Update app.py

app.py

@@ -4,23 +4,21 @@ from sklearn.linear_model import LogisticRegression
 import cv2
 import os
 from werkzeug.utils import secure_filename
-from scipy.interpolate import splprep, splev  # For smooth trajectory
+from scipy.interpolate import splprep, splev
 
 app = Flask(__name__)
 
-# Configure upload folder
 UPLOAD_FOLDER = '/tmp/uploads'
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)
 app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 ALLOWED_EXTENSIONS = {'mp4', 'avi', 'mov'}
 
-# Dummy ML model for LBW decision (to be replaced with a real model)
 def train_dummy_model():
     X = np.array([
-        [0.5, 0.0, 0.4, 0.5, 30, 0],  # Not Out
-        [0.5, 0.5, 0.5, 0.5, 35, 2],  # Out
-        [0.6, 0.2, 0.5, 0.6, 32, 1],  # Not Out
-        [0.5, 0.4, 0.5, 0.4, 34, 0],  # Out
+        [0.5, 0.0, 0.4, 0.5, 30, 0],
+        [0.5, 0.5, 0.5, 0.5, 35, 2],
+        [0.6, 0.2, 0.5, 0.6, 32, 1],
+        [0.5, 0.4, 0.5, 0.4, 34, 0],
     ])
     y = np.array([0, 1, 0, 1])
     model = LogisticRegression()
@@ -38,7 +36,7 @@ def smooth_trajectory(points):
     x = [p["x"] for p in points]
     y = [p["y"] for p in points]
     tck, u = splprep([x, y], s=0)
-    u_new = np.linspace(0, 1, 50)  # Smooth with 50 points
+    u_new = np.linspace(0, 1, 50)
     x_new, y_new = splev(u_new, tck)
     return [{"x": x, "y": y} for x, y in zip(x_new, y_new)]
 
@@ -61,7 +59,7 @@ def process_video(video_path):
             break
 
         hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-        mask = cv2.inRange(hsv, (0, 120, 70), (10, 255, 255))  # Adjust for your ball color
+        mask = cv2.inRange(hsv, (0, 120, 70), (10, 255, 255))
         contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
         if contours:
@@ -78,24 +76,22 @@ def process_video(video_path):
                 y_positions.append(norm_y)
                 last_point = current_point
 
-            # Detect pitching (first significant downward movement)
             if len(y_positions) > 2 and not pitching_detected:
                 if y_positions[-1] < y_positions[-2] and y_positions[-2] < y_positions[-3]:
                     pitching_detected = True
                     pitching_x = actual_path[-2]["x"]
                     pitching_y = actual_path[-2]["y"]
 
-            # Detect impact (sudden slowdown or stop)
             if len(actual_path) > 2 and not impact_detected:
                 speed_current = abs(y_positions[-1] - y_positions[-2])
                 speed_prev = abs(y_positions[-2] - y_positions[-3])
-                if speed_current < speed_prev * 0.3:  # Significant slowdown
+                if speed_current < speed_prev * 0.3:
                     impact_detected = True
                     impact_x = actual_path[-1]["x"]
                     impact_y = actual_path[-1]["y"]
 
         frame_count += 1
-        if frame_count > 50:  # Process more frames for accuracy
+        if frame_count > 50:
             break
 
     cap.release()
@@ -113,17 +109,12 @@ def process_video(video_path):
 
     fps = cap.get(cv2.CAP_PROP_FPS) or 30
     speed = (len(actual_path) / (frame_count / fps)) * 0.5
-
-    # Smooth the actual path
     actual_path = smooth_trajectory(actual_path)
-
-    # Projected path with basic physics (linear for now, add swing/spin later)
     projected_path = [
         {"x": impact_x, "y": impact_y},
         {"x": impact_x + spin * 0.1, "y": 1.0}
     ]
 
-    # Determine pitching and impact status
     pitching_status = "Inline" if 0.4 <= pitching_x <= 0.6 else "Outside Leg" if pitching_x < 0.4 else "Outside Off"
     impact_status = "Inline" if 0.4 <= impact_x <= 0.6 else "Outside"
     wicket_status = "Hitting" if 0.4 <= projected_path[-1]["x"] <= 0.6 else "Missing"
@@ -171,5 +162,42 @@ def analyze():
         'wicket': wicket_status
     })
 
+@app.route('/analyze_data', methods=['POST'])
+def analyze_data():
+    data = request.get_json()
+    actual_path = data['actual_path']
+    projected_path = data['projected_path']
+    pitching = data['pitching']
+    impact = data['impact']
+    speed = data['speed']
+    spin = data['spin']
+
+    pitching_x = pitching['x']
+    pitching_y = pitching['y']
+    impact_x = impact['x']
+    impact_y = impact['y']
+
+    pitching_status = "Inline" if 0.4 <= pitching_x <= 0.6 else "Outside Leg" if pitching_x < 0.4 else "Outside Off"
+    impact_status = "Inline" if 0.4 <= impact_x <= 0.6 else "Outside"
+    wicket_status = "Hitting" if 0.4 <= projected_path[-1]["x"] <= 0.6 else "Missing"
+
+    features = np.array([[pitching_x, pitching_y, impact_x, impact_y, speed, spin]])
+    prediction = model.predict(features)[0]
+    confidence = min(model.predict_proba(features)[0][prediction], 0.99)
+    decision = "Out" if prediction == 1 else "Not Out"
+
+    return jsonify({
+        'actual_path': actual_path,
+        'projected_path': projected_path,
+        'decision': decision,
+        'confidence': round(confidence, 2),
+        'pitching': {'x': pitching_x, 'y': pitching_y, 'status': pitching_status},
+        'impact': {'x': impact_x, 'y': impact_y, 'status': impact_status},
+        'wicket': wicket_status
+    })
+
+if __name__ == '__main__':
+    app.run(debug=True, port=5001)
+
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=7860, debug=True)