backup

2026-05-15 18:02:38 +02:00
parent 72016728e2
commit 307e452251
175 changed files with 9316 additions and 0 deletions
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+"""
+Full hero reel regeneration — 7-shot narrative arc.
+1. Door opens, muddy boots run in
+2. Mud tracked across carpet
+3. Stain on upholstered chair
+4. Carpet cleaning machine extracting dirt
+5. Clean bright staircase
+6. Office building wide carpet
+7. Restaurant with carpet
+"""
+import os, sys, time, subprocess
+
+try:
+    from google import genai
+    from google.genai import types
+except ImportError:
+    os.system(f"{sys.executable} -m pip install google-genai --quiet")
+    from google import genai
+    from google.genai import types
+
+API_KEY  = os.environ.get("GEMINI_API_KEY", "AIzaSyB_1p8KvaT_rdNJGPs8HKk8bKsvUlcL6Kg")
+BASE_DIR = os.path.dirname(os.path.dirname(__file__))
+VID_DIR  = os.path.join(BASE_DIR, "assets", "videos", "hero", "clips")
+REEL_OUT = os.path.join(BASE_DIR, "assets", "videos", "hero", "hero-reel.mp4")
+os.makedirs(VID_DIR, exist_ok=True)
+client   = genai.Client(api_key=API_KEY)
+
+SHOTS = [
+    {
+        "name": "v2-shot-01-door-entry",
+        "prompt": (
+            "Cinematic slow-motion wide shot. A wooden front door of an upstate New York home swings open. "
+            "A child and an adult walk inside wearing muddy boots. Camera stays low at floor level. "
+            "The boots leave dark muddy tracks across the beige carpet in the entryway with each step. "
+            "Warm afternoon light pours through the open door. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-02-mud-on-carpet",
+        "prompt": (
+            "Extreme close-up slow-motion shot at carpet level. Muddy boot soles press into clean beige carpet, "
+            "leaving dark brown mud stains and wet footprints with each step. "
+            "Camera is low, tight on the boots and the mud soaking into carpet fibers. "
+            "Dramatic side lighting. No faces visible. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-03-stain-on-chair",
+        "prompt": (
+            "Close-up cinematic shot of a light grey upholstered armchair. "
+            "A visible dark stain spreads across one cushion. "
+            "Camera slowly pushes in on the stain, showing the soiled fabric texture. "
+            "Warm natural light from a window. No people. No equipment. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-04-extraction-carpet",
+        "prompt": (
+            "Cinematic slow-motion wide shot. A technician pushes a Rug Doctor style carpet cleaning machine "
+            "steadily forward across a beige living room carpet. The machine is a tall upright unit with a handle "
+            "and flat rectangular cleaning head — like a large upright vacuum cleaner. "
+            "The carpet behind the machine is visibly brighter and cleaner than the carpet ahead of it. "
+            "No steam. No water spraying. Warm room light. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-05-clean-stairs",
+        "prompt": (
+            "Cinematic slow-motion shot looking up a bright residential carpeted staircase. "
+            "Each step has clean, fresh, plush beige carpet. Warm natural light from above. "
+            "Wood banisters on the sides. The carpet looks spotless and freshly cleaned. "
+            "No people. No machines. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-06-office",
+        "prompt": (
+            "Wide cinematic shot of a bright modern commercial office lobby. "
+            "Large windows, abundant natural daylight. Clean grey commercial carpet covers the entire floor. "
+            "White walls, glass partitions, professional lighting. Carpet looks spotlessly clean. "
+            "No people. No machines. Photorealistic."
+        ),
+    },
+    {
+        "name": "v2-shot-07-restaurant",
+        "prompt": (
+            "Wide cinematic shot of an upscale restaurant dining room with carpeted floors. "
+            "Warm ambient lighting, white tablecloths, wood accents. "
+            "The carpet is clean, rich, and well-maintained throughout the space. "
+            "No people. No machines. Photorealistic, luxurious atmosphere."
+        ),
+    },
+]
+
+MODEL = "veo-3.1-generate-preview"
+
+def poll(op, timeout=600):
+    elapsed = 0
+    while not op.done:
+        if elapsed >= timeout:
+            return None
+        print(f"  Waiting... ({elapsed}s)")
+        time.sleep(15)
+        elapsed += 15
+        op = client.operations.get(op)
+    return op
+
+saved = []
+for item in SHOTS:
+    out_path = os.path.join(VID_DIR, f"{item['name']}.mp4")
+    print(f"\n[{SHOTS.index(item)+1}/{len(SHOTS)}] {item['name']}...")
+    try:
+        op = client.models.generate_videos(
+            model=MODEL,
+            prompt=item["prompt"],
+            config=types.GenerateVideosConfig(
+                aspect_ratio="16:9", resolution="720p",
+                duration_seconds=6, number_of_videos=1,
+            ),
+        )
+        op = poll(op)
+        if op and op.response and op.response.generated_videos:
+            vid = op.response.generated_videos[0].video
+            video_bytes = client.files.download(file=vid)
+            if video_bytes:
+                with open(out_path, "wb") as f:
+                    f.write(video_bytes)
+                print(f"  Saved ({os.path.getsize(out_path)//1024}KB)")
+                saved.append(out_path)
+            else:
+                try:
+                    vid.save(out_path)
+                    print(f"  Saved via .save()")
+                    saved.append(out_path)
+                except Exception as e2:
+                    print(f"  Download failed: {e2}")
+        else:
+            print(f"  No video returned")
+    except Exception as e:
+        print(f"  Error: {e}")
+
+print(f"\n{len(saved)}/{len(SHOTS)} shots saved")
+
+if len(saved) < 2:
+    print("Not enough clips — skipping reconcat.")
+else:
+    order = [os.path.join(VID_DIR, f"{s['name']}.mp4") for s in SHOTS
+             if os.path.exists(os.path.join(VID_DIR, f"{s['name']}.mp4"))]
+    concat_file = os.path.join(VID_DIR, "concat-v2.txt")
+    with open(concat_file, "w") as f:
+        for p in order:
+            f.write(f"file '{p}'\n")
+    result = subprocess.run(
+        ["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", concat_file,
+         "-c:v", "libx264", "-crf", "22", "-preset", "fast", "-movflags", "+faststart", REEL_OUT],
+        capture_output=True, text=True
+    )
+    if result.returncode == 0:
+        print(f"  Reel saved ({os.path.getsize(REEL_OUT)//1024}KB)")
+    else:
+        print(f"  ffmpeg error: {result.stderr[-300:]}")
+
+print("\nDone.")