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.gitattributes

@@ -33,3 +33,4 @@ 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
+mivideo.mp4 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,46 @@
+import cv2
+import gradio as gr
+
+def process_video(video):
+    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+    eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
+    estadoanterior = ""
+    estadoactual = ""
+    output_frames = []
+
+    cap = cv2.VideoCapture(video)
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if frame is None:
+            break
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        hh, ww = gray.shape
+        faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+        for (x, y, w, h) in faces:
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
+            cv2.line(frame, (int(ww / 2), 0), (int(ww / 2), hh), (0, 0, 255), 2)
+            if x <= ww / 2:
+                estadoactual = "I"
+                if estadoactual != estadoanterior:
+                    estadoanterior = estadoactual
+            else:
+                estadoactual = "D"
+                if estadoactual != estadoanterior:
+                    estadoanterior = estadoactual
+
+        output_frames.append(frame)
+
+    cap.release()
+    return output_frames
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=process_video,
+    inputs=gr.Video(label="Sube tu video"),
+    outputs=gr.Video(label="Video procesado"),
+    title="Detección de Rostros y Ojos",
+    description="Este espacio detecta rostros y ojos en un video subido.",
+)
+
+if __name__ == "__main__":
+    demo.launch()

mivideo.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c8b03caab67ff8328e3ad9aaa13b5538a733008fdc63454fcf8c713a471f2ed
+size 28341120

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+opencv-python
+opencv-python-headless