Upload 2 files

app.py

@@ -0,0 +1,92 @@
+import gradio as gr
+import cv2
+import requests
+import os
+
+from ultralytics import YOLO
+
+
+
+model0 = YOLO('yolov3-tiny.pt')
+model1 = YOLO('yolov3-org.pt')
+model2 = YOLO('yolov3-spp.pt')
+model3 = YOLO('yolov5-s.pt')
+model4 = YOLO('yolov5-n.pt')
+model5 = YOLO('yolov5-m.pt')
+model6 = YOLO('yolov5-l.pt')
+model7 = YOLO('yolov5-x.pt')
+model8 = YOLO('yolov5-n6.pt')
+model9 = YOLO('yolov6-s.pt')
+model10 = YOLO('yolov6-n.pt')
+model11 = YOLO('yolov6-m.pt')
+model12 = YOLO('yolov6-l.pt')
+model13 = YOLO('yolov8-s.pt')
+model14 = YOLO('yolov8-n.pt')
+model15 = YOLO('yolov8-m.pt')
+model16 = YOLO('yolov8-l.pt')
+model17 = YOLO('yolov8-x.pt')
+
+
+
+models = []
+models.append(model0)
+models.append(model1)
+models.append(model2)
+models.append(model3)
+models.append(model4)
+models.append(model5)
+models.append(model6)
+models.append(model7)
+models.append(model8)
+models.append(model9)
+models.append(model10)
+models.append(model11)
+models.append(model12)
+models.append(model13)
+models.append(model14)
+models.append(model15)
+models.append(model16)
+models.append(model17)
+path  = [['plot.JPG']]
+
+
+
+
+def show_preds_image(image_path, selection):
+    image = cv2.imread(image_path)
+    outputs = models[selection].predict(source=image_path)
+    results = outputs[0].cpu().numpy()
+    for i, det in enumerate(results.boxes.xyxy):
+        cv2.rectangle(
+            image,
+            (int(det[0]), int(det[1])),
+            (int(det[2]), int(det[3])),
+            color=(0, 0, 255),
+            thickness=2,
+            lineType=cv2.LINE_AA
+        )
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+inputs = [
+    gr.components.Image(type="filepath", label="Input Image"),
+    gr.components.Dropdown(choices=[str(i) for i in range(len(models))], label="Select Model", type="index"),
+]
+outputs_image = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+model_select = []
+interface_image = gr.Interface(
+    fn=show_preds_image,
+    inputs=inputs,
+    outputs=outputs_image,
+    title="Paddy Growth Stage Recognition",
+    examples=path,
+    cache_examples=False,
+)
+
+
+
+gr.TabbedInterface(
+    [interface_image],
+    tab_names=['Image inference']
+).queue().launch()

requirements.txt

@@ -0,0 +1,48 @@
+# 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
+
+# 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
+