mirror of
https://github.com/NohamR/Stage-2023.git
synced 2025-05-24 14:21:58 +00:00
142 lines
4.3 KiB
Python
142 lines
4.3 KiB
Python
import pandas as pd
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import numpy as np
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import cv2
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import matplotlib.pyplot as plt
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from scipy.optimize import least_squares
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import os
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# from imageio import imread
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import cameratransform as ct
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cap = cv2.VideoCapture('cams/new/cut2.mp4')
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folder_path = "traitement/expgood/labels/"
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name = 'cut2'
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fps = 780
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allfiles = []
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for i in range(1, fps+1):
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allfiles.append(folder_path + name + '_' + str(i) + '.txt')
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try:
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os.remove('traitement/distance.txt')
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except:
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pass
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# Set the desired dimensions for displaying the video
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display_width = 1280
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display_height = 720
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display_width = 1920
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display_height = 1080
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width = 1920
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height = 1080
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frame_nb = 0
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bleu = (255, 0, 0)
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vert = (0, 255, 0)
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a = 2.736
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b = -51.49521
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x = [i for i in range(-10000, 100000)]
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y = [a * xi + b for xi in x]
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# # Cam part
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# img = cv2.imread("track/Sylvain/stage_Noham/stage_Noham/image_vide_pts.png")
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# nh,nw,_ = img.shape
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res = np.array([ 3.99594676, 3.53413555, 4.55 , 16.41739973, 74.96395791, 49.11271189, 2.79384615])
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image_size = (width,height)
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cam = ct.Camera(ct.RectilinearProjection(focallength_mm=res[0], sensor=(res[1],res[2]), image=image_size),
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ct.SpatialOrientation(elevation_m=res[3], tilt_deg=res[4], heading_deg = res[5], roll_deg = res[6] ) )
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if not cap.isOpened():
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print("Error opening video stream or file")
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while cap.isOpened():
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ret, frame = cap.read()
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if ret:
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df = pd.read_csv(allfiles[frame_nb], header=None, sep=' ')
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px_ground_pts = []
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for index, row in df.iterrows():
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class_id, center_x, center_y, bbox_width, bbox_height, object_id = row
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center_x = int(center_x * width)
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center_y = int(center_y * height)
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bbox_width = int(bbox_width * width)
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bbox_height = int(bbox_height * height)
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top_left_x = int(center_x - bbox_width / 2)
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top_left_y = int(center_y - bbox_height / 2)
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bottom_right_x = int(center_x + bbox_width / 2)
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bottom_right_y = int(center_y + bbox_height / 2)
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# (19;112) à (636;714) et (86;86) à (1087;715)
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if (((112-714)/(19-636)) * top_left_x + 112 - ((112-714)/(19-636)) *19 > top_left_y ) and (((86-715)/(86-1097)) * bottom_right_x + 112 - ((86-715)/(86-1097)) *86 < bottom_right_y ):
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label = f'Class: {int(class_id)}, Object ID: {int(object_id)}'
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cv2.putText(frame, label, (top_left_x, top_left_y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, vert, 1)
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# obetnir le centre du rectangle
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center_x = (top_left_x + bottom_right_x) // 2
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center_y = (top_left_y + bottom_right_y) // 2
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cv2.circle(frame, (center_x, center_y), 5, vert, -1)
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px_ground_pts += [[center_x, center_y]]
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else :
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pass
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# print('px_ground_pts: ', px_ground_pts)
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# print('np.arraypx_ground_pts: ', np.array(px_ground_pts))
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space_pts = []
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for pt in np.array(px_ground_pts):
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space_pts.append(cam.spaceFromImage(pt))
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space_pts = np.array(space_pts)
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# print('space_pts: ', space_pts)
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Xb = 26
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Yb = a * Xb + b
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Xv = 1
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Yv = a
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BH = ((space_pts[:,0] - Xb) * Xv) + ((space_pts[:,1] - Yb) * Yv) / np.sqrt( (Xv**2) + (Yv ** 2) )
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# print(BH.tolist())
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with open("traitement/distance.txt", 'a', encoding='utf-8') as file:
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file.write('\n' + str(BH.tolist()))
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# resized_frame = cv2.resize(frame, (display_width, display_height))
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# cv2.imshow('Frame', resized_frame)
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# plt.figure()
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#######################
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# plt.figure(1, figsize=[16, 9])
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# plt.scatter(space_pts[:,0], space_pts[:,1], color="red", s=2)
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# # plt.plot([28.569, 51.681],[26.665, 89.904], color='blue', linestyle='-', linewidth=1)
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# plt.plot(x, y, label=f"y = {a}x + {b}", color="black")
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# plt.scatter(Xb, Yb, color="black", s=20)
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# plt.title(frame_nb)
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# plt.legend()
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# # plt.axis("equal")
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# plt.xlim([0, 100])
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# plt.ylim([0, 150])
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# plt.draw()
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# plt.pause(0.0000000000001)
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# plt.clf()
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######################
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if cv2.waitKey(25) & 0xFF == ord('q'):break
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frame_nb = frame_nb + 1
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else:break
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cap.release()
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cv2.destroyAllWindows() |