Duplicate from Atualli/yolov5g

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: Yolov5g
+emoji: 🏆
+colorFrom: yellow
+colorTo: blue
+sdk: gradio
+sdk_version: 3.36.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: Atualli/yolov5g
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import torch
+import json
+import yolov5
+
+# Images
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt','yolov5s.pt')
+
+model_path = "yolov5x.pt" #"yolov5s.pt" #"yolov5m.pt", "yolov5l.pt", "yolov5x.pt",
+image_size = 640,
+conf_threshold = 0.25,
+iou_threshold = 0.45,
+model = yolov5.load(model_path, device="cpu")
+
+def yolov5_inference(
+    image: gr.inputs.Image = None,
+   
+):
+    """
+    YOLOv5 inference function
+    Args:
+        image: Input image
+        model_path: Path to the model
+        image_size: Image size
+        conf_threshold: Confidence threshold
+        iou_threshold: IOU threshold
+    Returns:
+        Rendered image
+    """
+    
+    results = model([image], size=image_size)
+    tensor = {
+      "tensorflow": [ 
+      ]
+    }
+
+    if results.pred is not None:
+        for i, element in enumerate(results.pred[0]):
+            object = {}
+            #print (element[0])
+            itemclass = round(element[5].item())
+            object["classe"] = itemclass
+            object["nome"] = results.names[itemclass]
+            object["score"] = element[4].item()
+            object["x"] = element[0].item()
+            object["y"] = element[1].item()
+            object["w"] = element[2].item()
+            object["h"] = element[3].item()
+            tensor["tensorflow"].append(object)
+          
+  
+
+    text = json.dumps(tensor)
+    #print (text)
+    return text #results.render()[0]
+        
+
+inputs = [
+    gr.inputs.Image(type="pil", label="Input Image"),
+]
+
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+title = "YOLOv5"
+description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."
+
+examples = [['zidane.jpg'], ['image3.jpg']]
+demo_app = gr.Interface(
+    fn=yolov5_inference,
+    inputs=inputs,
+    outputs=["text"],
+    title=title,
+    examples=examples,
+    #cache_examples=True,
+    #live=True,
+    #theme='huggingface',
+)
+demo_app.launch(debug=True, enable_queue=True)

app1.py

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+import gradio as gr
+import torch
+import json
+import yolov5
+
+# Images
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt','yolov5s.pt')
+
+model_path = "yolov5x.pt" #"yolov5s.pt" #"yolov5m.pt", "yolov5l.pt", "yolov5x.pt",
+image_size = 640,
+conf_threshold = 0.25,
+iou_threshold = 0.45,
+model = yolov5.load(model_path, device="cpu")
+
+def yolov5_inference(
+    image: gr.inputs.Image = None,
+   
+):
+    """
+    YOLOv5 inference function
+    Args:
+        image: Input image
+        model_path: Path to the model
+        image_size: Image size
+        conf_threshold: Confidence threshold
+        iou_threshold: IOU threshold
+    Returns:
+        Rendered image
+    """
+    
+    results = model([image], size=image_size)
+    tensor = {
+      "tensorflow": [ 
+      ]
+    }
+
+    if results.pred is not None:
+        for i, element in enumerate(results.pred[0]):
+            object = {}
+            #print (element[0])
+            itemclass = round(element[5].item())
+            object["classe"] = itemclass
+            object["nome"] = results.names[itemclass]
+            object["score"] = element[4].item()
+            object["x"] = element[0].item()
+            object["y"] = element[1].item()
+            object["w"] = element[2].item()
+            object["h"] = element[3].item()
+            tensor["tensorflow"].append(object)
+          
+  
+
+    text = json.dumps(tensor)
+    #print (text)
+    return text #results.render()[0]
+        
+
+inputs = [
+    gr.inputs.Image(type="pil", label="Input Image"),
+]
+
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+title = "YOLOv5"
+description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."
+
+examples = [['zidane.jpg'], ['image3.jpg']]
+demo_app = gr.Interface(
+    fn=yolov5_inference,
+    inputs=inputs,
+    outputs=["text"],
+    title=title,
+    examples=examples,
+    #cache_examples=True,
+    #live=True,
+    #theme='huggingface',
+)
+demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8080, enable_queue=True)
+demo_app.launch(debug=True, enable_queue=True)
+#demo_app.launch(debug=True, server_port=8083, enable_queue=True)

checkYolov5g.sh

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+#!/bin/sh
+export path=/home/atualli/.local/lib/python3.8/site-packages:$PATH
+cd ~/Projetos/huggingface/yolov7
+SERVER=192.168.0.153
+PORT=8080
+
+if lsof -Pi :$PORT -sTCP:LISTEN -t >/dev/null ; then
+    echo "running"
+else
+    ./telegramCrise.sh "reiniciando_yolox_V5G_linux_192.168.0.153:8080"
+    pkill -f app1.py
+    python app1.py &
+    echo "not running"
+fi
+
+

requirements.txt

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+torch
+yolov5

telegramCrise.sh

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+curl -X POST "https://api.telegram.org/bot766543741:AAE0oO_ni_QYkfS8tZxC-VZt0RJztFiZNHc/sendMessage?chat_id=-927074982&text=$1"