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| import cv2 | |
| import numpy as np | |
| import os | |
| import pickle | |
| from deepface import DeepFace | |
| import gradio as gr | |
| from datetime import datetime | |
| import fast_colorthief | |
| import webcolors | |
| from PIL import Image | |
| thres = 0.45 | |
| classNames= [] | |
| classFile = 'coco.names' | |
| with open(classFile,'rt') as f: | |
| #classNames = f.read().rstrip('n').split('n') | |
| classNames = f.readlines() | |
| # remove new line characters | |
| classNames = [x.strip() for x in classNames] | |
| print(classNames) | |
| configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt' | |
| weightsPath = 'frozen_inference_graph.pb' | |
| net = cv2.dnn_DetectionModel(weightsPath,configPath) | |
| net.setInputSize(320,320) | |
| net.setInputScale(1.0/ 127.5) | |
| net.setInputMean((127.5, 127.5, 127.5)) | |
| net.setInputSwapRB(True) | |
| def main(image): | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| rgb=cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| names=[] | |
| #object | |
| try: | |
| classIds, confs, bbox = net.detect(image,confThreshold=thres) | |
| except Exception as err: | |
| print(err) | |
| print(classIds,bbox) | |
| try: | |
| if len(classIds) != 0: | |
| for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox): | |
| if names.count(classNames[classId-1]) == 0: | |
| names.append(classNames[classId-1]) | |
| except Exception as err: | |
| print(err) | |
| #emotion | |
| try: | |
| face_analysis_2=DeepFace.analyze(image, actions = ['emotion'], enforce_detection=False) | |
| names.append(face_analysis_2["dominant_emotion"]) | |
| except: | |
| print("No face") | |
| names.append("No Face") | |
| # #Colour | |
| colourimage = Image.fromarray(image) | |
| colourimage = colourimage.convert('RGBA') | |
| colourimage = np.array(colourimage).astype(np.uint8) | |
| palette=fast_colorthief.get_palette(colourimage) | |
| for i in range(len(palette)): | |
| diff={} | |
| for color_hex, color_name in webcolors.CSS3_HEX_TO_NAMES.items(): | |
| r, g, b = webcolors.hex_to_rgb(color_hex) | |
| diff[sum([(r - palette[i][0])**2, | |
| (g - palette[i][1])**2, | |
| (b - palette[i][2])**2])]= color_name | |
| if names.count(diff[min(diff.keys())])==0: | |
| names.append(diff[min(diff.keys())]) | |
| return ' '.join(names) | |
| interface = gr.Interface(fn=main, | |
| inputs=["image"], | |
| outputs=[gr.inputs.Textbox(label='Names of person in image')], | |
| title='Color Object Emotion ', | |
| description='This Space:\n \n2) Detect Emotion \n3) Detect Colors.\n4) Object Detection \n<br>This space is for personal use. For the time being, it only recognizes few persons. To use it, simply duplicate the space and replace the images in the image folder.') | |
| interface.launch(inline=False) |