Day #299 - OpenVino Python Code for Faces - Pedestrain - Attributes

Hope this helps for other developers trying out executing intel openvino models in python

	#https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List
	import cv2
	import numpy as np
	import sys
	import time
	import os
	FACE_DATA_DIR = '/home/upsquared/Documents/projects/Code/faces'
	DATA_DIR = '/home/upsquared/Documents/projects/Code/samples'
	RESULTS_DIR = '/home/upsquared/Documents/projects/Code/results'
	ATTRIBUTES_RESULTS_DIR = '/home/upsquared/Documents/projects/Code/attribute_results'
	def Detect_Faces():
	attr_bin = '/opt/intel/computer_vision_sdk/deployment_tools/intel_models/face-detection-retail-0004/FP32/face-detection-retail-0004.bin'
	attr_xml = '/opt/intel/computer_vision_sdk/deployment_tools/intel_models/face-detection-retail-0004/FP32/face-detection-retail-0004.xml'
	ped_net = cv2.dnn.readNet(attr_bin, attr_xml)
	ped_net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
	print("Models loaded")
	files = os.listdir(FACE_DATA_DIR)
	#get all files in directory
	#loop through for all files
	i = 0
	for file in files:
	filepath = FACE_DATA_DIR + '//'+ file
	print(filepath)
	rawframe = cv2.imread(filepath)
	frame = cv2.resize(rawframe, (300,300))
	#https://docs.openvinotoolkit.org/latest/_models_intel_person_detection_retail_0013_description_person_detection_retail_0013.html
	try:
	blob = cv2.dnn.blobFromImage(frame,size=(300,300),ddepth=cv2.CV_8U)
	ped_net.setInput(blob)
	out = ped_net.forward()
	predictions = []
	for detection in out.reshape(-1,7):
	image_id,label,conf,x_min,y_min,x_max,y_max = detection
	print(conf)
	#print(label)
	if conf > 0.5:
	predictions.append(detection)
	#print(predictions)
	print(len(predictions))
	for detection in predictions:
	confidence = float(detection[2])
	xmin = int(detection[3]*frame.shape[1])
	ymin = int(detection[4]*frame.shape[0])
	xmax = int(detection[5]*frame.shape[1])
	ymax = int(detection[6]*frame.shape[0])
	print(xmin,ymin,xmax,ymax)
	cv2.rectangle(frame,(xmin,ymin),(xmax,ymax),color=(0,255,0))
	#Crop and Save
	cv2.imshow("Result",frame)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
	result_filepath = RESULTS_DIR + '//'+ str(i) + '.jpg'
	#write the output in directory
	cv2.imwrite(result_filepath,frame)
	i = i+1
	except:
	print('Error')
	print(frame)
	pass
	def Detect_Pedestrians_Adas():
	attr_bin = '/opt/intel/computer_vision_sdk/deployment_tools/intel_models/pedestrian-detection-adas-0002/FP32/pedestrian-detection-adas-0002.bin'
	attr_xml = '/opt/intel/computer_vision_sdk/deployment_tools/intel_models/pedestrian-detection-adas-0002/FP32/pedestrian-detection-adas-0002.xml'
	ped_net = cv2.dnn.readNet(attr_bin, attr_xml)
	ped_net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
	print("Models loaded")
	files = os.listdir(DATA_DIR)
	#get all files in directory
	#loop through for all files
	i = 0
	for file in files:
	filepath = DATA_DIR + '//'+ file
	print(filepath)
	frame = cv2.imread(filepath)
	#https://docs.openvinotoolkit.org/latest/_models_intel_pedestrian_detection_adas_0002_description_pedestrian_detection_adas_0002.html
	blob = cv2.dnn.blobFromImage(frame,size=(672,384),ddepth=cv2.CV_8U)
	ped_net.setInput(blob)
	out = ped_net.forward()
	predictions = []
	for detection in out.reshape(-1,7):
	image_id,label,conf,x_min,y_min,x_max,y_max = detection
	if conf > 0.5:
	predictions.append(detection)
	#print(predictions)
	print(len(predictions))
	for detection in predictions:
	confidence = float(detection[2])
	xmin = int(detection[3]*frame.shape[1])
	ymin = int(detection[4]*frame.shape[0])
	xmax = int(detection[5]*frame.shape[1])
	ymax = int(detection[6]*frame.shape[0])
	print(xmin,ymin,xmax,ymax)
	pedestrian = frame[ymin:ymax,xmin:xmax]
	result_filepath = RESULTS_DIR + '//'+ str(i) + '.jpg'
	#write the output in directory
	cv2.imwrite(result_filepath,pedestrian)
	cv2.rectangle(frame,(xmin,ymin),(xmax,ymax),color=(0,255,0))
	i = i+1
	cv2.imshow("Result",frame)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
	def Detect_Pedestrians_Retail():
	attr_bin = '/opt/intel/computer_vision_sdk/deployment_tools/intel_models/person-detection-retail-0013/FP32/person-detection-retail-0013.bin'
	attr_xml = '/opt/intel/computer_vision_sdk/deployment_tools/intel_models/person-detection-retail-0013/FP32/person-detection-retail-0013.xml'
	ped_net = cv2.dnn.readNet(attr_bin, attr_xml)
	ped_net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
	print("Models loaded")
	files = os.listdir(DATA_DIR)
	#get all files in directory
	#loop through for all files
	i = 0
	for file in files:
	filepath = DATA_DIR + '//'+ file
	print(filepath)
	frame = cv2.imread(filepath)
	#https://docs.openvinotoolkit.org/latest/_models_intel_pedestrian_detection_adas_0002_description_pedestrian_detection_adas_0002.html
	#blob = cv2.dnn.blobFromImage(frame,size=(384,672),ddepth=cv2.CV_8U)
	#https://docs.openvinotoolkit.org/latest/_models_intel_person_detection_retail_0013_description_person_detection_retail_0013.html
	blob = cv2.dnn.blobFromImage(frame,size=(544,320),ddepth=cv2.CV_8U)
	ped_net.setInput(blob)
	out = ped_net.forward()
	predictions = []
	for detection in out.reshape(-1,7):
	image_id,label,conf,x_min,y_min,x_max,y_max = detection
	#print(conf)
	#print(label)
	if conf > 0.5:
	predictions.append(detection)
	#print(predictions)
	print(len(predictions))
	for detection in predictions:
	confidence = float(detection[2])
	xmin = int(detection[3]*frame.shape[1])
	ymin = int(detection[4]*frame.shape[0])
	xmax = int(detection[5]*frame.shape[1])
	ymax = int(detection[6]*frame.shape[0])
	print(xmin,ymin,xmax,ymax)
	pedestrian = frame[ymin:ymax,xmin:xmax]
	result_filepath = RESULTS_DIR + '//'+ str(i) + '.jpg'
	#write the output in directory
	cv2.imwrite(result_filepath,pedestrian)
	cv2.rectangle(frame,(xmin,ymin),(xmax,ymax),color=(0,255,0))
	i = i+1
	#Crop and Save
	cv2.imshow("Result",frame)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
	def Detect_Attributes():
	attr_bin = '/opt/intel/computer_vision_sdk/deployment_tools/intel_models/person-attributes-recognition-crossroad-0200/FP32/person-attributes-recognition-crossroad-0200.bin'
	attr_xml = '/opt/intel/computer_vision_sdk/deployment_tools/intel_models/person-attributes-recognition-crossroad-0200/FP32/person-attributes-recognition-crossroad-0200.xml'
	ped_net = cv2.dnn.readNet(attr_bin, attr_xml)
	ped_net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
	print("Models loaded")
	files = os.listdir(RESULTS_DIR)
	#get all files in directory
	#loop through for all files
	for file in files:
	try:
	filepath = RESULTS_DIR + '//'+ file
	print(filepath)
	frame = cv2.imread(filepath,1)
	#H = 160
	#W = 80
	h,w = frame.shape[:2]
	if (w>=80 and h>=160):
	#https://docs.openvinotoolkit.org/latest/_models_intel_person_attributes_recognition_crossroad_0230_description_person_attributes_recognition_crossroad_0230.html
	blob = cv2.dnn.blobFromImage(frame,size=(80,160),ddepth=cv2.CV_8U)
	ped_net.setInput(blob)
	print("part 1")
	out = ped_net.forward("453")
	#print(out)
	predictions = []
	for detection in out.reshape(-1,8):
	is_male, has_bag, has_backpack, has_hat, has_longsleeves, has_longpants, has_longhair, has_coat_jacket = detection
	predictions.append(detection)
	print(predictions)
	#print(len(predictions))
	for detection in predictions:
	if(detection[0] > 0.5):
	print('MALE')
	else:
	print('Female')
	if(detection[1] > 0.5):
	print('Has BAG')
	if(detection[2] > 0.5):
	print('has_backpack')
	if(detection[3] > 0.5):
	print('has_hat')
	if(detection[4] > 0.5):
	print('has_longsleeves')
	if(detection[5] > 0.5):
	print('has_longpants')
	if(detection[6] > 0.5):
	print('has_longhair')
	if(detection[7] > 0.5):
	print('has_coat_jacket')
	print("part 2")
	out1 = ped_net.forward("456")
	print(out1)
	predictions = []
	for detection in out1.reshape(-1,2):
	point_with_top_colorx, point_with_top_colory = detection
	predictions.append(detection)
	for detection in predictions:
	print(detection[0])
	print(detection[1])
	print("part 3")
	out2 = ped_net.forward("459")
	print(out2)
	predictions = []
	for detection in out2.reshape(-1,2):
	point_with_bottom_colorx, point_with_bottom_colory = detection
	predictions.append(detection)
	for detection in predictions:
	print(detection[0])
	print(detection[1])
	except:
	pass
	#Detect_Faces()
	#Detect_Pedestrians_Adas()
	#Detect_Pedestrians_Retail()
	Detect_Attributes()

view raw OpenVino_Python_samples.py hosted with ❤ by GitHub

Happy Learning!!!