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import gradio as gr |
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import cv2 |
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import requests |
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import os |
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import cvzone |
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import math |
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import random |
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import numpy as np |
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from ultralytics import YOLO |
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model = YOLO('yolov8x-seg.pt') |
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path = [] |
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video_path = [] |
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listClasses = ['person', 'bicycle', 'car'] |
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def show_preds_image(image_path): |
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image = cv2.imread(image_path) |
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outputs = model.predict(source=image_path) |
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results = outputs[0].cpu().numpy() |
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yolo_classes = list(model.names.values()) |
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classes_ids = [yolo_classes.index(clas) for clas in yolo_classes] |
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colors = [random.choices(range(256), k=3) for _ in classes_ids] |
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for result in outputs: |
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for mask, box in zip(result.masks.xy, result.boxes): |
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cls = box.cls[0] |
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conf = math.ceil((box.conf[0]*100))/100 |
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if (int(cls)<3) and (conf > 0.70): |
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points = np.int32([mask]) |
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cv2.polylines(img, points, True, (255, 0, 0), 1) |
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color_number = classes_ids.index(int(box.cls[0])) |
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color = colors[color_number] |
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cv2.fillPoly(image, points, color) |
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x1, y1, x2, y2 = box.xyxy[0] |
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x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) |
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name = yolo_classes[int(cls)] |
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fontScale = 0.5 |
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color_number = classes_ids.index(int(box.cls[0])) |
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color = colors[color_number] |
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thickness = 1 |
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font = cv2.FONT_HERSHEY_SIMPLEX |
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cv2.putText(image, str(name) + " " + str(conf), (max(0,x1), max(35,y1)), font, |
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fontScale, color, thickness, cv2.LINE_AA) |
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return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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inputs_image = [ |
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gr.components.Image(type="filepath", label="Input Image"), |
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] |
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outputs_image = [ |
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gr.components.Image(type="numpy", label="Output Image"), |
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] |
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interface_image = gr.Interface( |
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fn=show_preds_image, |
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inputs=inputs_image, |
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outputs=outputs_image, |
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title="Object segmentation", |
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examples=path, |
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cache_examples=False, |
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).launch() |
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