img, (label_x, label_y - label_height), (label_x + label_width, label_y + label_height), color, cv2.FILLED)
# Draw the label text on the image
cv2.putText(img, label, (label_x, label_y), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 0), 1, cv2.LINE_AA)
def postprocess(detections, img_width, img_height, input_size, original_image):
boxes = detections[1][0]
scores = detections[2][0]
class_ids = detections[3][0]
# width_scale = img_width / input_size
# height_scale = img_height / input_size
ih, iw = [input_size, input_size] # [input_size, input_size] = [640, 640]
h, w = img_height, img_width # [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]
dw, dh = (iw - nw) // 2, (ih-nh) // 2 # [0, 80]
# dh, dw = 0, 0
# scale = 0.3292
new_boxes = []
for box, score, class_id in zip(boxes, scores, class_ids):
x1, y1, x2, y2 = box
x1 = (x1 - dw) / scale
y1 = (y1 - dh) / scale
x2 = (x2 - dw) / scale
y2 = (y2 - dh) / scale
box = [x1, y1, x2, y2]
draw_detections(original_image, box, score, class_id)
new_boxes.append(box)
return [class_ids, scores, new_boxes]
# fix all the variables
with open("coco_names.txt", "r") as f:
content = f.readlines()
content = "".join(content)
classes = content.split("\n")
color_palette_pred = np.random.uniform(0, 255, size=(len(classes), 3))
sess = rt.InferenceSession("yolov8n_coco_640.nms.onnx")
outputs = sess.get_outputs()
output_names = list(map(lambda output: output.name, outputs))
input_name = sess.get_inputs()[0].name
input_size = sess.get_inputs()[0].shape[2]
def run(img_path):
image_data, img_width, img_height, original_image = image_preprocess(img_path)
detections = inference(image_data)
l = postprocess(detections, img_width, img_height, input_size, original_image)
return original_image
demo = gr.Interface(
fn=run,
inputs=["image"],
outputs=["image"],
)
demo.launch()
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