Abhishek Gola
commited on
Commit
·
a771188
1
Parent(s):
b1404ee
Added handpose estimation to opencv spaces
Browse files- README.md +6 -0
- app_fixed.py +167 -0
- mp_handpose.py +200 -0
- mp_palmdet.py +2121 -0
- requirements.txt +4 -0
README.md
CHANGED
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@@ -7,6 +7,12 @@ sdk: gradio
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sdk_version: 5.34.2
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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sdk_version: 5.34.2
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app_file: app.py
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pinned: false
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short_description: Handpose estimation using Mediapipe with OpenCV
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tags:
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- opencv
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- handpose-estimation
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- palm-detection
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- mediapipe
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app_fixed.py
ADDED
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@@ -0,0 +1,167 @@
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import gradio as gr
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import cv2 as cv
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import tempfile
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from mp_handpose import MPHandPose
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from mp_palmdet import MPPalmDet
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import numpy as np
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from huggingface_hub import hf_hub_download
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handpose_detector_path = hf_hub_download(repo_id="opencv/handpose_estimation_mediapipe", filename="handpose_estimation_mediapipe_2023feb.onnx")
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palm_detector_path = hf_hub_download(repo_id="opencv/palm_detection_mediapipe", filename="palm_detection_mediapipe_2023feb.onnx")
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backend_id = cv.dnn.DNN_BACKEND_OPENCV
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target_id = cv.dnn.DNN_TARGET_CPU
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palm_detector = MPPalmDet(modelPath=palm_detector_path, nmsThreshold=0.3, scoreThreshold=0.6, backendId=backend_id, targetId=target_id)
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handpose_detector = MPHandPose(modelPath=handpose_detector_path, confThreshold=0.9, backendId=backend_id, targetId=target_id)
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def visualize(image, hands):
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display_screen = image.copy()
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display_3d = np.zeros((400, 400, 3), np.uint8)
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cv.line(display_3d, (200, 0), (200, 400), (255, 255, 255), 2)
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cv.line(display_3d, (0, 200), (400, 200), (255, 255, 255), 2)
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cv.putText(display_3d, 'Main View', (0, 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
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cv.putText(display_3d, 'Top View', (200, 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
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cv.putText(display_3d, 'Left View', (0, 212), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
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cv.putText(display_3d, 'Right View', (200, 212), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
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def draw_lines(image, landmarks, is_draw_point=True, thickness=2):
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connections = [
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(0, 1), (1, 2), (2, 3), (3, 4),
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(0, 5), (5, 6), (6, 7), (7, 8),
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(0, 9), (9, 10), (10, 11), (11, 12),
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(0, 13), (13, 14), (14, 15), (15, 16),
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(0, 17), (17, 18), (18, 19), (19, 20)
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]
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for (i, j) in connections:
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cv.line(image, landmarks[i], landmarks[j], (255, 255, 255), thickness)
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if is_draw_point:
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for p in landmarks:
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cv.circle(image, p, thickness, (0, 0, 255), -1)
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gc = GestureClassification()
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for handpose in hands:
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bbox = handpose[0:4].astype(np.int32)
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handedness = handpose[-2]
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handedness_text = 'Left' if handedness <= 0.5 else 'Right'
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landmarks_screen = handpose[4:67].reshape(21, 3).astype(np.int32)
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landmarks_word = handpose[67:130].reshape(21, 3)
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gesture = gc.classify(landmarks_screen)
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cv.rectangle(display_screen, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)
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cv.putText(display_screen, handedness_text, (bbox[0], bbox[1] + 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
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cv.putText(display_screen, gesture, (bbox[0], bbox[1] + 30), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
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landmarks_xy = landmarks_screen[:, 0:2]
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draw_lines(display_screen, landmarks_xy, is_draw_point=False)
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for p in landmarks_screen:
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r = max(5 - p[2] // 5, 0)
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r = min(r, 14)
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cv.circle(display_screen, (p[0], p[1]), r, (0, 0, 255), -1)
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# Main view
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landmarks_xy = (landmarks_word[:, [0, 1]] * 1000 + 100).astype(np.int32)
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draw_lines(display_3d, landmarks_xy, thickness=5)
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# Top view
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landmarks_xz = landmarks_word[:, [0, 2]]
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landmarks_xz[:, 1] *= -1
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landmarks_xz = (landmarks_xz * 1000 + [300, 100]).astype(np.int32)
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draw_lines(display_3d, landmarks_xz, thickness=5)
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# Left view
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landmarks_yz = landmarks_word[:, [2, 1]]
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landmarks_yz[:, 0] *= -1
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landmarks_yz = (landmarks_yz * 1000 + [100, 300]).astype(np.int32)
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draw_lines(display_3d, landmarks_yz, thickness=5)
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# Right view
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landmarks_zy = (landmarks_word[:, [2, 1]] * 1000 + [300, 300]).astype(np.int32)
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draw_lines(display_3d, landmarks_zy, thickness=5)
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return display_screen, display_3d
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class GestureClassification:
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def _vector_2_angle(self, v1, v2):
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uv1 = v1 / np.linalg.norm(v1)
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uv2 = v2 / np.linalg.norm(v2)
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return np.degrees(np.arccos(np.dot(uv1, uv2)))
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def _hand_angle(self, hand):
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return [
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self._vector_2_angle(np.array([hand[0][0] - hand[i][0], hand[0][1] - hand[i][1]]),
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np.array([hand[i + 1][0] - hand[i + 2][0], hand[i + 1][1] - hand[i + 2][1]]))
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for i in [2, 6, 10, 14, 18]
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]
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def _finger_status(self, lmList):
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originx, originy = lmList[0]
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keypoint_list = [[5, 4], [6, 8], [10, 12], [14, 16], [18, 20]]
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return [np.hypot(x2 - originx, y2 - originy) > np.hypot(x1 - originx, y1 - originy)
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for (x1, y1), (x2, y2) in [(lmList[i], lmList[j]) for i, j in keypoint_list]]
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def _classify(self, hand):
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angle_list = self._hand_angle(hand)
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thumbOpen, firstOpen, secondOpen, thirdOpen, fourthOpen = self._finger_status(hand)
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thr_angle = 65.
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thr_angle_thumb = 30.
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thr_angle_s = 49.
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g = "Undefined"
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if angle_list[0] > thr_angle_thumb and all(a > thr_angle for a in angle_list[1:]) and not any([firstOpen, secondOpen, thirdOpen, fourthOpen]):
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g = "Zero"
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elif angle_list[0] > thr_angle_thumb and angle_list[1] < thr_angle_s and all(a > thr_angle for a in angle_list[2:]) and firstOpen and not any([secondOpen, thirdOpen, fourthOpen]):
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g = "One"
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elif angle_list[0] > thr_angle_thumb and angle_list[1] < thr_angle_s and angle_list[2] < thr_angle_s and all(a > thr_angle for a in angle_list[3:]) and not thumbOpen and firstOpen and secondOpen and not any([thirdOpen, fourthOpen]):
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g = "Two"
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elif angle_list[0] > thr_angle_thumb and all(a < thr_angle_s for a in angle_list[1:4]) and angle_list[4] > thr_angle and not thumbOpen and all([firstOpen, secondOpen, thirdOpen]) and not fourthOpen:
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g = "Three"
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elif angle_list[0] > thr_angle_thumb and all(a < thr_angle_s for a in angle_list[1:]) and all([firstOpen, secondOpen, thirdOpen, fourthOpen]):
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g = "Four"
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elif all(a < thr_angle_s for a in angle_list) and all([thumbOpen, firstOpen, secondOpen, thirdOpen, fourthOpen]):
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g = "Five"
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elif angle_list[0] < thr_angle_s and all(a > thr_angle for a in angle_list[1:4]) and angle_list[4] < thr_angle_s and thumbOpen and not any([firstOpen, secondOpen, thirdOpen]) and fourthOpen:
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g = "Six"
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elif angle_list[0] < thr_angle_s and angle_list[1] < thr_angle and all(a > thr_angle for a in angle_list[2:4]) and angle_list[4] > thr_angle_s and thumbOpen and firstOpen and not any([secondOpen, thirdOpen, fourthOpen]):
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g = "Seven"
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elif angle_list[0] < thr_angle_s and all(a < thr_angle for a in angle_list[1:3]) and angle_list[3] > thr_angle and angle_list[4] > thr_angle_s and thumbOpen and all([firstOpen, secondOpen]) and not any([thirdOpen, fourthOpen]):
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g = "Eight"
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elif angle_list[0] < thr_angle_s and all(a < thr_angle for a in angle_list[1:4]) and angle_list[4] > thr_angle_s and thumbOpen and all([firstOpen, secondOpen, thirdOpen]) and not fourthOpen:
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g = "Nine"
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return g
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def classify(self, landmarks):
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return self._classify(landmarks[:21, :2])
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def process_video(video_path):
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cap = cv.VideoCapture(video_path)
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fps = cap.get(cv.CAP_PROP_FPS)
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width = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
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out_path = tempfile.mktemp(suffix=".mp4")
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out_writer = cv.VideoWriter(out_path, cv.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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palms = palm_detector.infer(frame)
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hands = np.empty((0, 132))
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for palm in palms:
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handpose = handpose_detector.infer(frame, palm)
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if handpose is not None:
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hands = np.vstack((hands, handpose))
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frame, _ = visualize(frame, hands)
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out_writer.write(frame)
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cap.release()
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out_writer.release()
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return out_path
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gr.Interface(
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fn=process_video,
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inputs=gr.File(label="Upload Video", file_types=[".mp4", ".avi"]),
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outputs=gr.Video(label="Processed Video"),
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title="Video Edge Detection",
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allow_flagging="never"
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).launch()
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mp_handpose.py
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|
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|
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|
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|
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|
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|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
import cv2 as cv
|
| 3 |
+
|
| 4 |
+
class MPHandPose:
|
| 5 |
+
def __init__(self, modelPath, confThreshold=0.8, backendId=0, targetId=0):
|
| 6 |
+
self.model_path = modelPath
|
| 7 |
+
self.conf_threshold = confThreshold
|
| 8 |
+
self.backend_id = backendId
|
| 9 |
+
self.target_id = targetId
|
| 10 |
+
|
| 11 |
+
self.input_size = np.array([224, 224]) # wh
|
| 12 |
+
self.PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2]
|
| 13 |
+
self.PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0
|
| 14 |
+
self.PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2
|
| 15 |
+
self.PALM_BOX_PRE_SHIFT_VECTOR = [0, 0]
|
| 16 |
+
self.PALM_BOX_PRE_ENLARGE_FACTOR = 4
|
| 17 |
+
self.PALM_BOX_SHIFT_VECTOR = [0, -0.4]
|
| 18 |
+
self.PALM_BOX_ENLARGE_FACTOR = 3
|
| 19 |
+
self.HAND_BOX_SHIFT_VECTOR = [0, -0.1]
|
| 20 |
+
self.HAND_BOX_ENLARGE_FACTOR = 1.65
|
| 21 |
+
|
| 22 |
+
self.model = cv.dnn.readNet(self.model_path)
|
| 23 |
+
self.model.setPreferableBackend(self.backend_id)
|
| 24 |
+
self.model.setPreferableTarget(self.target_id)
|
| 25 |
+
|
| 26 |
+
@property
|
| 27 |
+
def name(self):
|
| 28 |
+
return self.__class__.__name__
|
| 29 |
+
|
| 30 |
+
def setBackendAndTarget(self, backendId, targetId):
|
| 31 |
+
self.backend_id = backendId
|
| 32 |
+
self.target_id = targetId
|
| 33 |
+
self.model.setPreferableBackend(self.backend_id)
|
| 34 |
+
self.model.setPreferableTarget(self.target_id)
|
| 35 |
+
|
| 36 |
+
def _cropAndPadFromPalm(self, image, palm_bbox, for_rotation = False):
|
| 37 |
+
# shift bounding box
|
| 38 |
+
wh_palm_bbox = palm_bbox[1] - palm_bbox[0]
|
| 39 |
+
if for_rotation:
|
| 40 |
+
shift_vector = self.PALM_BOX_PRE_SHIFT_VECTOR
|
| 41 |
+
else:
|
| 42 |
+
shift_vector = self.PALM_BOX_SHIFT_VECTOR
|
| 43 |
+
shift_vector = shift_vector * wh_palm_bbox
|
| 44 |
+
palm_bbox = palm_bbox + shift_vector
|
| 45 |
+
# enlarge bounding box
|
| 46 |
+
center_palm_bbox = np.sum(palm_bbox, axis=0) / 2
|
| 47 |
+
wh_palm_bbox = palm_bbox[1] - palm_bbox[0]
|
| 48 |
+
if for_rotation:
|
| 49 |
+
enlarge_scale = self.PALM_BOX_PRE_ENLARGE_FACTOR
|
| 50 |
+
else:
|
| 51 |
+
enlarge_scale = self.PALM_BOX_ENLARGE_FACTOR
|
| 52 |
+
new_half_size = wh_palm_bbox * enlarge_scale / 2
|
| 53 |
+
palm_bbox = np.array([
|
| 54 |
+
center_palm_bbox - new_half_size,
|
| 55 |
+
center_palm_bbox + new_half_size])
|
| 56 |
+
palm_bbox = palm_bbox.astype(np.int32)
|
| 57 |
+
palm_bbox[:, 0] = np.clip(palm_bbox[:, 0], 0, image.shape[1])
|
| 58 |
+
palm_bbox[:, 1] = np.clip(palm_bbox[:, 1], 0, image.shape[0])
|
| 59 |
+
# crop to the size of interest
|
| 60 |
+
image = image[palm_bbox[0][1]:palm_bbox[1][1], palm_bbox[0][0]:palm_bbox[1][0], :]
|
| 61 |
+
# pad to ensure conner pixels won't be cropped
|
| 62 |
+
if for_rotation:
|
| 63 |
+
side_len = np.linalg.norm(image.shape[:2])
|
| 64 |
+
else:
|
| 65 |
+
side_len = max(image.shape[:2])
|
| 66 |
+
|
| 67 |
+
side_len = int(side_len)
|
| 68 |
+
pad_h = side_len - image.shape[0]
|
| 69 |
+
pad_w = side_len - image.shape[1]
|
| 70 |
+
left = pad_w // 2
|
| 71 |
+
top = pad_h // 2
|
| 72 |
+
right = pad_w - left
|
| 73 |
+
bottom = pad_h - top
|
| 74 |
+
image = cv.copyMakeBorder(image, top, bottom, left, right, cv.BORDER_CONSTANT, None, (0, 0, 0))
|
| 75 |
+
bias = palm_bbox[0] - [left, top]
|
| 76 |
+
return image, palm_bbox, bias
|
| 77 |
+
|
| 78 |
+
def _preprocess(self, image, palm):
|
| 79 |
+
'''
|
| 80 |
+
Rotate input for inference.
|
| 81 |
+
Parameters:
|
| 82 |
+
image - input image of BGR channel order
|
| 83 |
+
palm_bbox - palm bounding box found in image of format [[x1, y1], [x2, y2]] (top-left and bottom-right points)
|
| 84 |
+
palm_landmarks - 7 landmarks (5 finger base points, 2 palm base points) of shape [7, 2]
|
| 85 |
+
Returns:
|
| 86 |
+
rotated_hand - rotated hand image for inference
|
| 87 |
+
rotate_palm_bbox - palm box of interest range
|
| 88 |
+
angle - rotate angle for hand
|
| 89 |
+
rotation_matrix - matrix for rotation and de-rotation
|
| 90 |
+
pad_bias - pad pixels of interest range
|
| 91 |
+
'''
|
| 92 |
+
# crop and pad image to interest range
|
| 93 |
+
pad_bias = np.array([0, 0], dtype=np.int32) # left, top
|
| 94 |
+
palm_bbox = palm[0:4].reshape(2, 2)
|
| 95 |
+
image, palm_bbox, bias = self._cropAndPadFromPalm(image, palm_bbox, True)
|
| 96 |
+
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
|
| 97 |
+
pad_bias += bias
|
| 98 |
+
|
| 99 |
+
# Rotate input to have vertically oriented hand image
|
| 100 |
+
# compute rotation
|
| 101 |
+
palm_bbox -= pad_bias
|
| 102 |
+
palm_landmarks = palm[4:18].reshape(7, 2) - pad_bias
|
| 103 |
+
p1 = palm_landmarks[self.PALM_LANDMARKS_INDEX_OF_PALM_BASE]
|
| 104 |
+
p2 = palm_landmarks[self.PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]
|
| 105 |
+
radians = np.pi / 2 - np.arctan2(-(p2[1] - p1[1]), p2[0] - p1[0])
|
| 106 |
+
radians = radians - 2 * np.pi * np.floor((radians + np.pi) / (2 * np.pi))
|
| 107 |
+
angle = np.rad2deg(radians)
|
| 108 |
+
# get bbox center
|
| 109 |
+
center_palm_bbox = np.sum(palm_bbox, axis=0) / 2
|
| 110 |
+
# get rotation matrix
|
| 111 |
+
rotation_matrix = cv.getRotationMatrix2D(center_palm_bbox, angle, 1.0)
|
| 112 |
+
# get rotated image
|
| 113 |
+
rotated_image = cv.warpAffine(image, rotation_matrix, (image.shape[1], image.shape[0]))
|
| 114 |
+
# get bounding boxes from rotated palm landmarks
|
| 115 |
+
homogeneous_coord = np.c_[palm_landmarks, np.ones(palm_landmarks.shape[0])]
|
| 116 |
+
rotated_palm_landmarks = np.array([
|
| 117 |
+
np.dot(homogeneous_coord, rotation_matrix[0]),
|
| 118 |
+
np.dot(homogeneous_coord, rotation_matrix[1])])
|
| 119 |
+
# get landmark bounding box
|
| 120 |
+
rotated_palm_bbox = np.array([
|
| 121 |
+
np.amin(rotated_palm_landmarks, axis=1),
|
| 122 |
+
np.amax(rotated_palm_landmarks, axis=1)]) # [top-left, bottom-right]
|
| 123 |
+
|
| 124 |
+
crop, rotated_palm_bbox, _ = self._cropAndPadFromPalm(rotated_image, rotated_palm_bbox)
|
| 125 |
+
blob = cv.resize(crop, dsize=self.input_size, interpolation=cv.INTER_AREA).astype(np.float32)
|
| 126 |
+
blob = blob / 255.
|
| 127 |
+
|
| 128 |
+
return blob[np.newaxis, :, :, :], rotated_palm_bbox, angle, rotation_matrix, pad_bias
|
| 129 |
+
|
| 130 |
+
def infer(self, image, palm):
|
| 131 |
+
# Preprocess
|
| 132 |
+
input_blob, rotated_palm_bbox, angle, rotation_matrix, pad_bias = self._preprocess(image, palm)
|
| 133 |
+
|
| 134 |
+
# Forward
|
| 135 |
+
self.model.setInput(input_blob)
|
| 136 |
+
output_blob = self.model.forward(self.model.getUnconnectedOutLayersNames())
|
| 137 |
+
|
| 138 |
+
# Postprocess
|
| 139 |
+
results = self._postprocess(output_blob, rotated_palm_bbox, angle, rotation_matrix, pad_bias)
|
| 140 |
+
return results # [bbox_coords, landmarks_coords, conf]
|
| 141 |
+
|
| 142 |
+
def _postprocess(self, blob, rotated_palm_bbox, angle, rotation_matrix, pad_bias):
|
| 143 |
+
landmarks, conf, handedness, landmarks_word = blob
|
| 144 |
+
|
| 145 |
+
conf = conf[0][0]
|
| 146 |
+
if conf < self.conf_threshold:
|
| 147 |
+
return None
|
| 148 |
+
|
| 149 |
+
landmarks = landmarks[0].reshape(-1, 3) # shape: (1, 63) -> (21, 3)
|
| 150 |
+
landmarks_word = landmarks_word[0].reshape(-1, 3) # shape: (1, 63) -> (21, 3)
|
| 151 |
+
|
| 152 |
+
# transform coords back to the input coords
|
| 153 |
+
wh_rotated_palm_bbox = rotated_palm_bbox[1] - rotated_palm_bbox[0]
|
| 154 |
+
scale_factor = wh_rotated_palm_bbox / self.input_size
|
| 155 |
+
landmarks[:, :2] = (landmarks[:, :2] - self.input_size / 2) * max(scale_factor)
|
| 156 |
+
landmarks[:, 2] = landmarks[:, 2] * max(scale_factor) # depth scaling
|
| 157 |
+
coords_rotation_matrix = cv.getRotationMatrix2D((0, 0), angle, 1.0)
|
| 158 |
+
rotated_landmarks = np.dot(landmarks[:, :2], coords_rotation_matrix[:, :2])
|
| 159 |
+
rotated_landmarks = np.c_[rotated_landmarks, landmarks[:, 2]]
|
| 160 |
+
rotated_landmarks_world = np.dot(landmarks_word[:, :2], coords_rotation_matrix[:, :2])
|
| 161 |
+
rotated_landmarks_world = np.c_[rotated_landmarks_world, landmarks_word[:, 2]]
|
| 162 |
+
# invert rotation
|
| 163 |
+
rotation_component = np.array([
|
| 164 |
+
[rotation_matrix[0][0], rotation_matrix[1][0]],
|
| 165 |
+
[rotation_matrix[0][1], rotation_matrix[1][1]]])
|
| 166 |
+
translation_component = np.array([
|
| 167 |
+
rotation_matrix[0][2], rotation_matrix[1][2]])
|
| 168 |
+
inverted_translation = np.array([
|
| 169 |
+
-np.dot(rotation_component[0], translation_component),
|
| 170 |
+
-np.dot(rotation_component[1], translation_component)])
|
| 171 |
+
inverse_rotation_matrix = np.c_[rotation_component, inverted_translation]
|
| 172 |
+
# get box center
|
| 173 |
+
center = np.append(np.sum(rotated_palm_bbox, axis=0) / 2, 1)
|
| 174 |
+
original_center = np.array([
|
| 175 |
+
np.dot(center, inverse_rotation_matrix[0]),
|
| 176 |
+
np.dot(center, inverse_rotation_matrix[1])])
|
| 177 |
+
landmarks[:, :2] = rotated_landmarks[:, :2] + original_center + pad_bias
|
| 178 |
+
|
| 179 |
+
# get bounding box from rotated_landmarks
|
| 180 |
+
bbox = np.array([
|
| 181 |
+
np.amin(landmarks[:, :2], axis=0),
|
| 182 |
+
np.amax(landmarks[:, :2], axis=0)]) # [top-left, bottom-right]
|
| 183 |
+
# shift bounding box
|
| 184 |
+
wh_bbox = bbox[1] - bbox[0]
|
| 185 |
+
shift_vector = self.HAND_BOX_SHIFT_VECTOR * wh_bbox
|
| 186 |
+
bbox = bbox + shift_vector
|
| 187 |
+
# enlarge bounding box
|
| 188 |
+
center_bbox = np.sum(bbox, axis=0) / 2
|
| 189 |
+
wh_bbox = bbox[1] - bbox[0]
|
| 190 |
+
new_half_size = wh_bbox * self.HAND_BOX_ENLARGE_FACTOR / 2
|
| 191 |
+
bbox = np.array([
|
| 192 |
+
center_bbox - new_half_size,
|
| 193 |
+
center_bbox + new_half_size])
|
| 194 |
+
|
| 195 |
+
# [0: 4]: hand bounding box found in image of format [x1, y1, x2, y2] (top-left and bottom-right points)
|
| 196 |
+
# [4: 67]: screen landmarks with format [x1, y1, z1, x2, y2 ... x21, y21, z21], z value is relative to WRIST
|
| 197 |
+
# [67: 130]: world landmarks with format [x1, y1, z1, x2, y2 ... x21, y21, z21], 3D metric x, y, z coordinate
|
| 198 |
+
# [130]: handedness, (left)[0, 1](right) hand
|
| 199 |
+
# [131]: confidence
|
| 200 |
+
return np.r_[bbox.reshape(-1), landmarks.reshape(-1), rotated_landmarks_world.reshape(-1), handedness[0][0], conf]
|
mp_palmdet.py
ADDED
|
@@ -0,0 +1,2121 @@
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|
| 1 |
+
import numpy as np
|
| 2 |
+
import cv2 as cv
|
| 3 |
+
|
| 4 |
+
class MPPalmDet:
|
| 5 |
+
def __init__(self, modelPath, nmsThreshold=0.3, scoreThreshold=0.5, topK=5000, backendId=0, targetId=0):
|
| 6 |
+
self.model_path = modelPath
|
| 7 |
+
self.nms_threshold = nmsThreshold
|
| 8 |
+
self.score_threshold = scoreThreshold
|
| 9 |
+
self.topK = topK
|
| 10 |
+
self.backend_id = backendId
|
| 11 |
+
self.target_id = targetId
|
| 12 |
+
|
| 13 |
+
self.input_size = np.array([192, 192]) # wh
|
| 14 |
+
|
| 15 |
+
self.model = cv.dnn.readNet(self.model_path)
|
| 16 |
+
self.model.setPreferableBackend(self.backend_id)
|
| 17 |
+
self.model.setPreferableTarget(self.target_id)
|
| 18 |
+
|
| 19 |
+
self.anchors = self._load_anchors()
|
| 20 |
+
|
| 21 |
+
@property
|
| 22 |
+
def name(self):
|
| 23 |
+
return self.__class__.__name__
|
| 24 |
+
|
| 25 |
+
def setBackendAndTarget(self, backendId, targetId):
|
| 26 |
+
self.backend_id = backendId
|
| 27 |
+
self.target_id = targetId
|
| 28 |
+
self.model.setPreferableBackend(self.backend_id)
|
| 29 |
+
self.model.setPreferableTarget(self.target_id)
|
| 30 |
+
|
| 31 |
+
def _preprocess(self, image):
|
| 32 |
+
pad_bias = np.array([0., 0.]) # left, top
|
| 33 |
+
ratio = min(self.input_size / image.shape[:2])
|
| 34 |
+
if image.shape[0] != self.input_size[0] or image.shape[1] != self.input_size[1]:
|
| 35 |
+
# keep aspect ratio when resize
|
| 36 |
+
ratio_size = (np.array(image.shape[:2]) * ratio).astype(np.int32)
|
| 37 |
+
image = cv.resize(image, (ratio_size[1], ratio_size[0]))
|
| 38 |
+
pad_h = self.input_size[0] - ratio_size[0]
|
| 39 |
+
pad_w = self.input_size[1] - ratio_size[1]
|
| 40 |
+
pad_bias[0] = left = pad_w // 2
|
| 41 |
+
pad_bias[1] = top = pad_h // 2
|
| 42 |
+
right = pad_w - left
|
| 43 |
+
bottom = pad_h - top
|
| 44 |
+
image = cv.copyMakeBorder(image, top, bottom, left, right, cv.BORDER_CONSTANT, None, (0, 0, 0))
|
| 45 |
+
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
|
| 46 |
+
image = image.astype(np.float32) / 255.0 # norm
|
| 47 |
+
pad_bias = (pad_bias / ratio).astype(np.int32)
|
| 48 |
+
return image[np.newaxis, :, :, :], pad_bias # hwc -> nhwc
|
| 49 |
+
|
| 50 |
+
def infer(self, image):
|
| 51 |
+
h, w, _ = image.shape
|
| 52 |
+
|
| 53 |
+
# Preprocess
|
| 54 |
+
input_blob, pad_bias = self._preprocess(image)
|
| 55 |
+
|
| 56 |
+
# Forward
|
| 57 |
+
self.model.setInput(input_blob)
|
| 58 |
+
output_blob = self.model.forward(self.model.getUnconnectedOutLayersNames())
|
| 59 |
+
|
| 60 |
+
# Postprocess
|
| 61 |
+
results = self._postprocess(output_blob, np.array([w, h]), pad_bias)
|
| 62 |
+
|
| 63 |
+
return results
|
| 64 |
+
|
| 65 |
+
def _postprocess(self, output_blob, original_shape, pad_bias):
|
| 66 |
+
score = output_blob[1][0, :, 0]
|
| 67 |
+
box_delta = output_blob[0][0, :, 0:4]
|
| 68 |
+
landmark_delta = output_blob[0][0, :, 4:]
|
| 69 |
+
scale = max(original_shape)
|
| 70 |
+
|
| 71 |
+
# get scores
|
| 72 |
+
score = score.astype(np.float64)
|
| 73 |
+
score = 1 / (1 + np.exp(-score))
|
| 74 |
+
|
| 75 |
+
# get boxes
|
| 76 |
+
cxy_delta = box_delta[:, :2] / self.input_size
|
| 77 |
+
wh_delta = box_delta[:, 2:] / self.input_size
|
| 78 |
+
xy1 = (cxy_delta - wh_delta / 2 + self.anchors) * scale
|
| 79 |
+
xy2 = (cxy_delta + wh_delta / 2 + self.anchors) * scale
|
| 80 |
+
boxes = np.concatenate([xy1, xy2], axis=1)
|
| 81 |
+
boxes -= [pad_bias[0], pad_bias[1], pad_bias[0], pad_bias[1]]
|
| 82 |
+
# NMS
|
| 83 |
+
keep_idx = cv.dnn.NMSBoxes(boxes, score, self.score_threshold, self.nms_threshold, top_k=self.topK)
|
| 84 |
+
if len(keep_idx) == 0:
|
| 85 |
+
return np.empty(shape=(0, 19))
|
| 86 |
+
selected_score = score[keep_idx]
|
| 87 |
+
selected_box = boxes[keep_idx]
|
| 88 |
+
|
| 89 |
+
# get landmarks
|
| 90 |
+
selected_landmarks = landmark_delta[keep_idx].reshape(-1, 7, 2)
|
| 91 |
+
selected_landmarks = selected_landmarks / self.input_size
|
| 92 |
+
selected_anchors = self.anchors[keep_idx]
|
| 93 |
+
for idx, landmark in enumerate(selected_landmarks):
|
| 94 |
+
landmark += selected_anchors[idx]
|
| 95 |
+
selected_landmarks *= scale
|
| 96 |
+
selected_landmarks -= pad_bias
|
| 97 |
+
|
| 98 |
+
# [
|
| 99 |
+
# [bbox_coords, landmarks_coords, score]
|
| 100 |
+
# ...
|
| 101 |
+
# [bbox_coords, landmarks_coords, score]
|
| 102 |
+
# ]
|
| 103 |
+
return np.c_[selected_box.reshape(-1, 4), selected_landmarks.reshape(-1, 14), selected_score.reshape(-1, 1)]
|
| 104 |
+
|
| 105 |
+
def _load_anchors(self):
|
| 106 |
+
return np.array([[0.02083333, 0.02083333],
|
| 107 |
+
[0.02083333, 0.02083333],
|
| 108 |
+
[0.0625, 0.02083333],
|
| 109 |
+
[0.0625, 0.02083333],
|
| 110 |
+
[0.10416666, 0.02083333],
|
| 111 |
+
[0.10416666, 0.02083333],
|
| 112 |
+
[0.14583333, 0.02083333],
|
| 113 |
+
[0.14583333, 0.02083333],
|
| 114 |
+
[0.1875, 0.02083333],
|
| 115 |
+
[0.1875, 0.02083333],
|
| 116 |
+
[0.22916667, 0.02083333],
|
| 117 |
+
[0.22916667, 0.02083333],
|
| 118 |
+
[0.27083334, 0.02083333],
|
| 119 |
+
[0.27083334, 0.02083333],
|
| 120 |
+
[0.3125, 0.02083333],
|
| 121 |
+
[0.3125, 0.02083333],
|
| 122 |
+
[0.35416666, 0.02083333],
|
| 123 |
+
[0.35416666, 0.02083333],
|
| 124 |
+
[0.39583334, 0.02083333],
|
| 125 |
+
[0.39583334, 0.02083333],
|
| 126 |
+
[0.4375, 0.02083333],
|
| 127 |
+
[0.4375, 0.02083333],
|
| 128 |
+
[0.47916666, 0.02083333],
|
| 129 |
+
[0.47916666, 0.02083333],
|
| 130 |
+
[0.5208333, 0.02083333],
|
| 131 |
+
[0.5208333, 0.02083333],
|
| 132 |
+
[0.5625, 0.02083333],
|
| 133 |
+
[0.5625, 0.02083333],
|
| 134 |
+
[0.6041667, 0.02083333],
|
| 135 |
+
[0.6041667, 0.02083333],
|
| 136 |
+
[0.6458333, 0.02083333],
|
| 137 |
+
[0.6458333, 0.02083333],
|
| 138 |
+
[0.6875, 0.02083333],
|
| 139 |
+
[0.6875, 0.02083333],
|
| 140 |
+
[0.7291667, 0.02083333],
|
| 141 |
+
[0.7291667, 0.02083333],
|
| 142 |
+
[0.7708333, 0.02083333],
|
| 143 |
+
[0.7708333, 0.02083333],
|
| 144 |
+
[0.8125, 0.02083333],
|
| 145 |
+
[0.8125, 0.02083333],
|
| 146 |
+
[0.8541667, 0.02083333],
|
| 147 |
+
[0.8541667, 0.02083333],
|
| 148 |
+
[0.8958333, 0.02083333],
|
| 149 |
+
[0.8958333, 0.02083333],
|
| 150 |
+
[0.9375, 0.02083333],
|
| 151 |
+
[0.9375, 0.02083333],
|
| 152 |
+
[0.9791667, 0.02083333],
|
| 153 |
+
[0.9791667, 0.02083333],
|
| 154 |
+
[0.02083333, 0.0625],
|
| 155 |
+
[0.02083333, 0.0625],
|
| 156 |
+
[0.0625, 0.0625],
|
| 157 |
+
[0.0625, 0.0625],
|
| 158 |
+
[0.10416666, 0.0625],
|
| 159 |
+
[0.10416666, 0.0625],
|
| 160 |
+
[0.14583333, 0.0625],
|
| 161 |
+
[0.14583333, 0.0625],
|
| 162 |
+
[0.1875, 0.0625],
|
| 163 |
+
[0.1875, 0.0625],
|
| 164 |
+
[0.22916667, 0.0625],
|
| 165 |
+
[0.22916667, 0.0625],
|
| 166 |
+
[0.27083334, 0.0625],
|
| 167 |
+
[0.27083334, 0.0625],
|
| 168 |
+
[0.3125, 0.0625],
|
| 169 |
+
[0.3125, 0.0625],
|
| 170 |
+
[0.35416666, 0.0625],
|
| 171 |
+
[0.35416666, 0.0625],
|
| 172 |
+
[0.39583334, 0.0625],
|
| 173 |
+
[0.39583334, 0.0625],
|
| 174 |
+
[0.4375, 0.0625],
|
| 175 |
+
[0.4375, 0.0625],
|
| 176 |
+
[0.47916666, 0.0625],
|
| 177 |
+
[0.47916666, 0.0625],
|
| 178 |
+
[0.5208333, 0.0625],
|
| 179 |
+
[0.5208333, 0.0625],
|
| 180 |
+
[0.5625, 0.0625],
|
| 181 |
+
[0.5625, 0.0625],
|
| 182 |
+
[0.6041667, 0.0625],
|
| 183 |
+
[0.6041667, 0.0625],
|
| 184 |
+
[0.6458333, 0.0625],
|
| 185 |
+
[0.6458333, 0.0625],
|
| 186 |
+
[0.6875, 0.0625],
|
| 187 |
+
[0.6875, 0.0625],
|
| 188 |
+
[0.7291667, 0.0625],
|
| 189 |
+
[0.7291667, 0.0625],
|
| 190 |
+
[0.7708333, 0.0625],
|
| 191 |
+
[0.7708333, 0.0625],
|
| 192 |
+
[0.8125, 0.0625],
|
| 193 |
+
[0.8125, 0.0625],
|
| 194 |
+
[0.8541667, 0.0625],
|
| 195 |
+
[0.8541667, 0.0625],
|
| 196 |
+
[0.8958333, 0.0625],
|
| 197 |
+
[0.8958333, 0.0625],
|
| 198 |
+
[0.9375, 0.0625],
|
| 199 |
+
[0.9375, 0.0625],
|
| 200 |
+
[0.9791667, 0.0625],
|
| 201 |
+
[0.9791667, 0.0625],
|
| 202 |
+
[0.02083333, 0.10416666],
|
| 203 |
+
[0.02083333, 0.10416666],
|
| 204 |
+
[0.0625, 0.10416666],
|
| 205 |
+
[0.0625, 0.10416666],
|
| 206 |
+
[0.10416666, 0.10416666],
|
| 207 |
+
[0.10416666, 0.10416666],
|
| 208 |
+
[0.14583333, 0.10416666],
|
| 209 |
+
[0.14583333, 0.10416666],
|
| 210 |
+
[0.1875, 0.10416666],
|
| 211 |
+
[0.1875, 0.10416666],
|
| 212 |
+
[0.22916667, 0.10416666],
|
| 213 |
+
[0.22916667, 0.10416666],
|
| 214 |
+
[0.27083334, 0.10416666],
|
| 215 |
+
[0.27083334, 0.10416666],
|
| 216 |
+
[0.3125, 0.10416666],
|
| 217 |
+
[0.3125, 0.10416666],
|
| 218 |
+
[0.35416666, 0.10416666],
|
| 219 |
+
[0.35416666, 0.10416666],
|
| 220 |
+
[0.39583334, 0.10416666],
|
| 221 |
+
[0.39583334, 0.10416666],
|
| 222 |
+
[0.4375, 0.10416666],
|
| 223 |
+
[0.4375, 0.10416666],
|
| 224 |
+
[0.47916666, 0.10416666],
|
| 225 |
+
[0.47916666, 0.10416666],
|
| 226 |
+
[0.5208333, 0.10416666],
|
| 227 |
+
[0.5208333, 0.10416666],
|
| 228 |
+
[0.5625, 0.10416666],
|
| 229 |
+
[0.5625, 0.10416666],
|
| 230 |
+
[0.6041667, 0.10416666],
|
| 231 |
+
[0.6041667, 0.10416666],
|
| 232 |
+
[0.6458333, 0.10416666],
|
| 233 |
+
[0.6458333, 0.10416666],
|
| 234 |
+
[0.6875, 0.10416666],
|
| 235 |
+
[0.6875, 0.10416666],
|
| 236 |
+
[0.7291667, 0.10416666],
|
| 237 |
+
[0.7291667, 0.10416666],
|
| 238 |
+
[0.7708333, 0.10416666],
|
| 239 |
+
[0.7708333, 0.10416666],
|
| 240 |
+
[0.8125, 0.10416666],
|
| 241 |
+
[0.8125, 0.10416666],
|
| 242 |
+
[0.8541667, 0.10416666],
|
| 243 |
+
[0.8541667, 0.10416666],
|
| 244 |
+
[0.8958333, 0.10416666],
|
| 245 |
+
[0.8958333, 0.10416666],
|
| 246 |
+
[0.9375, 0.10416666],
|
| 247 |
+
[0.9375, 0.10416666],
|
| 248 |
+
[0.9791667, 0.10416666],
|
| 249 |
+
[0.9791667, 0.10416666],
|
| 250 |
+
[0.02083333, 0.14583333],
|
| 251 |
+
[0.02083333, 0.14583333],
|
| 252 |
+
[0.0625, 0.14583333],
|
| 253 |
+
[0.0625, 0.14583333],
|
| 254 |
+
[0.10416666, 0.14583333],
|
| 255 |
+
[0.10416666, 0.14583333],
|
| 256 |
+
[0.14583333, 0.14583333],
|
| 257 |
+
[0.14583333, 0.14583333],
|
| 258 |
+
[0.1875, 0.14583333],
|
| 259 |
+
[0.1875, 0.14583333],
|
| 260 |
+
[0.22916667, 0.14583333],
|
| 261 |
+
[0.22916667, 0.14583333],
|
| 262 |
+
[0.27083334, 0.14583333],
|
| 263 |
+
[0.27083334, 0.14583333],
|
| 264 |
+
[0.3125, 0.14583333],
|
| 265 |
+
[0.3125, 0.14583333],
|
| 266 |
+
[0.35416666, 0.14583333],
|
| 267 |
+
[0.35416666, 0.14583333],
|
| 268 |
+
[0.39583334, 0.14583333],
|
| 269 |
+
[0.39583334, 0.14583333],
|
| 270 |
+
[0.4375, 0.14583333],
|
| 271 |
+
[0.4375, 0.14583333],
|
| 272 |
+
[0.47916666, 0.14583333],
|
| 273 |
+
[0.47916666, 0.14583333],
|
| 274 |
+
[0.5208333, 0.14583333],
|
| 275 |
+
[0.5208333, 0.14583333],
|
| 276 |
+
[0.5625, 0.14583333],
|
| 277 |
+
[0.5625, 0.14583333],
|
| 278 |
+
[0.6041667, 0.14583333],
|
| 279 |
+
[0.6041667, 0.14583333],
|
| 280 |
+
[0.6458333, 0.14583333],
|
| 281 |
+
[0.6458333, 0.14583333],
|
| 282 |
+
[0.6875, 0.14583333],
|
| 283 |
+
[0.6875, 0.14583333],
|
| 284 |
+
[0.7291667, 0.14583333],
|
| 285 |
+
[0.7291667, 0.14583333],
|
| 286 |
+
[0.7708333, 0.14583333],
|
| 287 |
+
[0.7708333, 0.14583333],
|
| 288 |
+
[0.8125, 0.14583333],
|
| 289 |
+
[0.8125, 0.14583333],
|
| 290 |
+
[0.8541667, 0.14583333],
|
| 291 |
+
[0.8541667, 0.14583333],
|
| 292 |
+
[0.8958333, 0.14583333],
|
| 293 |
+
[0.8958333, 0.14583333],
|
| 294 |
+
[0.9375, 0.14583333],
|
| 295 |
+
[0.9375, 0.14583333],
|
| 296 |
+
[0.9791667, 0.14583333],
|
| 297 |
+
[0.9791667, 0.14583333],
|
| 298 |
+
[0.02083333, 0.1875],
|
| 299 |
+
[0.02083333, 0.1875],
|
| 300 |
+
[0.0625, 0.1875],
|
| 301 |
+
[0.0625, 0.1875],
|
| 302 |
+
[0.10416666, 0.1875],
|
| 303 |
+
[0.10416666, 0.1875],
|
| 304 |
+
[0.14583333, 0.1875],
|
| 305 |
+
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| 1589 |
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| 1590 |
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| 1600 |
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| 1607 |
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| 1608 |
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| 1609 |
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| 1610 |
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| 1611 |
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| 1612 |
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| 1618 |
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| 1620 |
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| 1621 |
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| 1622 |
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| 1623 |
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| 1624 |
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| 1628 |
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| 1629 |
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|
| 1630 |
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| 1631 |
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| 1632 |
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| 1633 |
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| 1634 |
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| 1635 |
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| 1636 |
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| 1637 |
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| 1638 |
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| 1639 |
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| 1640 |
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|
| 1641 |
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|
| 1642 |
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|
| 1643 |
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|
| 1644 |
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|
| 1645 |
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|
| 1646 |
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|
| 1647 |
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|
| 1648 |
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| 1649 |
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|
| 1650 |
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|
| 1651 |
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|
| 1652 |
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|
| 1653 |
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|
| 1654 |
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|
| 1655 |
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|
| 1656 |
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|
| 1657 |
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|
| 1658 |
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|
| 1659 |
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|
| 1660 |
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|
| 1661 |
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|
| 1662 |
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|
| 1663 |
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|
| 1664 |
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|
| 1665 |
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|
| 1666 |
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|
| 1667 |
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|
| 1668 |
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|
| 1669 |
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|
| 1670 |
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|
| 1671 |
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|
| 1672 |
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|
| 1673 |
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|
| 1674 |
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|
| 1675 |
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|
| 1676 |
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|
| 1677 |
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|
| 1678 |
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|
| 1679 |
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|
| 1680 |
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|
| 1681 |
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|
| 1682 |
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|
| 1683 |
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|
| 1684 |
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|
| 1685 |
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|
| 1686 |
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|
| 1687 |
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|
| 1688 |
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|
| 1689 |
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|
| 1690 |
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| 1691 |
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|
| 1692 |
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|
| 1693 |
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|
| 1694 |
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|
| 1695 |
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|
| 1696 |
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|
| 1697 |
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|
| 1698 |
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|
| 1699 |
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|
| 1700 |
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|
| 1701 |
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|
| 1702 |
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|
| 1703 |
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|
| 1704 |
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|
| 1705 |
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|
| 1706 |
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|
| 1707 |
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[0.20833333, 0.5416667],
|
| 1708 |
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[0.29166666, 0.5416667],
|
| 1709 |
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|
| 1710 |
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|
| 1711 |
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|
| 1712 |
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|
| 1713 |
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[0.29166666, 0.5416667],
|
| 1714 |
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|
| 1715 |
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|
| 1716 |
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|
| 1717 |
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|
| 1718 |
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|
| 1719 |
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|
| 1720 |
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[0.45833334, 0.5416667],
|
| 1721 |
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|
| 1722 |
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[0.45833334, 0.5416667],
|
| 1723 |
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|
| 1724 |
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|
| 1725 |
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|
| 1726 |
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|
| 1727 |
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[0.5416667, 0.5416667],
|
| 1728 |
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|
| 1729 |
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[0.5416667, 0.5416667],
|
| 1730 |
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[0.5416667, 0.5416667],
|
| 1731 |
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|
| 1732 |
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|
| 1733 |
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|
| 1734 |
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|
| 1735 |
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|
| 1736 |
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|
| 1737 |
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|
| 1738 |
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|
| 1739 |
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|
| 1740 |
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|
| 1741 |
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|
| 1742 |
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|
| 1743 |
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|
| 1744 |
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|
| 1745 |
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|
| 1746 |
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|
| 1747 |
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[0.7916667, 0.5416667],
|
| 1748 |
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[0.7916667, 0.5416667],
|
| 1749 |
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|
| 1750 |
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|
| 1751 |
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[0.875, 0.5416667],
|
| 1752 |
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|
| 1753 |
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|
| 1754 |
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[0.875, 0.5416667],
|
| 1755 |
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[0.875, 0.5416667],
|
| 1756 |
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[0.9583333, 0.5416667],
|
| 1757 |
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[0.9583333, 0.5416667],
|
| 1758 |
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[0.9583333, 0.5416667],
|
| 1759 |
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[0.9583333, 0.5416667],
|
| 1760 |
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[0.9583333, 0.5416667],
|
| 1761 |
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[0.9583333, 0.5416667],
|
| 1762 |
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[0.04166667, 0.625],
|
| 1763 |
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[0.04166667, 0.625],
|
| 1764 |
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[0.04166667, 0.625],
|
| 1765 |
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[0.04166667, 0.625],
|
| 1766 |
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[0.04166667, 0.625],
|
| 1767 |
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[0.04166667, 0.625],
|
| 1768 |
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[0.125, 0.625],
|
| 1769 |
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[0.125, 0.625],
|
| 1770 |
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[0.125, 0.625],
|
| 1771 |
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[0.125, 0.625],
|
| 1772 |
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[0.125, 0.625],
|
| 1773 |
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[0.125, 0.625],
|
| 1774 |
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[0.20833333, 0.625],
|
| 1775 |
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[0.20833333, 0.625],
|
| 1776 |
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[0.20833333, 0.625],
|
| 1777 |
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[0.20833333, 0.625],
|
| 1778 |
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[0.20833333, 0.625],
|
| 1779 |
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[0.20833333, 0.625],
|
| 1780 |
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[0.29166666, 0.625],
|
| 1781 |
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[0.29166666, 0.625],
|
| 1782 |
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[0.29166666, 0.625],
|
| 1783 |
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[0.29166666, 0.625],
|
| 1784 |
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[0.29166666, 0.625],
|
| 1785 |
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[0.29166666, 0.625],
|
| 1786 |
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[0.375, 0.625],
|
| 1787 |
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[0.375, 0.625],
|
| 1788 |
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[0.375, 0.625],
|
| 1789 |
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[0.375, 0.625],
|
| 1790 |
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[0.375, 0.625],
|
| 1791 |
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[0.375, 0.625],
|
| 1792 |
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[0.45833334, 0.625],
|
| 1793 |
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[0.45833334, 0.625],
|
| 1794 |
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[0.45833334, 0.625],
|
| 1795 |
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[0.45833334, 0.625],
|
| 1796 |
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[0.45833334, 0.625],
|
| 1797 |
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[0.45833334, 0.625],
|
| 1798 |
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[0.5416667, 0.625],
|
| 1799 |
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[0.5416667, 0.625],
|
| 1800 |
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[0.5416667, 0.625],
|
| 1801 |
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[0.5416667, 0.625],
|
| 1802 |
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[0.5416667, 0.625],
|
| 1803 |
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[0.5416667, 0.625],
|
| 1804 |
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[0.625, 0.625],
|
| 1805 |
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[0.625, 0.625],
|
| 1806 |
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[0.625, 0.625],
|
| 1807 |
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[0.625, 0.625],
|
| 1808 |
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[0.625, 0.625],
|
| 1809 |
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[0.625, 0.625],
|
| 1810 |
+
[0.7083333, 0.625],
|
| 1811 |
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[0.7083333, 0.625],
|
| 1812 |
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[0.7083333, 0.625],
|
| 1813 |
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[0.7083333, 0.625],
|
| 1814 |
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[0.7083333, 0.625],
|
| 1815 |
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[0.7083333, 0.625],
|
| 1816 |
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[0.7916667, 0.625],
|
| 1817 |
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[0.7916667, 0.625],
|
| 1818 |
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[0.7916667, 0.625],
|
| 1819 |
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[0.7916667, 0.625],
|
| 1820 |
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[0.7916667, 0.625],
|
| 1821 |
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[0.7916667, 0.625],
|
| 1822 |
+
[0.875, 0.625],
|
| 1823 |
+
[0.875, 0.625],
|
| 1824 |
+
[0.875, 0.625],
|
| 1825 |
+
[0.875, 0.625],
|
| 1826 |
+
[0.875, 0.625],
|
| 1827 |
+
[0.875, 0.625],
|
| 1828 |
+
[0.9583333, 0.625],
|
| 1829 |
+
[0.9583333, 0.625],
|
| 1830 |
+
[0.9583333, 0.625],
|
| 1831 |
+
[0.9583333, 0.625],
|
| 1832 |
+
[0.9583333, 0.625],
|
| 1833 |
+
[0.9583333, 0.625],
|
| 1834 |
+
[0.04166667, 0.7083333],
|
| 1835 |
+
[0.04166667, 0.7083333],
|
| 1836 |
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[0.04166667, 0.7083333],
|
| 1837 |
+
[0.04166667, 0.7083333],
|
| 1838 |
+
[0.04166667, 0.7083333],
|
| 1839 |
+
[0.04166667, 0.7083333],
|
| 1840 |
+
[0.125, 0.7083333],
|
| 1841 |
+
[0.125, 0.7083333],
|
| 1842 |
+
[0.125, 0.7083333],
|
| 1843 |
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[0.125, 0.7083333],
|
| 1844 |
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[0.125, 0.7083333],
|
| 1845 |
+
[0.125, 0.7083333],
|
| 1846 |
+
[0.20833333, 0.7083333],
|
| 1847 |
+
[0.20833333, 0.7083333],
|
| 1848 |
+
[0.20833333, 0.7083333],
|
| 1849 |
+
[0.20833333, 0.7083333],
|
| 1850 |
+
[0.20833333, 0.7083333],
|
| 1851 |
+
[0.20833333, 0.7083333],
|
| 1852 |
+
[0.29166666, 0.7083333],
|
| 1853 |
+
[0.29166666, 0.7083333],
|
| 1854 |
+
[0.29166666, 0.7083333],
|
| 1855 |
+
[0.29166666, 0.7083333],
|
| 1856 |
+
[0.29166666, 0.7083333],
|
| 1857 |
+
[0.29166666, 0.7083333],
|
| 1858 |
+
[0.375, 0.7083333],
|
| 1859 |
+
[0.375, 0.7083333],
|
| 1860 |
+
[0.375, 0.7083333],
|
| 1861 |
+
[0.375, 0.7083333],
|
| 1862 |
+
[0.375, 0.7083333],
|
| 1863 |
+
[0.375, 0.7083333],
|
| 1864 |
+
[0.45833334, 0.7083333],
|
| 1865 |
+
[0.45833334, 0.7083333],
|
| 1866 |
+
[0.45833334, 0.7083333],
|
| 1867 |
+
[0.45833334, 0.7083333],
|
| 1868 |
+
[0.45833334, 0.7083333],
|
| 1869 |
+
[0.45833334, 0.7083333],
|
| 1870 |
+
[0.5416667, 0.7083333],
|
| 1871 |
+
[0.5416667, 0.7083333],
|
| 1872 |
+
[0.5416667, 0.7083333],
|
| 1873 |
+
[0.5416667, 0.7083333],
|
| 1874 |
+
[0.5416667, 0.7083333],
|
| 1875 |
+
[0.5416667, 0.7083333],
|
| 1876 |
+
[0.625, 0.7083333],
|
| 1877 |
+
[0.625, 0.7083333],
|
| 1878 |
+
[0.625, 0.7083333],
|
| 1879 |
+
[0.625, 0.7083333],
|
| 1880 |
+
[0.625, 0.7083333],
|
| 1881 |
+
[0.625, 0.7083333],
|
| 1882 |
+
[0.7083333, 0.7083333],
|
| 1883 |
+
[0.7083333, 0.7083333],
|
| 1884 |
+
[0.7083333, 0.7083333],
|
| 1885 |
+
[0.7083333, 0.7083333],
|
| 1886 |
+
[0.7083333, 0.7083333],
|
| 1887 |
+
[0.7083333, 0.7083333],
|
| 1888 |
+
[0.7916667, 0.7083333],
|
| 1889 |
+
[0.7916667, 0.7083333],
|
| 1890 |
+
[0.7916667, 0.7083333],
|
| 1891 |
+
[0.7916667, 0.7083333],
|
| 1892 |
+
[0.7916667, 0.7083333],
|
| 1893 |
+
[0.7916667, 0.7083333],
|
| 1894 |
+
[0.875, 0.7083333],
|
| 1895 |
+
[0.875, 0.7083333],
|
| 1896 |
+
[0.875, 0.7083333],
|
| 1897 |
+
[0.875, 0.7083333],
|
| 1898 |
+
[0.875, 0.7083333],
|
| 1899 |
+
[0.875, 0.7083333],
|
| 1900 |
+
[0.9583333, 0.7083333],
|
| 1901 |
+
[0.9583333, 0.7083333],
|
| 1902 |
+
[0.9583333, 0.7083333],
|
| 1903 |
+
[0.9583333, 0.7083333],
|
| 1904 |
+
[0.9583333, 0.7083333],
|
| 1905 |
+
[0.9583333, 0.7083333],
|
| 1906 |
+
[0.04166667, 0.7916667],
|
| 1907 |
+
[0.04166667, 0.7916667],
|
| 1908 |
+
[0.04166667, 0.7916667],
|
| 1909 |
+
[0.04166667, 0.7916667],
|
| 1910 |
+
[0.04166667, 0.7916667],
|
| 1911 |
+
[0.04166667, 0.7916667],
|
| 1912 |
+
[0.125, 0.7916667],
|
| 1913 |
+
[0.125, 0.7916667],
|
| 1914 |
+
[0.125, 0.7916667],
|
| 1915 |
+
[0.125, 0.7916667],
|
| 1916 |
+
[0.125, 0.7916667],
|
| 1917 |
+
[0.125, 0.7916667],
|
| 1918 |
+
[0.20833333, 0.7916667],
|
| 1919 |
+
[0.20833333, 0.7916667],
|
| 1920 |
+
[0.20833333, 0.7916667],
|
| 1921 |
+
[0.20833333, 0.7916667],
|
| 1922 |
+
[0.20833333, 0.7916667],
|
| 1923 |
+
[0.20833333, 0.7916667],
|
| 1924 |
+
[0.29166666, 0.7916667],
|
| 1925 |
+
[0.29166666, 0.7916667],
|
| 1926 |
+
[0.29166666, 0.7916667],
|
| 1927 |
+
[0.29166666, 0.7916667],
|
| 1928 |
+
[0.29166666, 0.7916667],
|
| 1929 |
+
[0.29166666, 0.7916667],
|
| 1930 |
+
[0.375, 0.7916667],
|
| 1931 |
+
[0.375, 0.7916667],
|
| 1932 |
+
[0.375, 0.7916667],
|
| 1933 |
+
[0.375, 0.7916667],
|
| 1934 |
+
[0.375, 0.7916667],
|
| 1935 |
+
[0.375, 0.7916667],
|
| 1936 |
+
[0.45833334, 0.7916667],
|
| 1937 |
+
[0.45833334, 0.7916667],
|
| 1938 |
+
[0.45833334, 0.7916667],
|
| 1939 |
+
[0.45833334, 0.7916667],
|
| 1940 |
+
[0.45833334, 0.7916667],
|
| 1941 |
+
[0.45833334, 0.7916667],
|
| 1942 |
+
[0.5416667, 0.7916667],
|
| 1943 |
+
[0.5416667, 0.7916667],
|
| 1944 |
+
[0.5416667, 0.7916667],
|
| 1945 |
+
[0.5416667, 0.7916667],
|
| 1946 |
+
[0.5416667, 0.7916667],
|
| 1947 |
+
[0.5416667, 0.7916667],
|
| 1948 |
+
[0.625, 0.7916667],
|
| 1949 |
+
[0.625, 0.7916667],
|
| 1950 |
+
[0.625, 0.7916667],
|
| 1951 |
+
[0.625, 0.7916667],
|
| 1952 |
+
[0.625, 0.7916667],
|
| 1953 |
+
[0.625, 0.7916667],
|
| 1954 |
+
[0.7083333, 0.7916667],
|
| 1955 |
+
[0.7083333, 0.7916667],
|
| 1956 |
+
[0.7083333, 0.7916667],
|
| 1957 |
+
[0.7083333, 0.7916667],
|
| 1958 |
+
[0.7083333, 0.7916667],
|
| 1959 |
+
[0.7083333, 0.7916667],
|
| 1960 |
+
[0.7916667, 0.7916667],
|
| 1961 |
+
[0.7916667, 0.7916667],
|
| 1962 |
+
[0.7916667, 0.7916667],
|
| 1963 |
+
[0.7916667, 0.7916667],
|
| 1964 |
+
[0.7916667, 0.7916667],
|
| 1965 |
+
[0.7916667, 0.7916667],
|
| 1966 |
+
[0.875, 0.7916667],
|
| 1967 |
+
[0.875, 0.7916667],
|
| 1968 |
+
[0.875, 0.7916667],
|
| 1969 |
+
[0.875, 0.7916667],
|
| 1970 |
+
[0.875, 0.7916667],
|
| 1971 |
+
[0.875, 0.7916667],
|
| 1972 |
+
[0.9583333, 0.7916667],
|
| 1973 |
+
[0.9583333, 0.7916667],
|
| 1974 |
+
[0.9583333, 0.7916667],
|
| 1975 |
+
[0.9583333, 0.7916667],
|
| 1976 |
+
[0.9583333, 0.7916667],
|
| 1977 |
+
[0.9583333, 0.7916667],
|
| 1978 |
+
[0.04166667, 0.875],
|
| 1979 |
+
[0.04166667, 0.875],
|
| 1980 |
+
[0.04166667, 0.875],
|
| 1981 |
+
[0.04166667, 0.875],
|
| 1982 |
+
[0.04166667, 0.875],
|
| 1983 |
+
[0.04166667, 0.875],
|
| 1984 |
+
[0.125, 0.875],
|
| 1985 |
+
[0.125, 0.875],
|
| 1986 |
+
[0.125, 0.875],
|
| 1987 |
+
[0.125, 0.875],
|
| 1988 |
+
[0.125, 0.875],
|
| 1989 |
+
[0.125, 0.875],
|
| 1990 |
+
[0.20833333, 0.875],
|
| 1991 |
+
[0.20833333, 0.875],
|
| 1992 |
+
[0.20833333, 0.875],
|
| 1993 |
+
[0.20833333, 0.875],
|
| 1994 |
+
[0.20833333, 0.875],
|
| 1995 |
+
[0.20833333, 0.875],
|
| 1996 |
+
[0.29166666, 0.875],
|
| 1997 |
+
[0.29166666, 0.875],
|
| 1998 |
+
[0.29166666, 0.875],
|
| 1999 |
+
[0.29166666, 0.875],
|
| 2000 |
+
[0.29166666, 0.875],
|
| 2001 |
+
[0.29166666, 0.875],
|
| 2002 |
+
[0.375, 0.875],
|
| 2003 |
+
[0.375, 0.875],
|
| 2004 |
+
[0.375, 0.875],
|
| 2005 |
+
[0.375, 0.875],
|
| 2006 |
+
[0.375, 0.875],
|
| 2007 |
+
[0.375, 0.875],
|
| 2008 |
+
[0.45833334, 0.875],
|
| 2009 |
+
[0.45833334, 0.875],
|
| 2010 |
+
[0.45833334, 0.875],
|
| 2011 |
+
[0.45833334, 0.875],
|
| 2012 |
+
[0.45833334, 0.875],
|
| 2013 |
+
[0.45833334, 0.875],
|
| 2014 |
+
[0.5416667, 0.875],
|
| 2015 |
+
[0.5416667, 0.875],
|
| 2016 |
+
[0.5416667, 0.875],
|
| 2017 |
+
[0.5416667, 0.875],
|
| 2018 |
+
[0.5416667, 0.875],
|
| 2019 |
+
[0.5416667, 0.875],
|
| 2020 |
+
[0.625, 0.875],
|
| 2021 |
+
[0.625, 0.875],
|
| 2022 |
+
[0.625, 0.875],
|
| 2023 |
+
[0.625, 0.875],
|
| 2024 |
+
[0.625, 0.875],
|
| 2025 |
+
[0.625, 0.875],
|
| 2026 |
+
[0.7083333, 0.875],
|
| 2027 |
+
[0.7083333, 0.875],
|
| 2028 |
+
[0.7083333, 0.875],
|
| 2029 |
+
[0.7083333, 0.875],
|
| 2030 |
+
[0.7083333, 0.875],
|
| 2031 |
+
[0.7083333, 0.875],
|
| 2032 |
+
[0.7916667, 0.875],
|
| 2033 |
+
[0.7916667, 0.875],
|
| 2034 |
+
[0.7916667, 0.875],
|
| 2035 |
+
[0.7916667, 0.875],
|
| 2036 |
+
[0.7916667, 0.875],
|
| 2037 |
+
[0.7916667, 0.875],
|
| 2038 |
+
[0.875, 0.875],
|
| 2039 |
+
[0.875, 0.875],
|
| 2040 |
+
[0.875, 0.875],
|
| 2041 |
+
[0.875, 0.875],
|
| 2042 |
+
[0.875, 0.875],
|
| 2043 |
+
[0.875, 0.875],
|
| 2044 |
+
[0.9583333, 0.875],
|
| 2045 |
+
[0.9583333, 0.875],
|
| 2046 |
+
[0.9583333, 0.875],
|
| 2047 |
+
[0.9583333, 0.875],
|
| 2048 |
+
[0.9583333, 0.875],
|
| 2049 |
+
[0.9583333, 0.875],
|
| 2050 |
+
[0.04166667, 0.9583333],
|
| 2051 |
+
[0.04166667, 0.9583333],
|
| 2052 |
+
[0.04166667, 0.9583333],
|
| 2053 |
+
[0.04166667, 0.9583333],
|
| 2054 |
+
[0.04166667, 0.9583333],
|
| 2055 |
+
[0.04166667, 0.9583333],
|
| 2056 |
+
[0.125, 0.9583333],
|
| 2057 |
+
[0.125, 0.9583333],
|
| 2058 |
+
[0.125, 0.9583333],
|
| 2059 |
+
[0.125, 0.9583333],
|
| 2060 |
+
[0.125, 0.9583333],
|
| 2061 |
+
[0.125, 0.9583333],
|
| 2062 |
+
[0.20833333, 0.9583333],
|
| 2063 |
+
[0.20833333, 0.9583333],
|
| 2064 |
+
[0.20833333, 0.9583333],
|
| 2065 |
+
[0.20833333, 0.9583333],
|
| 2066 |
+
[0.20833333, 0.9583333],
|
| 2067 |
+
[0.20833333, 0.9583333],
|
| 2068 |
+
[0.29166666, 0.9583333],
|
| 2069 |
+
[0.29166666, 0.9583333],
|
| 2070 |
+
[0.29166666, 0.9583333],
|
| 2071 |
+
[0.29166666, 0.9583333],
|
| 2072 |
+
[0.29166666, 0.9583333],
|
| 2073 |
+
[0.29166666, 0.9583333],
|
| 2074 |
+
[0.375, 0.9583333],
|
| 2075 |
+
[0.375, 0.9583333],
|
| 2076 |
+
[0.375, 0.9583333],
|
| 2077 |
+
[0.375, 0.9583333],
|
| 2078 |
+
[0.375, 0.9583333],
|
| 2079 |
+
[0.375, 0.9583333],
|
| 2080 |
+
[0.45833334, 0.9583333],
|
| 2081 |
+
[0.45833334, 0.9583333],
|
| 2082 |
+
[0.45833334, 0.9583333],
|
| 2083 |
+
[0.45833334, 0.9583333],
|
| 2084 |
+
[0.45833334, 0.9583333],
|
| 2085 |
+
[0.45833334, 0.9583333],
|
| 2086 |
+
[0.5416667, 0.9583333],
|
| 2087 |
+
[0.5416667, 0.9583333],
|
| 2088 |
+
[0.5416667, 0.9583333],
|
| 2089 |
+
[0.5416667, 0.9583333],
|
| 2090 |
+
[0.5416667, 0.9583333],
|
| 2091 |
+
[0.5416667, 0.9583333],
|
| 2092 |
+
[0.625, 0.9583333],
|
| 2093 |
+
[0.625, 0.9583333],
|
| 2094 |
+
[0.625, 0.9583333],
|
| 2095 |
+
[0.625, 0.9583333],
|
| 2096 |
+
[0.625, 0.9583333],
|
| 2097 |
+
[0.625, 0.9583333],
|
| 2098 |
+
[0.7083333, 0.9583333],
|
| 2099 |
+
[0.7083333, 0.9583333],
|
| 2100 |
+
[0.7083333, 0.9583333],
|
| 2101 |
+
[0.7083333, 0.9583333],
|
| 2102 |
+
[0.7083333, 0.9583333],
|
| 2103 |
+
[0.7083333, 0.9583333],
|
| 2104 |
+
[0.7916667, 0.9583333],
|
| 2105 |
+
[0.7916667, 0.9583333],
|
| 2106 |
+
[0.7916667, 0.9583333],
|
| 2107 |
+
[0.7916667, 0.9583333],
|
| 2108 |
+
[0.7916667, 0.9583333],
|
| 2109 |
+
[0.7916667, 0.9583333],
|
| 2110 |
+
[0.875, 0.9583333],
|
| 2111 |
+
[0.875, 0.9583333],
|
| 2112 |
+
[0.875, 0.9583333],
|
| 2113 |
+
[0.875, 0.9583333],
|
| 2114 |
+
[0.875, 0.9583333],
|
| 2115 |
+
[0.875, 0.9583333],
|
| 2116 |
+
[0.9583333, 0.9583333],
|
| 2117 |
+
[0.9583333, 0.9583333],
|
| 2118 |
+
[0.9583333, 0.9583333],
|
| 2119 |
+
[0.9583333, 0.9583333],
|
| 2120 |
+
[0.9583333, 0.9583333],
|
| 2121 |
+
[0.9583333, 0.9583333]], dtype=np.float32)
|
requirements.txt
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
opencv-python
|
| 2 |
+
gradio
|
| 3 |
+
numpy
|
| 4 |
+
huggingface_hub
|