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app.py

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+import gradio as gr
+import cv2
+import requests
+import os
+import cvzone
+import math
+import random
+import numpy as np
+
+from ultralytics import YOLO
+
+
+
+
+model = YOLO('yolov8x-seg.pt')
+path  = []
+video_path = []
+
+listClasses = ['person', 'bicycle', 'car']
+
+
+def show_preds_image(image_path):
+    image = cv2.imread(image_path)
+    outputs = model.predict(source=image_path)
+    results = outputs[0].cpu().numpy()
+
+    yolo_classes = list(model.names.values())
+    classes_ids = [yolo_classes.index(clas) for clas in yolo_classes]
+    colors = [random.choices(range(256), k=3) for _ in classes_ids]
+    
+    for result in outputs:
+        for mask, box in zip(result.masks.xy, result.boxes):
+            
+    #for r in results:
+        #boxes = r.boxes
+        #for box in boxes:
+            cls = box.cls[0]
+            conf = math.ceil((box.conf[0]*100))/100
+            if (int(cls)<3) and (conf > 0.70):
+                
+                points = np.int32([mask])
+                # cv2.polylines(img, points, True, (255, 0, 0), 1)
+                color_number = classes_ids.index(int(box.cls[0]))
+                color = colors[color_number]
+                            
+                cv2.fillPoly(image, points, color)
+                
+                x1, y1, x2, y2 = box.xyxy[0]
+                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+
+                cv2.rectangle(
+                    image,
+                    (int(x1), int(y1), int(x2), int(y2)),
+                    color=(0, 0, 255),
+                    thickness=2,
+                    lineType=cv2.LINE_AA
+                )
+
+
+                name = yolo_classes[int(cls)]
+                # fontScale
+                fontScale = 0.5
+
+                color_number = classes_ids.index(int(box.cls[0]))
+                color = colors[color_number]
+
+                # Line thickness of 2 px
+                thickness = 1
+                font = cv2.FONT_HERSHEY_SIMPLEX
+
+                cv2.putText(image, str(name) + " " + str(conf), (max(0,x1), max(35,y1)), font,
+                       fontScale, color, thickness, cv2.LINE_AA)
+
+
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+def show_preds_webcam(pil_image):
+    image= np.array(pil_image)
+    outputs = model.predict(image)
+    results = outputs[0].cpu().numpy()
+
+    yolo_classes = list(model.names.values())
+    classes_ids = [yolo_classes.index(clas) for clas in yolo_classes]
+    colors = [random.choices(range(256), k=3) for _ in classes_ids]
+    
+    for result in outputs:
+        for mask, box in zip(result.masks.xy, result.boxes):
+            
+    #for r in results:
+        #boxes = r.boxes
+        #for box in boxes:
+            cls = box.cls[0]
+            conf = math.ceil((box.conf[0]*100))/100
+            if (int(cls)<3) and (conf > 0.70):
+                
+                points = np.int32([mask])
+                # cv2.polylines(img, points, True, (255, 0, 0), 1)
+                color_number = classes_ids.index(int(box.cls[0]))
+                color = colors[color_number]
+                            
+                cv2.fillPoly(image, points, color)
+                
+                x1, y1, x2, y2 = box.xyxy[0]
+                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+
+                cv2.rectangle(
+                    image,
+                    (int(x1), int(y1), int(x2), int(y2)),
+                    color=(0, 0, 255),
+                    thickness=2,
+                    lineType=cv2.LINE_AA
+                )
+
+
+                name = yolo_classes[int(cls)]
+                # fontScale
+                fontScale = 0.5
+
+                color_number = classes_ids.index(int(box.cls[0]))
+                color = colors[color_number]
+
+                # Line thickness of 2 px
+                thickness = 1
+                font = cv2.FONT_HERSHEY_SIMPLEX
+
+                cv2.putText(image, str(name) + " " + str(conf), (max(0,x1), max(35,y1)), font,
+                       fontScale, color, thickness, cv2.LINE_AA)
+
+    
+    return image
+
+    
+
+inputs_image = [
+    gr.components.Image(type="filepath", label="Input Image"),
+]
+outputs_image = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+interface_image = gr.Interface(
+    fn=show_preds_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Object segmentation",
+    examples=path,
+    cache_examples=False,
+)
+
+
+    
+
+outputs_video = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+
+
+interface_webcam =  gr.Interface(
+    fn=show_preds_webcam,
+    live=True,
+    inputs=gr.Image(source="webcam", streaming=True, type="pil"),
+    outputs=outputs_video,
+)
+
+gr.TabbedInterface(
+    [  interface_webcam, interface_image],
+    tab_names=[  'Webcam', "Image"]
+).queue().launch()
+
+

requirements.txt

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+# Ultralytics requirements
+# Usage: pip install -r requirements.txt
+
+# Base ----------------------------------------
+hydra-core>=1.2.0
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.1
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.64.0
+ultralytics
+cvzone
+python-math
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# clearml
+# comet
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=6.0  # CoreML export
+# onnx>=1.12.0  # ONNX export
+# onnx-simplifier>=0.4.1  # ONNX simplifier
+# nvidia-pyindex  # TensorRT export
+# nvidia-tensorrt  # TensorRT export
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TF exports (-cpu, -aarch64, -macos)
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
+
+# Extras --------------------------------------
+ipython  # interactive notebook
+psutil  # system utilization
+thop>=0.1.1  # FLOPs computation
+# albumentations>=1.0.3
+# pycocotools>=2.0.6  # COCO mAP
+# roboflow
+
+# HUB -----------------------------------------
+GitPython>=3.1.24

yolov8x-seg.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d63cbfa5764867c0066bedfa43cf2dcd90a412a1de44b2e238c43978a9d28ea6
+size 144076467