Skill: lnj22/pedestrian-traffic-counting-gemini-count-in-video (version 0.1.0)

--- name: gemini-count-in-video description: Analyze and count objects in videos using Google Gemini API (object counting, pedestrian detection, vehicle tracking, and surveillance video analysis). --- # Gemini Video Understanding Skill ## Purpose This skill enables video analysis and object counting using the Google Gemini API, with a focus on **counting pedestrians**, **detecting objects**, **tracking movement**, and **analyzing surveillance footage**. It supports precise prompting for differentiated counting (e.g., pedestrians vs cyclists vs vehicles). ## When to Use - Counting pedestrians, vehicles, or other objects in surveillance videos - Distinguishing between different types of objects (walkers vs cyclists, cars vs trucks) - Analyzing traffic patterns and movement through a scene - Processing multiple videos for batch object counting - Extracting structured count data from video footage ## Required Libraries The following Python libraries are required: ```python from google import genai from google.genai import types import os import time ``` ## Input Requirements - **File formats**: MP4, MPEG, MOV, AVI, FLV, MPG, WebM, WMV, 3GPP - **Size constraints**: - Use inline bytes for small files (rule of thumb: <20MB). - Use the File API upload flow for larger videos (most surveillance footage). - Always wait for processing to complete before analysis. - **Video quality**: Higher resolution provides better counting accuracy for distant objects - **Duration**: Longer videos may require longer processing times; consider the full video length for accurate counting ## Output Schema For object counting tasks, structure results as JSON: ```json { "success": true, "video_file": "surveillance_001.mp4", "model": "gemini-2.0-flash-exp", "counts": { "pedestrians": 12, "cyclists": 3, "vehicles": 5 }, "notes": "Optional observations about the counting process or edge cases" } ``` ### Field Descriptions - `success`: Whether the analysis completed successfully - `video_file`: Name of the analyzed video file - `model`: Gemini model used for the request - `counts`: Object counts by category - `notes`: Any clarifications or warnings about the count ## Code Examples ### Basic Pedestrian Counting (File API Upload) ```python from google import genai import os import time import re client = genai.Client([REDACTED_SECRET]"GEMINI_API_KEY")) # Upload video (File API for >20MB) myfile = client.files.upload(file="surveillance.mp4") # Wait for processing while myfile.state.name == "PROCESSING": time.sleep(5) myfile = client.files.get(name=myfile.name) if myfile.state.name == "FAILED": raise ValueError("Video processing failed") # Prompt for counting pedestrians with clear exclusion criteria prompt = """Count the total number of pedestrians who are WALKING through the scene in this surveillance video. IMPORTANT RULES: - ONLY count people who are walking on foot - DO NOT count people riding bicycles - DO NOT count people driving cars or other vehicles - Count each unique pedestrian only once, even if they appear in multiple frames Provide your answer as a single integer number representing the total count of pedestrians. Answer with just the number, nothing else. Your answer should be enclosed in <answer> and </answer> tags, such as <answer>5</answer>. """ response = client.models.generate_content( model="gemini-2.0-flash-exp", contents=[prompt, myfile], ) # Parse the response response_text = response.text.strip() match = re.search(r"<answer>(\d+)</answer>", response_text) if match: count = int(match.group(1)) print(f"Pedestrian count: {count}") else: print("Could not parse count from response") ``` ### Batch Processing Multiple Videos ```python from google import genai import os import time import re def upload_and_wait(client, file_path: str, max_wait_s: int = 300): """Upload video and wait for processing.""" myfile = client.files.upload(file=file_path) waited = 0 while myfile.state.name == "PROCESSING" and waited < max_wait_s: time.sleep(5) waited += 5 myfile = client.files.get(name=myfile.name) if myfile.state.name == "FAILED": raise ValueError(f"Video processing failed: {myfile.state.name}") if myfile.state.name == "PROCESSING": raise TimeoutError(f"Processing timeout after {max_wait_s}s") return myfile client = genai.Client([REDACTED_SECRET]"GEMINI_API_KEY")) # Process all videos in directory video_dir = "/app/video" video_extensions = {".mp4", ".mkv", ".avi", ".mov"} results = {} for filename in os.listdir(video_dir): if any(filename.lower().endswith(ext) for ext in video_extensions): video_path = os.path.join(video_dir, filename) print(f"Processing {filename}...") # Upload and analyze myfile = upload_and_wait(client, video_path) response = client.models.generate_content( model="gemini-2.0-flash-exp", contents=["Count pedestrians walking through the scene. Answer with just the number.", myfile], ) # Extract count count = int(re.search(r'\d+', response.text).group()) results[filename] = count print(f" Count: {count}") print(f"\nProcessed {len(results)} videos") # Results dictionary can now be used for further processing or saving ``` ### Differentiating Object Types ```python # Count different categories separately prompt = """Analyze this surveillance video and count: 1. Pedestrians (people walking on foot) 2. Cyclists (people riding bicycles) 3. Vehicles (cars, trucks, motorcycles) RULES: - Count each unique individual/vehicle only once - If someone switches from walking to cycling, count them in their primary mode - Provide counts as three separate numbers Format your answer as: Pedestrians: <number> Cyclists: <number> Vehicles: <number> """ response = client.models.generate_content( model="gemini-2.0-flash-exp", contents=[prompt, myfile], ) # Parse multiple counts text = response.text pedestrians = int(re.search(r'Pedestrians:\s*(\d+)', text).group(1)) cyclists = int(re.search(r'Cyclists:\s*(\d+)', text).group(1)) vehicles = int(re.search(r'Vehicles:\s*(\d+)', text).group(1)) ``` ### Using Answer Tags for Reliable Parsing ```python # Request structured output with XML-like tags prompt = """Count the total number of pedestrians walking through the scene. You should reason and think step by step. Provide your answer as a single integer. Your answer should be enclosed in <answer> and </answer> tags, such as <answer>5</answer>. """ response = client.models.generate_content( model="gemini-2.0-flash-exp", contents=[prompt, myfile], ) # Robust extraction match = re.search(r"<answer>(\d+)</answer>", response.text) if match: count = int(match.group(1)) else: # Fallback: try to find any number in response numbers = re.findall(r'\d+', response.text) count = int(numbers[0]) if numbers else 0 ``` ## Best Practices - **Use the File API** for all surveillance videos (typically >20MB) and always wait for processing to complete. - **Be specific in prompts**: Clearly define what to count and what to exclude (e.g., "walking pedestrians only, not cyclists"). - **Use structured output formats**: Request answers in specific formats (like `<answer>N</answer>`) for reliable parsing. - **Ask for reasoning**: Include "think step by step" to improve counting accuracy. - **Handle edge cases**: Specify rules for partial appearances, people entering/exiting frame, and mode changes. - **Use gemini-2.0-flash-exp or gemini-2.5-flash**: These models provide good balance of speed and accuracy for object counting. - **Test with sample videos**: Verify prompt effectiveness on representative samples before batch processing. ## Error Handling ```python import time def upload_and_wait(client, file_path: str, max_wait_s: int = 300): """Upload video and wait for processing with timeout.""" myfile = client.files.upload(file=file_path) waited = 0 while myfile.state.name == "PROCESSING" and waited < max_wait_s: time.sleep(5) waited += 5 myfile = client.files.get(name=myfile.name) if myfile.state.name == "FAILED": raise ValueError(f"Video processing failed: {myfile.state.name}") if myfile.state.name == "PROCESSING": raise TimeoutError(f"Processing timeout after {max_wait_s}s") return myfile def count_with_fallback(client, video_path): """Count pedestrians with error handling and fallback.""" try: myfile = upload_and_wait(client, video_path) prompt = """Count pedestrians walking through the scene. Answer with just the number in <answer></answer> tags.""" response = client.models.generate_content( model="gemini-2.0-flash-exp", contents=[prompt, myfile], ) # Try structured parsing first match = re.search(r"<answer>(\d+)</answer>", response.text) if match: return int(match.group(1)) # Fallback to any number found numbers = re.findall(r'\d+', response.text) if numbers: return int(numbers[0]) print(f"Warning: Could not parse count, defaulting to 0") return 0 except Exception as e: print(f"Error processing video: {e}") return 0 ``` Common issues: - **Upload processing stuck**: Use timeout logic and fail gracefully after max wait time - **Ambiguous responses**: Use structured output tags like `<answer></answer>` for reliable parsing - **Rate limits**: Add retry logic with exponential backoff for batch processing - **Inconsistent counts**: Be very explicit in prompts about counting rules and exclusions ## Limitations - Counting accuracy depends on video quality, camera angle, and object size/distance - Very crowded scenes may have higher counting variance - Occlusion (objects blocking each other) can affect accuracy - Long videos require longer processing times (typically 5-30 seconds per video) - The model may occasionally misclassify similar objects (e.g., motorcyclist as cyclist) - For highest accuracy, use clear prompts with explicit inclusion/exclusion criteria ## Version History - **1.0.0** (2026-01-21): Tailored for pedestrian traffic counting with focus on object counting, differentiation, and batch processing ## Resources - [Video API Docs](https://ai.google.dev/gemini-api/docs/video-understanding) - [Get API Key](https://aistudio.google.com/apikey) - [Pricing](https://ai.google.dev/pricing) - [Python SDK Documentation](https://ai.google.dev/gemini-api/docs/quickstart?lang=python)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)