File size: 3,472 Bytes
7fd1105 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
import cv2
import numpy as np
import requests
from scipy.interpolate import splprep, splev
# Camera setup (replace with your camera indices or IP streams)
caps = [cv2.VideoCapture(0)] # Add more cameras as needed
def smooth_trajectory(points):
if len(points) < 3:
return points
x = [p["x"] for p in points]
y = [p["y"] for p in points]
tck, u = splprep([x, y], s=0)
u_new = np.linspace(0, 1, 50)
x_new, y_new = splev(u_new, tck)
return [{"x": x, "y": y} for x, y in zip(x_new, y_new)]
def process_frame(frame):
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, (0, 120, 70), (10, 255, 255)) # Adjust for your ball color
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours:
c = max(contours, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(c)
return x + w / 2, y + h / 2
return None, None
actual_path = []
y_positions = []
pitching_detected = False
impact_detected = False
last_point = None
frame_count = 0
spin = 0
while True:
frames = []
for cap in caps:
ret, frame = cap.read()
if ret:
frames.append(frame)
if not frames:
break
# Process the first camera feed (add logic for multiple cameras)
frame = frames[0]
center_x, center_y = process_frame(frame)
if center_x is not None:
norm_x = center_x / 1280
norm_y = center_y / 720
current_point = (norm_x, norm_y)
if last_point != current_point:
actual_path.append({"x": norm_x, "y": norm_y})
y_positions.append(norm_y)
last_point = current_point
if len(y_positions) > 2 and not pitching_detected:
if y_positions[-1] < y_positions[-2] and y_positions[-2] < y_positions[-3]:
pitching_detected = True
pitching_x = actual_path[-2]["x"]
pitching_y = actual_path[-2]["y"]
if len(actual_path) > 2 and not impact_detected:
speed_current = abs(y_positions[-1] - y_positions[-2])
speed_prev = abs(y_positions[-2] - y_positions[-3])
if speed_current < speed_prev * 0.3:
impact_detected = True
impact_x = actual_path[-1]["x"]
impact_y = actual_path[-1]["y"]
frame_count += 1
if impact_detected or frame_count > 50:
break
cv2.imshow('Frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
for cap in caps:
cap.release()
cv2.destroyAllWindows()
if not actual_path:
print("No ball detected")
exit()
if not pitching_detected:
pitching_x = actual_path[len(actual_path)//2]["x"]
pitching_y = actual_path[len(actual_path)//2]["y"]
if not impact_detected:
impact_x = actual_path[-1]["x"]
impact_y = actual_path[-1]["y"]
actual_path = smooth_trajectory(actual_path)
projected_path = [
{"x": impact_x, "y": impact_y},
{"x": impact_x + spin * 0.1, "y": 1.0}
]
# Send data to Hugging Face app
data = {
'actual_path': actual_path,
'projected_path': projected_path,
'pitching': {'x': pitching_x, 'y': pitching_y},
'impact': {'x': impact_x, 'y': impact_y},
'speed': frame_count / 30 * 0.5, # Rough speed estimate
'spin': spin
}
# Replace with your Hugging Face Space URL
response = requests.post('https://your-username-cricket-lbw-analyzer.hf.space/analyze_data', json=data)
print(response.json()) |