Spaces:
Sleeping
Sleeping
Add requirements and implement Gradio interface for people counting
Browse files- README.md +37 -32
- app.py +114 -0
- pyproject.toml +1 -0
- requirements.txt +5 -0
- uv.lock +532 -4
README.md
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#
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This
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## Features
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- Real-time person detection using YOLOv8
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- Object tracking to count unique entries
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2. Install dependencies:
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```bash
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pip install .
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```
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```bash
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python main.py video_path
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```
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##
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# Store Entry People Counter
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This Hugging Face Space provides an interface to count the number of people entering through a line in a video (e.g., store entrance). It uses YOLOv8 for person detection and tracking.
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[](https://huggingface.co/spaces/your-username/store-entry-counter)
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## Features
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- Real-time person detection using YOLOv8
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- Object tracking to count unique entries
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- Visual feedback:
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- Green line: Counting threshold
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- Blue boxes: Detected people
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- Yellow dots: Tracking points
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- Red circles: Entry indicators
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- Live counter with background
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## How to Use
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1. Open the Space in your browser
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2. Upload a video file showing people entering (supports most video formats)
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3. Wait for processing (time depends on video length)
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4. View the processed video with counting visualization
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5. See the final count in the results box
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## Understanding the Output
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- **Green Line**: This is the counting threshold. When people cross this line from top to bottom, they are counted
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- **Blue Boxes**: Shows detected people in each frame
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- **Yellow Dots**: Tracks the position of each person
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- **Red Circles**: Appears when someone crosses the line and is counted
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- **Counter**: Shows the running total of people who have entered
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## Technical Details
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- Uses YOLOv8 for accurate person detection
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- Implements object tracking to maintain person identity across frames
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- Counts unique crossings to avoid double-counting
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- Processes video frames sequentially with progress indication
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## Limitations
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- Best results with clear, unobstructed view of people
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- May not work well in extremely crowded scenes
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- Video quality affects detection accuracy
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- Processing time depends on video length and resolution
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## Credits
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Built using:
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- [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)
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- [Gradio](https://gradio.app/)
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- OpenCV
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app.py
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import gradio as gr
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import cv2
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import numpy as np
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from ultralytics import YOLO
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from collections import defaultdict
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import tempfile
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import os
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class PersonCounter:
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def __init__(self, line_position=0.5):
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self.model = YOLO("yolov8n.pt")
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self.tracker = defaultdict(list)
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self.crossed_ids = set()
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self.line_position = line_position
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self.count = 0
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def process_frame(self, frame):
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height, width = frame.shape[:2]
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line_y = int(height * self.line_position)
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# Draw counting line
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cv2.line(frame, (0, line_y), (width, line_y), (0, 255, 0), 2)
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# Run detection and tracking
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results = self.model.track(frame, persist=True, classes=[0])
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if results[0].boxes.id is not None:
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boxes = results[0].boxes.xyxy.cpu().numpy()
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track_ids = results[0].boxes.id.cpu().numpy().astype(int)
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for box, track_id in zip(boxes, track_ids):
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# Draw bounding box
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cv2.rectangle(frame, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])),
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(255, 0, 0), 2)
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# Get feet position
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center_x = (box[0] + box[2]) / 2
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feet_y = box[3]
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# Draw tracking point
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cv2.circle(frame, (int(center_x), int(feet_y)), 5, (0, 255, 255), -1)
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# Store tracking history
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if track_id in self.tracker:
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prev_y = self.tracker[track_id][-1]
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# Check if person has crossed the line
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if prev_y < line_y and feet_y >= line_y and track_id not in self.crossed_ids:
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self.crossed_ids.add(track_id)
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self.count += 1
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# Draw crossing indicator
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cv2.circle(frame, (int(center_x), int(line_y)), 8, (0, 0, 255), -1)
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self.tracker[track_id] = [feet_y]
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# Draw count with background
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count_text = f"Count: {self.count}"
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font = cv2.FONT_HERSHEY_SIMPLEX
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font_scale = 1.5
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thickness = 3
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(text_width, text_height), _ = cv2.getTextSize(count_text, font, font_scale, thickness)
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cv2.rectangle(frame, (10, 10), (20 + text_width, 20 + text_height),
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(0, 0, 0), -1)
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cv2.putText(frame, count_text, (15, 15 + text_height),
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font, font_scale, (0, 255, 0), thickness)
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return frame
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def process_video(video_path, progress=gr.Progress()):
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# Create temp directory for output
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temp_dir = tempfile.mkdtemp()
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output_path = os.path.join(temp_dir, "result.mp4")
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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raise ValueError("Could not open video file")
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = int(cap.get(cv2.CAP_PROP_FPS))
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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writer = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
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counter = PersonCounter(line_position=0.5)
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for frame_idx in progress.tqdm(range(total_frames)):
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ret, frame = cap.read()
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if not ret:
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break
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processed_frame = counter.process_frame(frame)
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writer.write(processed_frame)
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cap.release()
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writer.release()
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return output_path, f"Final count: {counter.count} people entered"
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# Create Gradio interface
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demo = gr.Interface(
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fn=process_video,
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inputs=gr.Video(label="Upload a video file"),
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outputs=[
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gr.Video(label="Processed Video"),
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gr.Textbox(label="Results")
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],
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title="Store Entry People Counter",
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description="Upload a video to count the number of people entering through a line. The green line represents the counting threshold, blue boxes show detected people, and the counter increases when someone crosses the line from top to bottom.",
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examples=[],
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cache_examples=False
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)
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if __name__ == "__main__":
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demo.launch()
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pyproject.toml
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"numpy",
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"ultralytics",
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"pip>=25.0.1",
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]
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"numpy",
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"ultralytics",
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"pip>=25.0.1",
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"gradio>=5.23.0",
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]
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requirements.txt
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gradio>=4.0.0
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opencv-python-headless
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numpy
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ultralytics
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torch
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uv.lock
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revision = 1
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requires-python = ">=3.12"
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resolution-markers = [
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"sys_platform == 'darwin'",
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"
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"sys_platform == '
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]
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[[package]]
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version = "0.1.0"
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source = { virtual = "." }
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dependencies = [
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{ name = "numpy" },
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{ name = "opencv-python" },
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{ name = "pip" },
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[package.metadata]
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requires-dist = [
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{ name = "numpy" },
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{ name = "opencv-python" },
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{ name = "pip", specifier = ">=25.0.1" },
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{ url = "https://files.pythonhosted.org/packages/0e/f6/65ecc6878a89bb1c23a086ea335ad4bf21a588990c3f535a227b9eea9108/charset_normalizer-3.4.1-py3-none-any.whl", hash = "sha256:d98b1668f06378c6dbefec3b92299716b931cd4e6061f3c875a71ced1780ab85", size = 49767 },
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]
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[[package]]
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name = "colorama"
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version = "0.4.6"
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{ url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321 },
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]
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[[package]]
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name = "filelock"
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version = "3.18.0"
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{ url = "https://files.pythonhosted.org/packages/56/53/eb690efa8513166adef3e0669afd31e95ffde69fb3c52ec2ac7223ed6018/fsspec-2025.3.0-py3-none-any.whl", hash = "sha256:efb87af3efa9103f94ca91a7f8cb7a4df91af9f74fc106c9c7ea0efd7277c1b3", size = 193615 },
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]
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| 176 |
[[package]]
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| 177 |
name = "idna"
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| 178 |
version = "3.10"
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@@ -245,6 +479,18 @@ wheels = [
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| 245 |
{ url = "https://files.pythonhosted.org/packages/4c/fa/be89a49c640930180657482a74970cdcf6f7072c8d2471e1babe17a222dc/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:be4816dc51c8a471749d664161b434912eee82f2ea66bd7628bd14583a833e85", size = 2349213 },
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| 248 |
[[package]]
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| 249 |
name = "markupsafe"
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| 250 |
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| 320 |
{ url = "https://files.pythonhosted.org/packages/ac/c2/0d5aae823bdcc42cc99327ecdd4d28585e15ccd5218c453b7bcd827f3421/matplotlib-3.10.1-cp313-cp313t-win_amd64.whl", hash = "sha256:bc411ebd5889a78dabbc457b3fa153203e22248bfa6eedc6797be5df0164dbf9", size = 8134832 },
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| 323 |
[[package]]
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| 324 |
name = "mpmath"
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| 325 |
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| 509 |
{ url = "https://files.pythonhosted.org/packages/a4/7d/f1c30a92854540bf789e9cd5dde7ef49bbe63f855b85a2e6b3db8135c591/opencv_python-4.11.0.86-cp37-abi3-win_amd64.whl", hash = "sha256:085ad9b77c18853ea66283e98affefe2de8cc4c1f43eda4c100cf9b2721142ec", size = 39488044 },
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| 512 |
[[package]]
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| 513 |
name = "packaging"
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| 624 |
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| 626 |
[[package]]
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| 627 |
name = "pyparsing"
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| 646 |
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| 647 |
[[package]]
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| 648 |
name = "pytz"
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| 649 |
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| 694 |
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| 695 |
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| 696 |
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| 697 |
[[package]]
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| 698 |
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| 746 |
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| 747 |
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| 748 |
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| 749 |
[[package]]
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| 750 |
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| 755 |
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| 757 |
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| 758 |
[[package]]
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| 759 |
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| 764 |
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| 767 |
[[package]]
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| 768 |
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| 776 |
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| 778 |
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| 779 |
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| 860 |
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