import cv2 import numpy as np import requests import os from mtcnn.mtcnn import MTCNN # import mailsend import combine from skimage import color from skimage.feature import graycomatrix, graycoprops wrinkles = "" tone = "" contrastt = "" diss = "" pig = "" stored_text = "" # response_text = "" def image_save(stored_text2,response_text2): stored_text = stored_text2 # print("lets see !!!") # print("this is st",stored_text2) # print(" ") # print("this is url",response_text2) # return stored_text return stored_text image_url="" save_path="" def save_image_from_url(image_url, save_path): try: # Send a GET request to the image URL response = requests.get(image_url) response.raise_for_status() # Check for any errors in the request # Get the file name from the URL file_name = os.path.basename(image_url) # Combine the file name with the save_path to create the complete file path file_path = os.path.join(save_path, "arducam_pic.jpg") # Save the image to the specified location with open(file_path, 'wb') as file: file.write(response.content) # print("Image saved successfully!") return True except Exception as e: print(f"Error occurred: {e}") return False img = cv2.imread("C:/Users/Aditi/OneDrive/Desktop/SkinTellect/arducam_pic.jpg") def detect(): # print("let's start...............") # Load the pre-trained MTCNN face detector detector = MTCNN() # Read the input image # img = cv2.imread("C:/Users/Aditi/OneDrive/Desktop/SkinTellect/arducam_pic.jpg") # Detect faces in the image using MTCNN faces = detector.detect_faces(img) # Check if any faces were detected if len(faces) > 0: for face in faces: x, y, w, h = face['box'] # Crop the face region face_region = img[y:y+h, x:x+w] # Convert the face region to grayscale gray_face = cv2.cvtColor(face_region, cv2.COLOR_BGR2GRAY) # Apply Canny edge detection on the grayscale face region edges = cv2.Canny(gray_face, 130, 1000) # Count the number of edges in the cropped face region num_edges = np.count_nonzero(edges) if num_edges > 1000: # print("Wrinkle Found on this face!") found = "Wrinkles found" return "wrinkles found" # mailsend.send_mailToCustomer(found,finalmailid) # combine.var(found,finalmailid) else: # print("No Wrinkle Found on your face!") found = "No Wrinkle found" return "wrinkles not found" # mailsend.send_mailToCustomer(found,finalmailid) # combine.var(found,finalmailid) else: print("No face detected in the image.") # skintone def skin_tone_analysis(): # Load the image using OpenCV # img = cv2.imread(image_path) # Convert the image from BGR to RGB color space img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert the RGB image to LAB color space img_lab = color.rgb2lab(img_rgb) # Extract the L*, a*, and b* channels l_channel, a_channel, b_channel = cv2.split(img_lab) # Calculate the mean a* and b* values mean_a = np.mean(a_channel) mean_b = np.mean(b_channel) # Calculate the skin tone index (STI) sti = np.sqrt(mean_a ** 2 + mean_b ** 2) # Determine the skin tone category based on the STI value if sti < 20: skin_tone_category = "Dark skin" elif sti < 40: skin_tone_category = "Medium skin" else: skin_tone_category = "Light skin" # combine.var(found,final) return skin_tone_category tone = skin_tone_category print("Skin tone : ",skin_tone_category) # Replace 'path_to_image.jpg' with the actual path to your image # image_path = 'C:/Users/Aditi/OneDrive/Desktop/SkinTellect/arducam_pic.jpg' # result = skin_tone_analysis(image_path) # print("Skin Tone Category:", result) # skintexture def skin_texture_analysis(): # Load the image using OpenCV # img = cv2.imread(image_path) # Convert the image to grayscale gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Calculate the GLCM with specific properties (contrast and dissimilarity) glcm = graycomatrix(gray_img, [1], [0], 256, symmetric=True, normed=True) contrast = graycoprops(glcm, 'contrast')[0, 0] dissimilarity = graycoprops(glcm, 'dissimilarity')[0, 0] # print("contrast : ",contrast) # print("dissimilarity : ",dissimilarity) return contrast contrastt = contrast diss = dissimilarity # Display the results # print("Contrast:", contrast) # print("Dissimilarity:", dissimilarity) # final_skintextanalysis = contrast.append(dissimilarity) # Replace 'path_to_image.jpg' with the actual path to your image # image_path = 'C:/Users/Aditi/OneDrive/Desktop/SkinTellect/arducam_pic.jpg' # skin_texture_analysis(image_path) # pigmentation def pigmentation_analysis(): # Load the image using OpenCV # img = cv2.imread(image_path) # Check if the image is loaded successfully if img is None: print("Error: Unable to load the image.") return None # Convert the image from BGR to HSV color space img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # Define the lower and upper bounds of pigmented color in HSV lower_bound = np.array([0, 50, 50]) # Lower bound for hue (red) upper_bound = np.array([20, 255, 255]) # Upper bound for hue (yellow) # Create a mask to isolate pigmented regions mask = cv2.inRange(img_hsv, lower_bound, upper_bound) # Count the number of pigmented pixels pigmented_pixels = np.sum(mask == 255) # Calculate the total number of pixels in the image total_pixels = mask.shape[0] * mask.shape[1] # Calculate the percentage of pigmented pixels in the image pigmentation_percentage = (pigmented_pixels / total_pixels) * 100 pig = pigmentation_percentage # print("ye zroor hoga : ",pig) # print("Pigmentation : ",pigmentation_percentage) return pigmentation_percentage # Replace 'path_to_image.jpg' with the actual path to your image # image_path = 'C:/Users/Aditi/OneDrive/Desktop/SkinTellect/arducam_pic.jpg' # result = pigmentation_analysis(image_path) # if result is not None: # print("Percentage of Pigmentation:", result) def letdo(finalmailid,ph_no,wrinkles,tone,contrastt,pig,age,gender): pigm="" cont="" if(contrastt>50): cont='HIGH' elif(20 < contrastt <= 50): cont='MEDIUM' else: cont='LOW' if(pig>50): pigm='HIGH' elif(20 < pig <= 50): pigm='MEDIUM' else: pigm='LOW' combine.var(finalmailid,ph_no, wrinkles,tone,cont,pigm,age,gender) # combine.index()