Upload New File

leafAfflictions_BACKEND/app.py

+from flask import Flask, request, jsonify
+import cv2
+import numpy as np
+import os
+import time
+
+
+app = Flask(__name__)
+
+UPLOAD_FOLDER = 'uploads'
+if not os.path.exists(UPLOAD_FOLDER):
+    os.makedirs(UPLOAD_FOLDER)
+
+
+
+# Function to check if the image is likely a leaf and calculate affliction percentage
+
+def is_likely_leaf(img_rgb, mask):
+    total_pixels = mask.sum() / 255  # Count of white pixels in the mask
+
+    # If the object is too small, it's likely not a leaf
+    if total_pixels < 5000:  # this threshold might need adjustment based on your image resolutions
+        return False
+
+    # Convert image to HSV
+    hsv = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV)
+
+    # Define a mask for green pixels
+    green_mask = cv2.inRange(hsv, (28, 0, 26), (95, 255, 255))
+
+    # Count green pixels within the object (leaf)
+    green_pixels = cv2.bitwise_and(green_mask, green_mask, mask=mask).sum() / 255
+
+    green_ratio = green_pixels / total_pixels
+
+    # If more than 30% of the leaf is green, it's likely a leaf
+    if green_ratio > 0.3:  # adjust this threshold as needed
+        return True
+    return False
+def process_image(img):
+    timestamp = int(time.time())
+
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    blurred_gray = cv2.GaussianBlur(gray, (5, 5), 0)
+    binary = cv2.adaptiveThreshold(blurred_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
+    kernel = np.ones((5, 5), np.uint8)
+    binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
+    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    mask = np.zeros_like(gray)
+    if contours:
+        leaf_contour = max(contours, key=cv2.contourArea)
+        cv2.drawContours(mask, [leaf_contour], -1, (255), thickness=cv2.FILLED)
+
+    gray_background = np.ones_like(img_rgb) * 127  # A (127,127,127) RGB image
+    leaf_on_gray = cv2.bitwise_and(gray_background, gray_background, mask=cv2.bitwise_not(mask)) + cv2.bitwise_and(
+        img_rgb, img_rgb, mask=mask)
+
+    if is_likely_leaf(leaf_on_gray, mask):
+        img_HSV = cv2.cvtColor(leaf_on_gray, cv2.COLOR_RGB2HSV)
+        healthy_mask = cv2.inRange(img_HSV, (28, 0, 26), (95, 255, 255))
+        inverse_healthy_mask = cv2.bitwise_not(healthy_mask)
+        total_leaf_area = np.sum(mask == 255)
+        afflicted_area = np.sum((inverse_healthy_mask == 255) & (mask == 255))
+        affliction_percentage = round((afflicted_area / total_leaf_area) * 100, 2)
+
+        # leaf_on_gray_path = f'leaf_on_gray_{timestamp}.jpeg'
+        # cv2.imwrite(leaf_on_gray_path, cv2.cvtColor(leaf_on_gray, cv2.COLOR_RGB2BGR))
+
+        leaf_on_gray_path = os.path.join(UPLOAD_FOLDER, f'leaf_on_gray_{timestamp}.jpeg')
+        cv2.imwrite(leaf_on_gray_path, cv2.cvtColor(leaf_on_gray, cv2.COLOR_RGB2BGR))
+
+        # healthy_path = f'healthy_areas_{timestamp}.jpeg'
+        # cv2.imwrite(healthy_path, cv2.bitwise_and(leaf_on_gray, leaf_on_gray, mask=healthy_mask))
+
+        healthy_path = os.path.join(UPLOAD_FOLDER, f'healthy_areas_{timestamp}.jpeg')
+        cv2.imwrite(healthy_path, cv2.bitwise_and(leaf_on_gray, leaf_on_gray, mask=healthy_mask))
+
+        # afflictions_path = f'afflictions_{timestamp}.jpeg'
+        # cv2.imwrite(afflictions_path, cv2.bitwise_and(leaf_on_gray, leaf_on_gray, mask=inverse_healthy_mask))
+
+        afflictions_path = os.path.join(UPLOAD_FOLDER, f'afflictions_{timestamp}.jpeg')
+        cv2.imwrite(afflictions_path, cv2.bitwise_and(leaf_on_gray, leaf_on_gray, mask=inverse_healthy_mask))
+
+        # mask_image_path = f'mask_image_{timestamp}.jpeg'
+        # cv2.imwrite(mask_image_path, np.where(mask == 255, inverse_healthy_mask, 127))
+
+        mask_image_path = os.path.join(UPLOAD_FOLDER, f'mask_image_{timestamp}.jpeg')
+        cv2.imwrite(mask_image_path, np.where(mask == 255, inverse_healthy_mask, 127))
+
+        return affliction_percentage, True, leaf_on_gray_path, healthy_path, afflictions_path, mask_image_path
+    else:
+        return None, False, None, None, None, None
+
+
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    try:
+        uploaded_file = request.files['file']
+
+        if uploaded_file.filename != '':
+            nparr = np.fromstring(uploaded_file.read(), np.uint8)
+            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+            affliction_percentage, is_leaf, leaf_on_gray_path, healthy_path, afflictions_path, mask_image_path = process_image(img)
+
+            if is_leaf:
+                return jsonify({
+                    "success": True,
+                    "affliction_percentage": affliction_percentage,
+                    "leaf_on_gray_image": leaf_on_gray_path,
+                    "healthy_areas_image": healthy_path,
+                    "afflictions_image": afflictions_path,
+                    "mask_image": mask_image_path
+                })
+            else:
+                return jsonify({"success": False, "error": "Image is not a clear picture of a leaf."})
+
+        else:
+            return jsonify({"success": False, "error": "No file uploaded."})
+    except Exception as e:
+        return jsonify({"success": False, "error": str(e)})
+
+from flask import send_from_directory
+
+@app.route('/uploads/<filename>')
+def uploaded_file(filename):
+    return send_from_directory(UPLOAD_FOLDER, filename)
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)