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# Import libraries
import cv2 # for reading images, draw bounding boxes
from ultralytics import YOLO
import gradio as gr
# Define constants
BOX_COLORS = {
"unchecked": (242, 48, 48),
"checked": (38, 115, 101),
"block": (242, 159, 5)
}
BOX_PADDING = 2
# Load models
DETECTION_MODEL = YOLO("detector-model.pt")
CLASSIFICATION_MODEL = YOLO("classifier-model.pt") # 0: block, 1: checked, 2: unchecked
def detect(image_path):
"""
Output inference image with bounding box
Args:
- image: to check for checkboxes
Return: image with bounding boxes drawn
"""
image = cv2.imread(image_path)
if image is None:
return image
# Predict on image
results = DETECTION_MODEL.predict(source=image, conf=0.5, iou=0.8) # Predict on image
boxes = results[0].boxes # Get bounding boxes
if len(boxes) == 0:
return image
# Get bounding boxes
for box in boxes:
detection_class_conf = round(box.conf.item(), 2)
detection_class = list(BOX_COLORS)[int(box.cls)]
# Get start and end points of the current box
start_box = (int(box.xyxy[0][0]), int(box.xyxy[0][1]))
end_box = (int(box.xyxy[0][2]), int(box.xyxy[0][3]))
box = image[start_box[1]:end_box[1], start_box[0]: end_box[0], :]
# Determine the class of the box using classification model
cls_results = CLASSIFICATION_MODEL.predict(source=box, conf=0.5)
probs = cls_results[0].probs # cls prob, (num_class, )
classification_class = list(BOX_COLORS)[2 - int(probs.top1)]
classification_class_conf = round(probs.top1conf.item(), 2)
cls = classification_class if classification_class_conf > 0.9 else detection_class
# 01. DRAW BOUNDING BOX OF OBJECT
line_thickness = round(0.002 * (image.shape[0] + image.shape[1]) / 2) + 1
image = cv2.rectangle(img=image,
pt1=start_box,
pt2=end_box,
color=BOX_COLORS[cls],
thickness = line_thickness) # Draw the box with predefined colors
# 02. DRAW LABEL
text = cls + " " + str(detection_class_conf)
# Get text dimensions to draw wrapping box
font_thickness = max(line_thickness - 1, 1)
(text_w, text_h), _ = cv2.getTextSize(text=text, fontFace=2, fontScale=line_thickness/3, thickness=font_thickness)
# Draw wrapping box for text
image = cv2.rectangle(img=image,
pt1=(start_box[0], start_box[1] - text_h - BOX_PADDING*2),
pt2=(start_box[0] + text_w + BOX_PADDING * 2, start_box[1]),
color=BOX_COLORS[cls],
thickness=-1)
# Put class name on image
start_text = (start_box[0] + BOX_PADDING, start_box[1] - BOX_PADDING)
image = cv2.putText(img=image, text=text, org=start_text, fontFace=0, color=(255,255,255), fontScale=line_thickness/3, thickness=font_thickness)
return image
iface = gr.Interface(fn=detect,
inputs=gr.inputs.Image(label="Upload scanned document", type="filepath"),
outputs="image")
iface.launch() |