main.py

main.py

+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from uvicorn import run
+import cv2
+import numpy as np
+from PIL import Image
+import shutil
+import shutil
+import os
+
+app = FastAPI()
+
+origins = ["*"]
+methods = ["*"]
+headers = ["*"]
+
+app.add_middleware(
+    CORSMiddleware, 
+    allow_origins = origins,
+    allow_credentials = True,
+    allow_methods = methods,
+    allow_headers = headers    
+)
+
+origin = r"C:\xampp\htdocs\wcolor\php\files\fishwatercolor.jpg"
+target = r"D:\Lectures\4thYear2ndSem\RP\Water\watercolor\fishwatercolor.jpg"
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+
+templates = Jinja2Templates(directory="templates")
+
+blue_range = np.array([[100, 50, 50], [130, 255, 255]])
+green_range = np.array([[40, 50, 50], [80, 255, 255]])
+red_range1 = np.array([[0, 50, 50], [10, 255, 255]])
+red_range2 = np.array([[170, 50, 50], [180, 255, 255]])
+
+def show_water_color():
+
+    shutil.copy(origin,target)
+    # Get the image from the frontend
+    # image_file = request.files['image']
+    # img = Image.open(image_file)
+    img = Image.open("fishwatercolor.jpg")
+
+    # Convert the image to the HSV color space
+    hsv_img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2HSV)
+
+    # Calculate the percentage of blue pixels in the image
+    blue_mask = cv2.inRange(hsv_img, blue_range[0], blue_range[1])
+    blue_pixels = np.sum(blue_mask == 255)
+    total_pixels = blue_mask.shape[0] * blue_mask.shape[1]
+    blue_percent = blue_pixels / total_pixels * 100
+
+    # Calculate the percentage of green pixels in the image
+    green_mask = cv2.inRange(hsv_img, green_range[0], green_range[1])
+    green_pixels = np.sum(green_mask == 255)
+    green_percent = green_pixels / total_pixels * 100
+
+    # Calculate the percentage of red pixels in the image
+    red_mask1 = cv2.inRange(hsv_img, red_range1[0], red_range1[1])
+    red_mask2 = cv2.inRange(hsv_img, red_range2[0], red_range2[1])
+    red_mask = cv2.addWeighted(red_mask1, 1.0, red_mask2, 1.0, 0.0)
+    red_pixels = np.sum(red_mask == 255)
+    red_percent = red_pixels / total_pixels * 100
+
+    # Determine the dominant color of the image
+    colors, counts = np.unique(hsv_img.reshape(-1, hsv_img.shape[-1]), axis=0, return_counts=True)
+    max_count_index = np.argmax(counts)
+    dominant_color = colors[max_count_index]
+
+    # Process the water color
+    if green_percent > 10:
+        water_color = 'green'
+    elif green_percent < 1:
+        water_color = 'brown'
+    else:
+        water_color = 'normal'
+
+    # Return the results as JSON
+    # results = {
+    #     'image_name': image_file.filename,
+    #     'blue_pixels_percent': blue_percent,
+    #     'green_pixels_percent': green_percent,
+    #     'red_pixels_percent': red_percent,
+    #     'dominant_color': dominant_color.tolist(),
+    #     'water_color': water_color
+    # }
+    results = {
+        'water_color': water_color
+    }
+    return results
+
+@app.get("/getResult")
+async def send_image_get_prediction(request: Request):
+    rt = show_water_color()
+    return templates.TemplateResponse("Saveimage.html", {"request": request, "variable": rt})
+
+
+
+
+if __name__ == "__main__":
+    port = int(os.environ.get('PORT', 5000))
+    run(app, host="0.0.0.0", port=port)
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