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test.py

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+import glob
+import cv2
+import pickle 
+import math
+import random
+import datetime
+import numpy as np
+import statistics
+from skimage import transform
+import traceback
+import argparse
+
+from rectification import *
+from Speed_estimate import SpeedEstimation
+
+
+def calibration_image_median(data):
+    # real data (m)
+    real_width_car= 2
+    real_length_car= 4
+
+    real_width_motor= 0.5
+    real_length_motor=2
+
+    real_width_person=0.5
+    real_height_person=0.5
+
+    width_pixel_car=[]
+    length_pixel_car=[]
+
+    width_pixel_motor=[]
+    length_pixel_motor=[]
+
+    width_pixel_person=[]
+    height_pixel_person=[]
+
+    for obj in data:
+        try:
+            if data[obj]['label'] == 'car':
+                for box in data[obj]['boxes']:
+                    width_pixel_car.append(min(box[2]-box[0],box[3]-box[1]))
+                    length_pixel_car.append(max(box[2]-box[0],box[3]-box[1]))
+
+            if data[obj]['label'] == 'motor':
+                for box in data[obj]['boxes']:
+                    width_pixel_motor.append(min(box[2]-box[0],box[3]-box[1]))
+                    length_pixel_motor.append(max(box[2]-box[0],box[3]-box[1]))
+
+            if data[obj]['label'] == 'pedestrian':
+                for box in data[obj]['boxes']:
+                    width_pixel_person.append(min(box[2]-box[0],box[3]-box[1]))
+                    height_pixel_person.append(max(box[2]-box[0],box[3]-box[1]))
+        except:
+            continue
+
+    if len(width_pixel_car) !=0:
+        median_x = statistics.mean(width_pixel_car)
+        median_y = statistics.mean(length_pixel_car)
+        return real_width_car/median_x, real_length_car/median_y
+
+    elif len(width_pixel_motor) !=0:
+        median_x = statistics.mean(width_pixel_motor)
+        median_y = statistics.mean(length_pixel_motor)
+        return real_width_motor/median_x, real_length_motor/median_y
+    else:
+        median_x = statistics.mean(width_pixel_person)
+        median_y = statistics.mean(height_pixel_person)
+        return real_width_person/median_x, real_height_person/median_y
+
+def main():
+    parse = argparse.ArgumentParser()
+    parse.add_argument("--videopath", help=" path of video mp4 ", default="car_hightway.mp4")
+    parse.add_argument("--resultpath", help=" path of result video mp4 ", default="car_hightway_speed.mp4")
+    parse.add_argument("--pklpath", help=" path of pkl file", default="car_hightway.pkl" )
+    parse.add_argument("--widthreal", help= "real width",default=150)
+    parse.add_argument("--heightreal", help="real height",default=70)
+
+    agrs= parse.parse_args()
+
+    with open(agrs.pklpath,"rb") as f:
+        data= pickle.load(f)
+
+    cap= cv2.VideoCapture(agrs.videopath)
+
+    frame_width = int(cap.get(3))
+    frame_height = int(cap.get(4))
+    size = (frame_width, frame_height)
+
+    result = cv2.VideoWriter(agrs.resultpath,cv2.VideoWriter_fourcc(*'MJPG'),30,size)
+
+
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    _,img_frame = cap.read()
+    H, max_x,max_y = matrix_homorgenous(img_frame)
+
+    # estimate x,y in real 
+    x= agrs.widthreal
+    y= agrs.heightreal
+
+    # coeffient width, height for image plane
+    coef_x=x/max_x
+    coef_y=y/max_y
+
+
+
+    # "12.mkv"
+    # coef_x,coef_y = 50/3000,50/1064
+    # "video_cut"
+    # coef_x,coef_y = 70/1022,50/1202 
+    # "car_highway"
+    # coef_x,coef_y = 150/1076,70/1324
+    # "town"
+    # coef_x,coef_y = 30/7680,70/7680 
+
+    while True:
+        ret,frame= cap.read()
+        if not ret:
+            break
+        if ret:
+            for obj in data:
+                try:
+                    for ind,fr in enumerate(data[obj]['frames']):
+                        if fr == cap.get(cv2.CAP_PROP_POS_FRAMES) :
+                            x_min_i,y_min_i,x_max_i,y_max_i = data[obj]['boxes'][ind]
+                            loc_after = data[obj]['boxes'][ind]
+                            loc_before = data[obj]['boxes'][ind-1]
+                            speed= SpeedEstimation(loc_before, loc_after, 2 , 4, H, fps, coef_x,coef_y).compute_speed()
+                            cv2.rectangle(frame, (x_min_i,y_min_i), (x_max_i,y_max_i), (0,0,255))
+                            cv2.putText(frame,"{}km/h".format(int(speed)),(x_min_i-2,y_min_i-2),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2,cv2.LINE_AA)
+                except:
+                    traceback.print_exc()
+            # result.write(frame)
+            cv2.imshow("Image",cv2.resize(frame,(1500,1000)))
+            if cv2.waitKey(20) & 0xFF == ord("q"):
+                break
+    cap.release()
+    cv2.destroyAllWindows()
+
+if __name__=="__main__":
+    main()