Initial commit

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+/home/myominhtet/Desktop/Group filter=lfs diff=lfs merge=lfs -text
+Scraper/detect/yolo/yolov7-main/runs/train/best.pt filter=lfs diff=lfs merge=lfs -text
+Scraper/detect/yolo/yolov7-main/runs/train/last.pt filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,61 +1,79 @@
-import gradio as gr
 import os
 import subprocess
-import cv2
-import numpy as np
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import FileResponse, PlainTextResponse
+import shutil
 
-# Define the detect function
-def detect_and_crop(input_image):
-    # Define paths and parameters
-    weights_path = 'yolo/yolov7-main/runs/train/best.pt'
-    img_size = 640
-    conf = 0.20
-    source = 'dataset/images/train/'  # Folder for input images
+app = FastAPI()
+
+# Define paths and parameters
+weights_path = 'yolo/yolov7-main/runs/train/best.pt'
+img_size = 640
+conf = 0.20
+source_folder = 'dataset/images/train/'
+output_folder = 'out/fixed_folder/'
+
+# Ensure folders exist
+os.makedirs(source_folder, exist_ok=True)
+os.makedirs(output_folder, exist_ok=True)
 
-    # Ensure the input image folder exists
-    os.makedirs(source, exist_ok=True)
+# Root endpoint to prevent 404 on /
+@app.get("/")
+async def root():
+    return PlainTextResponse("Welcome to the YOLOv7 Object Detection API. Use the /detect endpoint to upload an image.")
 
-    # Save the input image to the source directory
-    input_image.save(os.path.join(source, 'input_image.jpg'))
+# Optional: Add a route for favicon.ico to avoid 404
+@app.get("/favicon.ico", include_in_schema=False)
+async def favicon():
+    return PlainTextResponse("", status_code=204)
 
+# Define the detect function
+def detect_and_crop(image_path: str):
     # Run the detection command
     command = [
         'python', 'yolo/yolov7-main/detect.py',
         '--weights', weights_path,
         '--conf-thres', str(conf),
         '--img-size', str(img_size),
-        '--source', os.path.join(source, 'input_image.jpg'),
+        '--source', image_path,
         '--project', 'out/',  # Output directory
         '--name', 'fixed_folder',  # Folder name for results
         '--exist-ok'  # Don't increment folder name
     ]
 
-    # Execute the command
-    subprocess.run(command)
-
-    # Load the result image
-    output_image_path = 'out/fixed_folder/input_image_upscaled.jpg'
+    # Execute the command and check for errors
+    result = subprocess.run(command, capture_output=True, text=True)
+    if result.returncode != 0:
+        raise HTTPException(status_code=500, detail=f"Detection failed: {result.stderr}")
 
-    # Check if the image exists
-    if not os.path.exists(output_image_path):
-        return "No output image found."
+    # Locate the output image in the expected output directory
+    output_files = os.listdir(output_folder)
+    output_image_path = None
+    for file_name in output_files:
+        if file_name.endswith(".jpg") or file_name.endswith(".jpeg") or file_name.endswith(".png"):
+            output_image_path = os.path.join(output_folder, file_name)
+            break
 
-    # Read the output image
-    output_image = cv2.imread(output_image_path)
+    if not output_image_path or not os.path.exists(output_image_path):
+        raise HTTPException(status_code=404, detail="Output image not found.")
 
-    # Convert BGR (OpenCV format) to RGB (Gradio format)
-    output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
+    return output_image_path
 
-    return output_image
+# FastAPI endpoint to accept an image, perform detection, and return the processed image
+@app.post("/detect")
+async def detect_endpoint(file: UploadFile = File(...)):
+    # Save the uploaded file to the source folder
+    input_image_path = os.path.join(source_folder, 'input_image.jpg')
+    with open(input_image_path, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
 
-# Set up the Gradio interface
-iface = gr.Interface(
-    fn=detect_and_crop,
-    inputs=gr.Image(type="pil"),  # Input type
-    outputs=gr.Image(type="numpy"),  # Output type
-    title="YOLOv7 Object Detection",
-    description="Upload an image for object detection and cropping."
-)
+    # Perform detection and get the path to the output image
+    try:
+        output_image_path = detect_and_crop(input_image_path)
+    except HTTPException as e:
+        raise e
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error: {str(e)}")
 
-# Launch the app
-iface.launch()
+    # Return the output image as a response
+    return FileResponse(output_image_path, media_type="image/jpeg")

traced_model.pt

@@ -1,3 +1,3 @@
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+oid sha256:2e3e93c89ce09bb5995f3d6d1446814239bdc677684bce26f3573e0e39591ae8
+size 283665354