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README.md

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+---
+title: Odev Trajektori Analizi
+emoji: 🔥
+colorFrom: yellow
+colorTo: gray
+sdk: gradio
+sdk_version: 5.10.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+
+def create_mask(frame):
+    """Frame'de kırmızı renk maskesi oluşturur."""
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+    mask1 = cv2.inRange(hsv, (0, 120, 70), (10, 255, 255))
+    mask2 = cv2.inRange(hsv, (170, 120, 70), (180, 255, 255))
+    mask = mask1 | mask2
+    return mask
+
+def apply_morphology(mask, kernel_size=15):
+    """Maskeye morfolojik işlemler uygular."""
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
+    morph = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    return morph
+
+def find_largest_component(morph):
+    """En büyük bağlı bileşenin merkezini bulur."""
+    num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(morph, connectivity=8)
+    if num_labels > 1:
+        largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA])
+        cX = int(centroids[largest_label][0])
+        cY = int(centroids[largest_label][1])
+        return cX, cY
+    else:
+        return None
+
+def draw_trajectory(frame, trajectory):
+    """Topun izlediği yolu çizer."""
+    if len(trajectory) > 1:
+        pts = np.array(trajectory, np.int32)
+        pts = pts.reshape((-1, 1, 2))
+        cv2.polylines(frame, [pts], False, (0, 255, 0), 2)
+
+def analyze_trajectory(trajectory, std_threshold=10):
+    """Trajektori analizini yapar."""
+    if len(trajectory) < 2:
+        return "Yolu belirlemek için yeterli veri yok."
+    else:
+        x_coords = np.array(trajectory)[:, 0]
+        std_x = np.std(x_coords)
+        return "ok" if std_x < std_threshold else "not ok"
+
+def process_video(video, kernel_size=15, std_threshold=10):
+    """Videoyu işler ve topun yörüngesini analiz eder."""
+    try:
+        cap = cv2.VideoCapture(video)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        _, frame = cap.read()
+
+        if frame is None:
+            return "Videodan çerçeve okunamadı!", None, None
+
+        fourcc = cv2.VideoWriter_fourcc(*'XVID')
+        height, width = frame.shape[:2]
+        try:
+            out_mask = cv2.VideoWriter('mask_output.avi', fourcc, fps, (width, height), False)
+            out_final = cv2.VideoWriter('output.avi', fourcc, fps, (width, height))
+        except:
+            return "Video dosyaları oluşturulamadı!", None, None
+
+        trajectory = []
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            mask = create_mask(frame)
+            out_mask.write(mask)
+
+            morph = apply_morphology(mask, kernel_size)
+
+            center = find_largest_component(morph)
+            if center:
+                cX, cY = center
+                trajectory.append((cX, cY))
+                cv2.circle(frame, (cX, cY), 5, (0, 255, 0), -1)
+
+            draw_trajectory(frame, trajectory)
+
+            out_final.write(frame)
+
+        cap.release()
+        out_mask.release()
+        out_final.release()
+
+        result_text = analyze_trajectory(trajectory, std_threshold)
+
+        return result_text, 'mask_output.avi', 'output.avi'
+
+    except:
+        return "Video işlenirken bir hata oluştu!", None, None
+
+demo = gr.Interface(
+    fn=process_video,
+    inputs=[
+        gr.Video(label="Girdi Video"),
+        gr.Slider(1, 50, value=15, step=1, label="Kernel Boyutu"),  # Accordion içindeki bileşenler ayrı ayrı eklendi
+        gr.Slider(1, 100, value=10, step=1, label="Standart Sapma Eşiği")  # Accordion içindeki bileşenler ayrı ayrı eklendi
+    ],
+    outputs=[
+        gr.Textbox(label="Yol Analiz Sonucu"),
+        gr.Video(label="Maske Video"),
+        gr.Video(label="Çıktı Video")
+    ],
+    examples=[
+        ["ok.mp4"],
+        ["not_ok1.mp4"],
+        ["not_ok2.mp4"]
+    ]
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    demo.launch(show_error=True)

requirements.txt

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+opencv-python
+numpy