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Apr 17, 2019 . 1 changed file with 25 additions and 46 deletions.There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,18 +1,19 @@ import cv2, numpy as np, csv #https://github.com/opencv/opencv/blob/master/samples/dnn/openpose.py outfile_path = '/home/stephen/Desktop/workout.csv' protoFile = "/home/stephen/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt" weightsFile = "/home/stephen/pose/mpi/pose_iter_160000.caffemodel" net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile) data, input_width, input_height, threshold, frame_number = [], 368, 386, 0.1, 0 input_source = "/home/stephen/Desktop/workout.MP4" cap = cv2.VideoCapture(input_source) # use the previous location of the body part if the model is wrong previous_x, previous_y = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] while True: @@ -33,7 +34,7 @@ output_width, output_height = output.shape[2], output.shape[3] # Empty list to store the detected keypoints x_data, y_data = [], [] # Iterate through the body parts for i in range(15): @@ -45,52 +46,30 @@ x, y = (img_width * point[0]) / output_width, (img_height * point[1]) / output_height # Is the point likely to be correct? if prob > threshold: x_data.append(x) y_data.append(y) xy = tuple(np.array([x,y], int)) cv2.circle(img, xy, 5, (25,0,255), 5) # No? us the location in the previous frame else: x_data.append(previous_x[i]) y_data.append(previous_y[i]) # add these points to the list of data data.append(x_data + y_data) previous_x, previous_y = x_data, y_data frame_number+=1 # use this break statement to check your data before processing the whole video #if frame_number == 300: break print(frame_number) cv2.imshow('img', img) k = cv2.waitKey(1) if k == 27: break # write the data to a .csv file import pandas as pd df = pd.DataFrame(data) df.to_csv(outfile_path, index = False) print('save complete') -
Sep 21, 2018 .There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,96 @@ import cv2, numpy as np, csv #https://github.com/opencv/opencv/blob/master/samples/dnn/openpose.py protoFile = "/home/stephen/Desktop/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt" weightsFile = "/home/stephen/Desktop/pose/mpi/pose_iter_160000.caffemodel" net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile) data, input_width, input_height, threshold, frame_number = [], 368, 386, 0.1, 0 input_source = "/home/stephen/Desktop/me.MP4" cap = cv2.VideoCapture(input_source) # use the previous location of the body part if the model is wrong previous_points = [(0,0),(0,0),(0,0),(0,0),(0,0),(0,0),(0,0),(0,0),(0,0),(0,0),(0,0),(0,0),(0,0),(0,0)] while True: ret, img = cap.read() if not ret: break # get the image shape img_width, img_height = img.shape[1], img.shape[0] # get a blob from the image inputBlob = cv2.dnn.blobFromImage(img, 1.0 / 255, (input_width, input_height),(0, 0, 0), swapRB=False, crop=False) # set the input and perform a forward pass net.setInput(inputBlob) output = net.forward() # get the output shape output_width, output_height = output.shape[2], output.shape[3] # Empty list to store the detected keypoints points = [] # Iterate through the body parts for i in range(15): # find probability that point is correct _, prob, _, point = cv2.minMaxLoc(output[0, i, :, :]) # Scale the point to fit on the original image x, y = (img_width * point[0]) / output_width, (img_height * point[1]) / output_height # Is the point likely to be correct? if prob > threshold: points.append((int(x), int(y))) # No? us the location in the previous frame else: points.append(previous_points[i]) # add these points to the list of data data.append(points) previous_points = points frame_number+=1 # use this break statement to check your data before processing the whole video if frame_number == 10: break print(frame_number) # write the data to a .csv file with open('/home/stephen/Desktop/open_pose_data.csv', 'w') as csvfile: fieldnames = [] for i in range(30): fieldnames.append(str(i)) writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for trick in data: writer.writerow({'0': trick[0][0], '1':trick[0][1], '2':trick[1][0], '3':trick[1][1], '4':trick[2][0], '5':trick[2][1], '6':trick[3][0], '7':trick[3][1], '8':trick[4][0], '9':trick[4][1], '10':trick[5][0], '11':trick[5][1], '12':trick[6][0], '13':trick[6][1], '14':trick[7][0], '15':trick[7][1], '16':trick[8][0], '17':trick[8][1], '18':trick[9][0], '19':trick[9][1], '20':trick[10][0], '21':trick[10][1], '22':trick[11][0], '23':trick[11][1], '24':trick[12][0], '25':trick[12][1], '26':trick[13][0], '27':trick[13][1], '28':trick[14][0], '29':trick[14][1]})